Database Marketing Intensive

2013 DMA Database Marketing Post Intensive

Program Agenda

Post Intensive Session on Database Marketing

Developing a 21st Century Database—The Tools, Tactics and Tests to Meet Your Business Needs

Within the past few years, massive changes in data, technology and the web have significantly impacted

the planning, research, marketing and sales processes. Business needs have shifted dramatically with a

focus on faster analysis, broader multi-channel integration and dynamic database information systems.

This nine-hour seminar is designed for the database marketer who is looking to enhance or overhaul

database business operations at their company. The instructor lineup consists of leading industry

professionals who regularly evaluate cutting-edge technologies and best practices in database marketing.

Over the course of two days, attendees will be exposed to current and future systems, trends,

recommendations and pitfalls that lie ahead in today’s and tomorrow’s database marketing landscape.

Day 1 Wednesday, October 16 1:00 – 4:30

1:00 – 1:15 Program Introduction/Class Discussion—PEGG NADLER

Part 1-- 1:15—2:15

Re-evaluating Your Marketing Database System: A How To

BERNICE GROSSMAN, DMRS Group

A “check list” of the most important items to review when re-evaluating your marketing database, your

vendor, and the design and attending functionality of your current solution tool. Attendees will be

provided with a proven method of what to look for and how to know what is and is not working. Before

you conclude your marketing database is broken, learn how to answer the key questions that determine

the state of your database.

Part 2—2:15 – 3:15

A Primer on Database Systems—Deciphering Differences and Determining Directions

MARCUS TEWKSBURY, VP Client Partner, Experian

There is a myriad of database technologies on the market today—and this session is designed to equip

attendees with the key benchmarks to assess and select marketing systems that meet their company’s

existing and anticipated needs. Included in this overview will be an examination of current marketing

automation application software, including traditional vendors, B2B, CRM systems and Web content

systems.

Part 3-- 3:30 – 4:30

Deadly Sins and the Ten Commandments: How to Achieve Best-Practices Database Content and Key

Metrics Reporting

JIM WHEATON, Wheaton Group

A database is only as good as its content, and bad content always costs you money. There is nothing

glamorous about creating and maintaining best-practices content. Data audits and other forms of quality

assurance are hard work. The same is true about carefully reflecting the nuances of your business and

data when creating dashboards and reports. This session will tell you why all of this, although often

overlooked, is so important for database success.

Day 2 Thursday, October 17 8:30 – 2:45

Part 4— 8:30 – 9:45

Leveraging Your Database: Reporting, Templates & Strategic Applications

AL BESSIN, Bessin Consulting

Identifying the customer, their wants and needs, and what drives their behavior forms the basis for

successful marketing in today’s business environment. Learn how to create a customer balance sheet;

identify where mistakes are being made; and use findings to drive business transformation. Understand

what media is working by looking at different ways in which results are being reported for online and

offline marketing campaigns. Emphasis will be on determining the most practical and actionable methods

to use including marketing performance, lifetime value and business strategy.

Part 5—9:45 – 10:45

Modeling and Analytics

STEVE KIM, VP, Quantitative Marketing Group, Merkle

This session will highlight the critical dimensions for a successful use of database information when

building and deploying models and analytics in your business. Insights, targeting and measurement are

all critical components to fully understand the most effective channels in marketing programs. Big Data

presents major challenges and opportunities in data collection and its manipulation in the creation of

modeling and analysis. We will explore the latest in analytical hardware and software and discuss how to

utilize your data to detail ROI in building the business case and calculating its impact.

Part 6—11:00 – 12:00

Navigating the Data Maze

JOANNE BRANSCUM, Director Management Information, Acxiom Global Services

DOUG CHRISTIANSON, Senior Principal, Acxiom Global Services

Database marketers are now faced with massive amounts of data, mounting privacy issues and growing

regulations on the use, collection and dissemination of data. This session will look at both traditional and

new sources of data used to shape database analysis programs. We will address the latest trends in Big

Data for the B-C and B-B worlds. Data collection and data connection as well as best practices for

determining and protecting your customer data needs will be discussed.

Break & Boxed Lunch Pickup 12:00 – 12:15 (Boxed Lunch—working lunch during Part 7)

Part 7—12:15 – 1:30

Integrating Digital Media Data with Your Marketing Database

RANDY HLAVAC, Lecturer Professor – Northwestern University, Medill IMC (Integrated Marketing

Communications)

Social media, mobile, web communities and other electronic media hold the potential for providing new,

high impact data to improve the ability of our marketing database systems to drive highly targeted CRM

and electronic programs. But challenges exist using this data. What data is important (and legal) to add to

your database? How do we monitor and assess data quality and impact? How do we entice visitors to

provide data? We will examine how to integrate your social, mobile, web, and CRM marketing efforts into a single Social CRM system.

Part 8—1:45 – 2:45

Marketing ROI: How to Ensure Political, Technical, and Business Success for a Database Project

PEGG NADLER, Pegg Nadler Associates

This session will set a realistic foundation for positioning your database for success within your company.

We will look at war stories and success stories and provide guidance and benchmarks for conducting a

business needs/expectations survey and the justification for the continued investment and deployment in

your marketing database division.

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Re-evaluating Your Marketing Database System: A How To

Database Marketing Post Intensive Part 1

Bernice GrossmanFounder, DMRS Group

[email protected]

• DMRS has been working with client companies to maximize their data marketing efforts since 1983. We are an independent consultancy, we own no data, no software, nor any processing services or facilities.

• We manage data audits/assessments and operational needs assessments:Choosing the right vendors Data / ETL, MSP / ESP, MDB / CRM, MA / SFA Implementation End-user marketing applications for off-line and on-line

• Our client list spans a broad spectrum of Domestic and International businesses including Avis, Epson, Microsoft, Pfizer, United Airlines, Nestle, Simon, United States Gypsum, and United Airlines

Who is DMRS ?

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This Session • This session will provide a check list of the most important items to

review when re-evaluating your marketing database, your vendor, and the design and attending functionality of your current solution tool.

• Attendees will be provided with a proven method of what to look for and how to know what is and is not working. Before you conclude your marketing database is broken, come to this session and learn the key questions to ask that will help determine the state of your database.

• Key takeaways:– What do you need now that you didn't need when your marketing

database was built?– What about your data?– How should you review database integration with email and

social media - what exists now that didn't exist at the time of the build?

First, A Definitionjust so that we’re all on the same page

An MDB (Marketing Database) is a single repository for all data identified as relevant to meet the goals of marketing that are defined as actionable and accessible for:

• Capturing data from all channels• Consistent data hygiene and de-duplication rules• Allows for segmentation and query• Integrates Direct, E-Mail, Social Media (transactional, web site, call center,

behavioral, attitudinal, events – more)• Performs complete Campaign Management • Measures media performance• Manages multi-channel marketing• Performs modeling and predicting behavior analyses• It is read only. It is NOT a contact management system.

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IS YOUR MDB “BROKEN”?• What is “broken”? We’re going to look at a few examples in a moment.• Length of contract • When does your contract expire?• (If inside) Is it time to take it off-site?• Are you all integrated?• Does your MDB work?

• What are the metrics you use to decide this?– Do the MDB counts match the transaction counts?– Does the geography match

• Who decides that it does or does not work?• Does anyone want to use it?

• Who? Why?• Who does not?

• Is marketing grumbling• Is IT smirking

Some “Broken” Examples• Pharmaceutical Company

– Kept each drug on a separate MDB – became too expensive – realized they were paying for certain processes three times but only needed to “buy” it once

• Membership Organization– The users were in silos – just like their data– Change Management was very difficult– Never contemplated the problems of moving data back and forth

(especially from their SFA to the MDB)

• Large Retail Shopping Installation– Never thought through how to use the response management

functionality

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Is Everything Still the Same at the MSP?

• Corporate mission statement and customer service philosophy

• Total number of staff • Key executives• Ownership information and

organization chart• Quality control procedures

from data receipt to MDB update

• # of customer support staff • # of technical support staff• Customer mix

• System software information• Percent of budget applied to

R&D • Willingness to provide details

pending litigation• MDB staff attrition over the last

year• Company privacy policy• Primary industries that are

served • Number/type of user group

meetings held each year

Are Their Data Center Capabilities Still the Same?

• Available data center locations • Back up procedures• Real-time redundancy (servers, HVAC, etc.)• Disaster recovery and business continuity procedures• Contingency for downtime and preventive maintenance• Physical and data security measures • Connectivity options• Service levels for problem reporting and resolution

– Do these meet your needs today?• Ability to provide support 24 x 7 x 365

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What About …………• Has their client list changed? How?• What have they done to enhance their look-up tables for

company name, title, first name • Can their solution now support both your marketing and

contact management/SFA needs? How?• Have they integrated with an ESP?

– Who?– How are they integrated? – Is it really one platform or is it two that are “made” to

look like one?• How are they integrating Social Media?• What is available to you in Real Time? WHY do you

need real time?

THE CRITICAL QUESTIONS• When was the last time your BRD was updated? • When was the last time you compared your BRD to what

you are receiving? This should be done at least 1x/yr• When was the last time you looked at your ERD?• Has the staff that manages your MDB changed? • What do you need now that you didn't need when your

MDB was built? How old is your MDB?• Have you reviewed the MDB integration processes with

email and social media issues that didn't exist at the time of the build?

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Do you still have the same “25 Questions”?

WHAT 25 Questions?If you had an ideal standard and fresh marketing database, what questions would you want answered from the data?

But, there are 2 conditions:• Question must be quantitative!• Question cannot use a subjective word (e.g. big

or better)!For example: How many customers who purchase SKU #123 in Mississippi also purchased SKU #456

Original Business Goals and Functional Requirements

Business goals• Become customer-centric by

developing a complete view of the customer with all pertinent data

• Increase effectiveness and efficiency of acquisition and retention marketing with better customer targeting and campaign management

• Improve overall ROI by marketing to most valuable customers

• Target individual customers with specific messages designed to best meet their needs

• Understand customer behavior for each product within channels and across the brands

Functional requirements• Provide access for query and analysis by

both marketing and sales• Integrate the mail and email query and

campaign management functions.• Provide accurate information on new

customers, cost to acquire customers, number of inactive customers, migration of customers between value segments and the cost of migration

• Use 3rd party B-to-B data to establish corporate hierarchy links of ownership and firmographic profile info

• Enhance customer data through the use of 3rd party for demographics, lifestyles, behavioral, attitudinal

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Has Your Team Changed?• Team Champion – Owns the Vision and Articulates it to the Team• Marketing (all channels)

– Direct mail– Email– Telemarketing– Social Media – Space– Acquisition– Retention– Product

• Sales • IT• Finance• Legal

HAS YOUR ENVIRONMENT CHANGED?

Data locations: – Oracle data warehouse– Mainframe flat files– SQL Server– SalesForce.com

2,000,000 eligible records on file. Approx. 50 Gb of data representing the last 3 years. Growth over the next 3 years is expected at a rate of 25% per year.

Files includedbusiness-to-business consumer US and International data customers/prospectfull postal address, just email, some “handles”

Estimated # users = 20.

THIS IS WHAT IS WAS:

WHAT ABOUT NOW?

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What about your data?

• Is it the same or has it changed in scope• Have you added new products, services, bought

other companies, etc.,• Have you changed the channels you use for

acquisition and/or retention or the amount you use of a channel?

• Have you changed data vendors?

Data Sources – Marketing StrategiesHave You Added New Ones or Made Significant

Changes?• DATA

• Transactional Files• Email • Web Site Data• Operations• Complaints• Reviews• Tech Support• Social• Other

• MARKETING• New Channels• Different Schedules• Re-Organized• New Management• Decided to Outsource• Added / Deleted Partners• Bought / Sold a Company• Other

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Have You Recently Reviewed..

Your data enhancement sources and methodologiesHave you created a “best record” and are the requirements

still the same Have you reviewed data standardization and sanitization

routinesWhat about records with only:

Postal Addresses.E-Mail Addresses.Social Media “handles”

What about those record missing “key” data elements

What About……………• Response time

– Do you need increased speed?– When was the last time you had the server sized?

• Query capabilities• Multiple users

– Have you added or deleted users?• Simultaneous usage

– Has this stayed the same?• Multiple locations• Data feeds and updates

– Have you added new ones?

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Remember when you ……

• Created validation rules for all of the data feeds• Developed Appropriate Audit Reports for

Data feedsDatabase refreshesStandard reports

• Developed Reject procedures – and decided what to do what to do when key checkpoints failed

• Do you still follow those rules??

Created Sanity Checks….• Standard reports that ran after database

refreshes and database feeds to verify key metrics

• Threshold reportsIf “x” metric exceeds an appropriate number does a red flag goes up?Who is advised?Are the reports still automatically distributed to the appropriate people?

are those people still at your company?are the reports read?

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THE 8 MUST HAVE’S –Do You Have More / or Are They

Just Different?QueryCalculatingReportingDirect and E Mail Campaign ManagementSocial Media Integration Data ExtractData ImportData Mining, Analysis, Tracking & Modeling

Do Any of These Still Exists?

• Disparate platforms ---- not everything is connected

• No common repository to store everything

• Creating selections is just too complicated – almost no one knows SQL except IT

• Data is still not sanitized, standardized, unduplicated nor aggregated the same way across all of the sources

• Still no written set of up-to-date business rules

• Sill no written BRD?

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Nice to Have or Now Must Have’s

• Real time access• Data from files not integrated (by name and

address) with the MDB – integration is done by an ID

• Social Media “handles” are matched to email addresses

• Bi-synchronous feed with SFA

What are your users doing?

• What are the work-arounds?• Might these be the reason your MDB is “broken”• How many are there?• How can you get these to be integrated into the

on-going functionality of the processes your MSP provides?

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Some Final Thoughts• Politics will always rear it’s ugly head – nothing

changes

• This was a high emotional stressful project and it still it

• There was high, often undirected, energy and its still there

• Big questions like, “who really owns the data”, MUST be answered - this is like a moving target!

• Although there were multiple levels of expectation for the Master Marketing Database (MDB), have you finally all agreed? Does this need to be reviewed?

LIST OF PLAYERS IN THIS SPACE IS ENDLESS

Customer Relationship Management (CRM)

Extract, Transform, Load (ETL)

Marketing Service Provider (MSP)

Marketing Automation / Lead Management

OUR DATA IS A MESS!HOW TO CLEAN UP YOUR MARKET ING DATABASE

BY BERNICE GROSSMAN & RUTH P. STEVENS OCTOBER 2005

BUSINESS-TO-BUSINESS DATABASE MARKET ING

E X ECUTI V E SUMM A RY

Business-to-business companies are often frustrated by inaccurate customer information. But there are

steps you can take to keep your data clean and up to date. The most essential action steps are manual,

using processes to enter data correctly in the first place, and to conduct outbound communications to

verify its ongoing accuracy. These steps can then be supplemented by the automated method, which

usually means sending your data to an outside service provider for regular clean-up. The authors sent a

sample of 10,000 business records to four leading vendors and share the results.

OUR DATA IS A ME SS! HOW TO CLE AN UP YOUR MARKETING DATABA SE

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“ W H AT DO YOU THINK A BOUT YOUR CUS TOMER DATA? ”

You can ask this question of anyone involved in B-to-B sales and marketing, and the answer you receive will just about always be the same. “Our data is a mess.”

Of course, it’s likely that the answer will be couched in more forceful terms than “a mess.” But the implication is clear. People in business- to-business marketing are aware that they need to do a better job of collecting and maintaining accurate and up-to-date information about their customers and prospects.

There are steps you can take to keep your data clean and fresh. This paper reviews the data hygiene methods available to business marketers today. It will also introduce the results of a research study among data hygiene vendors that will help you understand what you can expect a third-party service provider to do to keep your customer information clean.

W H AT IS A “ME S S” ? THE S TATE OF YOUR BUSINE S S - TO -BUSINE S S DATA TODAY

Part of the problem faced by business marketers is definitional. While everyone says “My data is a mess,” they may mean different things by it. Marketers, for example, may be talking about situations when their direct mail arrives but doesn’t get delivered beyond the mail room. For sales people, it’s when they pick up the phone and discover the customer’s direct phone number has changed. But there’s more. The business may have moved its offices. Or the customer’s title may have changed. Or the data fields may be mixed up, for example, an old purchase order number that’s parked in the customer name field. Or the state of Nebraska may be abbreviated NB, while the post office only accepts NE. It goes on and on.

Each of these problems is common in business marketing databases, and creates enormous waste — of marketing communications investment, and of business opportunity — not to mention frustration

at all levels. So what can you do about it?

The solutions lies in data hygiene, defined as follows: Correcting inaccurate fields and standardizing formats and data elements. There are two general approaches to data hygiene: manual options and automated clean up. Let us look at what each of these can — and cannot — do for you.

M A NUA L PROCE S SE S

There are two key manual methods involved in data hygiene:

1. Enter clean data in the first place 2. Institute on-going updating processes

The most important is the first: If the data is entered or received incorrectly at the start, you have not only wasted a business opportunity, you have created needless extra expense to go back and correct the information. Bad data is worse than no data at all.

Smart companies are using the following key methods for correct data input:

• Create and maintain a set of processes known as Input Editing Standards (IES). These are the rules for data elements that must be followed at the point of entry. For example, you might standardize all references to the International Business Machines corporation as IBM. You would require that 2-digit state abbreviations conform with USPS standards. And you would require that all titles be spelled out fully. Most companies create an input standards document when they first create a computerized database of customer information. But over the years that document may get lost, out-dated or filed somewhere collecting dust. Your first step is to find that document, review it, refresh it, and put it into use.

• Train data entry personnel on the IES rules, and repeat the training at least

quarterly. It’s not just for new employees, but also needed as an ongoing refresher. A corollary point: Don’t expect to pay your key-entry personnel peanuts and get great results. They need substantial training and incentives to do a good job maintaining your data asset.

• Use address-checking software at point of entry, to ensure deliverability.

Starting out with clean data is only the beginning. Business data tends to degrade at the rate of 3-6% per month, so you must invest in ongoing maintenance. Here are the best manual methods for data cleanliness:

• Train and motivate employees who have direct customer contact to request updates at each encounter. This includes call center personnel, customer service, sales people and distributors. It may be the job of marketing to keep the database clean, but data is a valuable corporate asset, and everyone has a stake in its quality.

• Segment your file, and conduct outbound confirmation contacts for the highest value accounts. This can be by mail, email or telephone.

• When using first-class mail, request the address correction service provided by the USPS. Put in place a process to update the addresses from the “nixies,” meaning the undeliverable mail that is returned to you.

• Invite your customers to help you maintain their information correctly. Make the contact information available on a password-protected website, and ask your customers to key-enter changes as they occur. Offering them a good reason to do so, or perhaps apremium or incentive, will result in higher levels of customer compliance.

HOW TO CLE AN UP YOUR MARKET ING DATABA SE

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THE AUTOM ATED ME THOD

Once you have manual methods underway, send your data out to a service provider for regular clean up. We recommend data cleansing at a third party at least twice a year. Large providers of business data are skilled at matching your file to their databases of standardized, updated records, and giving you back the good information. On a per-record basis, automated clean-up is inexpensive, and should be combined with an ongoing manual program of data hygiene.

There is quite a bit of misapprehension about the nature of automated data hygiene. Because it involves a matching process against a larger national database, some people confuse it with other data processes. So, before we go into more detail about what it can do, let us be clear about what we don’t mean by automated data clean up for business marketers.

Sending your names out for clean up is not to be confused with:

• De-duplication, which means identifying records that qualify as duplicates.

• Data append, which means adding extra fields like an industry code, years in business, a credit score, or company size.

At the same time, it’s important to realize that automated data hygiene cannot clean up everything on your database. For example, changes to a person’s title or direct phone number are unlikely to be reported with any speed into a national database. So much of the time, the vendor will have no fresher title or phone data than you have yourself. And there’s another matter to consider: Whose data is correct? If the name you have on your file for a company or a person is different from the name on the national database, how will you decide which one to accept? Most companies give preference to the data that was most recently collected or confirmed with customers.

The national databases maintained by various vendors have only one ultimate standard against which address accuracy can be measured, namely, the USPS. In fact, the only “true” addresses, street, state and ZIP code, are those recognized by the post office. So you can count on the outside vendors to clean up addresses to the point where they will support mail delivery. But you won’t have the same level of confidence in the potential clean-up of telephone numbers, fax numbers, email addresses, and job titles. For such elements, verification via outbound contact and/or inbound web-based updating is the only method to ensure accuracy and timeliness.

This may be a disappointment to those who were hoping that they could simply “send our data out for clean up.” In fact, the best method for ongoing maintenance of many important data elements used by business marketers is outbound contact and verification. Because this is an expensive and time-consuming process, we recommend that you verify your most valuable accounts first, and then decide the benefit of continuing on to your lesser value accounts.

For a thorough understanding of what outside vendors can and cannot do for you via automated hygiene processes, we conducted a research study in 2004 that involved clean up of a sample file by four leading suppliers who have deep experience with business data. Please review the research results presented in this report.


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In the spring of 2004, we invited a group of leading business-to-business data services providers to join us in a research project to compare their various approaches to data clean up. Four vendors agreed to participate: Acxiom, DataFlux, Donnelley Marketing and Harte-Hanks. We compiled a sample fi le of 10,000 “live” names, containing 12 fi elds. The names we used came from a variety of client sources. They were all names of individuals at business addresses.

We asked the vendors to perform their typical hygiene processes on the data and send the results back to us within 30 days. We also asked them to answer some questions about their companies and their approaches to data hygiene. Finally, we requested that they do this work at no charge, for the benefi t of members of the business marketing community.

As you can imagine, this research project raised several fairly touchy issues. First, we were asking the vendors to open their doors, and reveal the results of their processes as compared to their direct competition. We gratefully acknowledge the courage and openness of the vendors who chose to participate. To reduce the competitive pressure, we are withholding the identity of the specifi c vendors in the report below (Table 5), which reveals the number of data elements corrected, by vendor.

Second, everyone involved in the project recognized the importance of protecting the privacy of the business people and companies whose names happened to turn up on the sample fi le. If the vendors were to apply their actual standard data hygiene processes to the fi le, live data was required. To protect the privacy of those involved, we have decided not to publish the sample records of individual names and addresses after clean up.

THE DATA H YGIENE COMPA R ATI V E A N A LYSIS PROJEC T

TA BLE 1:V ENDOR DEFINITIONS OF DATA H YGIENE

To make sure we were all talking about the same thing, we asked the vendors, “What is your company’s defi nition of data hygiene?”

Acxiom

“ Purpose-driven data management practices and/or processes that promote data accuracy. Typically is applied to name and address data content, correction and completion.”

DataFlux

“ A 5-phase data management cycle, including profi ling (inspection), quality (correction), integration (merging and linking), augmentation (enhancement), and monitoring (auditing and control). Understand the data problems: improve the data.”

Donnelley Marketing

“ A broad range of processes that collectively deliver the highest deliverability of an address: standardizing, correcting, updating and verifying.”

Harte-Hanks

“ The process of solving business problems resulting from inadequate data quality: accuracy, completeness, timeliness, validity.”

TA BLE 2 :V ENDOR-DE SCRIBED DIFFERENTI ATION

We thought readers would fi nd it helpful to understand how the vendors view themselves in comparison with their competition. So we asked the vendors,“What are the 5 most important ways your work differs from your competitors’?”


5

Acxiom

1. Ability to recognize and parse name, business name, and address components

2. Ability to recognize the difference between business and consumer entities

3. Ad hoc, batch, automated batch and real time support of hygiene solution delivery

4. Abilitec-enabled occupancy database tool

5. NCOA/ChangePlus

DataFlux

1. Integrated data profi ling and data quality technologies

2. Technologies developed in house, using the same core engine

3. Forthcoming capability to monitor data quality over time

4. Interface permits business users (non IT staff)

5. All processing done in one pass

Donnelley Marketing

1. Proprietary fi le of 13 million businesses in the US and Canada, and over 8 million executive contacts and title elements, for verifi cation, appending and addition of missing elements

2. 100% telephone verifi cation of each business record at least once a year

3. Ability to track executives at their home or business addresses

4. OnePass system allows all B-to-B hygiene to be done in one continuous logical fl ow

5. Proprietary Mailability Score that ranks each address based on deliverability

Harte-Hanks

1. Expertise in multiple vertical markets 2. Ability to provide customizable and fl exible client-specifi c hierarchy of business rules 3. Objective selection of best-of-breed vendors of business fi les, to suit client needs 4. Integrated access to both USPS advanced postal products and HH proprietary data 5. Broad data management tool set

TA BLE 3 :V ENDOR DEFINITIONS OF “BA D” DATA

To understand any potential differences in the subject matter, we asked, “How do you characterize ‘bad’ B-to-B data?”

TA BLE 4 :THE SA MPLE FILE

Our compiled fi le of business names and addresses came from a variety of client sources. We aimed for 10,000 names, and resulted in 9,699 usable records.

Each record contained the following fi elds:

Last name

First name

Phone number

Fax number

Email address

Business title

Company name

Address 1

Address 2

City

State

ZIP code

Acxiom

“ Data that fails to meet specifi c data content requirements and/or cannot be used to fulfi ll a specifi c business purpose.”

DataFlux

“ Any data that does not support the underlying processes or business applications built on that information.”

Donnelley Marketing

“ There is no right answer. The answer is to look within the customer segment and identify what is ‘bad’ to them.”

Harte-Hanks

“ Data that fails to support the mission of delivering the right message to the right individual through the right channel.”


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TA BLE 5 :TOPLINE C ORREC TION C OUNT S ON THE 9,69 9 REC ORDS

Each vendor reported slightly different counts, based on match rates and the data the vendors have on hand. You will notice that there are wide fl uctuations in the counts on non-postal data, like fax, phone and email. This is because some vendors own current data, while others rent or lease it from third parties as needed by their clients. For this research project, we did not want the vendors to incur any out-of-pocket expense, so data owners delivered higher counts in these categories. Another reason for discrepancies is the way the vendors defi ned certain fi elds, like Address line 1 versus Address line 2. What strikes us as we look at these results is, for the most part, how similar they are.

C ONCLUSIONS & OB SERVATIONS

This study suggests that large, reputable vendors will provide very similar services when it comes to postal address standardization and correction, such as ZIP+4 and NCOA (National Change of Address). The marketer should not ask the vendor to change a title or company name — this information must come from the customer. Marketers can request that the vendor provide

phone numbers, fax numbers and email addresses, but these are not part of standard data clean up as defi ned by most vendors. The append rates for these elements will differ by vendor, and no vendor can provide 100% coverage. In short, it’s the marketer who must make the fi nal call about customer data accuracy.

Vendor 1 Vendor 2 Vendor 3 Vendor 4

ZIP codes corrected 344 479 446 864

ZIP+4s added 8652 9375 8706 9101

Carrier routes coded 9342 9381 9401 9121

Delivery points coded 9333 9324 9203 9101

Street addresses corrected (addr1) 1776 5387 7583 685

Street addresses corrected (addr2) 704 1588 NA 3007

City names corrected 592 472 470 648

State codes corrected 163 472 470 158

Phone numbers appended 2101 4097 NA 4183

Fax numbers appended 0 2931 NA 2834

Email addresses appended 520 861 NA 645

NCOA matches 760 760 778 689

Delivery point validated records 9151 9212 9203 9086

CASS certifi ed records 9645 9596 9334 9589

This publication is part of a series entitled Business-to-Business Database Marketing, by Bernice Grossman and Ruth P. Stevens. Papers published to date include:

“Our Data is a Mess! How to Clean Up Your Marketing Database” (October 2005)

“Keep it Clean: Address Standardization Data Maintenance for Business Marketers” (February 2006)

“Outsourcing Your Marketing Database: A ‘Request for Information’ is the First Step” (March 2006)

These papers are available for download at www.dmrsgroup.com and www.ruthstevens.com

BERNICE GROS SM A N is president of DMRS Group, Inc., a marketing database consultancy in New York City. She is past chair of the B-to-B Council of The DMA. Reach her at [email protected]

RUTH P. S TE V EN S consults on customer acquisition & retention, and teaches marketing to graduate students at Columbia Business School. She is the author of The DMA Lead Generation Handbook, and her new book is Trade Show and Event Marketing, now available at Amazon. Reach her at [email protected]

9/23/2013

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Data “Based” Marketing… an infrastructure blueprint for serving the empowered consumer


Marcus TewksburyGlobal Vice President – Product Strategy

Experian

The Modern Consumer…

HYPERCONNECTEDHYPERCONNECTED

HIGHLY VOCALHIGHLY VOCAL

MOBILEMOBILE

EMPOWEREDEMPOWERED

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… and Their Path to Purchase

Not Just In Cartoons

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3

It’s about applying what you know… where it’s needed

• Data Enablement

• Blueprint

• Readiness Assessment

• Q&A

6

Agenda

9/23/2013

4

Why Are CMO’s Becoming Tech Focused?

Data Obsession

2.8

Zettabytes

On average, firms use less than 5% of the data available to them.Gartner estimates that 70‐85% of data is

“unstructured”.

70‐85%

The total worldwide volume of data is growing at 59% per year, with the number of files growing at 88% per year.

In 2012, the amount of information created and replicated was 2.8 zettabytes (2.8 trillion gigabytes).

59%Data

88%Files

5%5%

60%McKinsey estimates that retailers could improve operating margins by c. 60% by better leveraging their customer data

Source: Microsoft, McKinsey & Co.

9/23/2013

5

Following the $’s

05101520253035404550

2007 2010 2013

MeasurementCRMDMSocialMobileDisplayEmailMass

$50

$40$45

Budgets swung from Mass… and not going

back

Dollars are swinging back, but to growth areas

Email volumes are growing, but not the % of budget. Doing more… for

the same.

Tech 10‐Year History

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Tech 3‐Year History

Tech Today

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7

Big Data

Digital Ubiquity = Tracking Ubiquity

9/23/2013

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Reducing to Bite Sized Chunks

LinkageONLINE

OFFLINE

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Addressability

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Painting The Picture

Tech Today

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11

• Data Enablement

• Blueprint


• Q&A

21

Agenda

• Data Enablement

• Blueprint


• Q&A

22

Agenda

9/23/2013

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• Data Enablement

• Blueprint


• Q&A

23

Agenda

9/23/2013

1

Deadly Sins and the Ten Commandments:How to Achieve Best-Practices

Database Contentand Key Metrics Reporting

Jim WheatonPrincipal, Wheaton Group

919-969-8859, [email protected]

2

Overview of Wheaton Group

• We provide the link between the data and the marketing.– Database construction, management and hosting.– Data mining and consulting.– Collaborate on multi-channel communication programs.

• For example, outsourced database marketing department for Excelligence Learning Corporation, and White Cap Construction Supply.

• Four Principals with over 120 years of experience across well over 100 clients, and many verticals.

• A focus on B2B through our B2BMarketing.com joint venture.

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3

Overview of Today’s Session

• Best-Practices Marketing Database Content, the foundation for:– Analysis and measurement.– Data-driven CRM.

• The First 5 Commandments of Best-Practices Content.

• Insightful Key Performance Indicators (“KPIs” and “Dashboards”).

• The Second 5 Commandments of Best-Practices Content.

4

The CRM Revolution:“Star Wars” Database & Business Intelligence Technologies

• Access and manipulate massive amounts of data in seconds.

• Powerful GUI interfaces for eye-catching dashboards & reports.

• However, a caveat…

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9/23/2013

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10

Car Restorations and Best-Practices Content:Some Similarities

• It’s all about hard, ugly work and attention to detail:– Data audits and other forms of quality assurance. – Capturing the essence of the business in the data.

• Without all the hard work, the result will be what some car restorers call (crudely but accurately) candy-coated crap.

• This is because bad content always costs you money!– An example from financial services…

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11

Before We Start with the 10 Commandments,One Over-Riding Concept

• The ability to rapidly create multiple past-point-in-time views is essential for just about all quality analytics.

• For example:– Predictive analytics such as modeling.– Cohort analysis such as lifetime value estimations.– Monitor changes in customer inventories (KPIs).– Unanticipated back-in-time reporting capability, such as how to

catch a serial killer.

12

Commandment #1:Customer Hierarchies Must Be Created & Maintained

• Requires robust linkages across multiple database levels.

• Supports, for example:– Insight into the true nature of multi-buyers.– Accurate performance metrics such as lifetime value.– Innovative targeting programs.

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13

Commandment #2:Inquiry & Demand Transactions Must Be Maintained

• Examples of demand transactions:– Retail and direct including e-commerce: orders and items.– Subscriptions and continuities: payments.

• Include bill-to/ship-to (and, when applicable) sold-to linkages.– For B2B, universal applicability.– For B2B and B2C, seminal to gifting.

14

Commandment #3:Post-Demand Transactions Must Be Maintained

• Track progression from Demand to Gross to Net, including:– Backorders and cancels.– Returns, refunds and rebates.– Exchanges and allowances.– Delivery issues.

• Critical for large differences between Gross and Net, such as:– Trial periods at reduced or no cost.– Bad debt.– High-return businesses such as women’s apparel.

• Improves predictions & customers needing remedial action.

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15

Commandment #4:Promotion Transactions Must Be Maintained

• Maintain all promotional contacts across all channels.– Do not forget email.– Field sales and phone “touches,” if you can get them.

• Typical content: – Start date.– End date.– Coding (source codes, key codes, offer codes, etc.).– Offer terms (buy-one-get-one, percentage-off, dollars-off, etc.)

• Example of a 7-figure system with no promotion history…

16

Commandment #5:Supplemental Sources Must Be Considered

• For example:– Overlay demographics and psychographics.– For B2B, overlay “firmagraphics.”– Customer service (complaints, etc.).– Customer-generated gift messages.

• New media inputs (e.g., social networks & complainers).

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17

Key Performance Indicators:The Three Rules

• Rule #1: Strive for simplicity.

• Rule #2: Customer inventory report as the foundational KPI.

• Rule #3: Supplement with “The Why KPIs.”

18

Key Performance Indicator:The Customer Inventory Report

• Three factors determine monthly gross revenue (demand):– Number of customers.– Percent monthly buying rate.– Demand per buying customer.

• Track monthly, including year-over-year.– Or, as appropriate, weekly, seasonal, etc.– For example…

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19

The Customer Inventory Report:An Example

2008 2007 2006 2005Number of Companies 50,000 45,248 41,283 38,029Percent Monthly Buying Rate 25.0% 26.7% 26.9% 25.4%Demand Per Buying Company $300.00 $308.27 $301.83 $245.42Total Revenue $3,755,910 $3,723,296 $3,354,804 $2,369,592

2008 2007 2006YoY Number of Companies 10.5% 9.6% 8.6%YoY Percent Monthly Buying Rate -6.2% -0.9% 6.0%YoY Demand Per Buying Company -2.7% 2.1% 23.0%YoY Revenue 0.9% 11.0% 41.6%

Segments 1-3

# of Month's Demand/Com- Buying Buying

Month panies Rate Company Revenue

Oct 2008 10.5% -6.2% -2.7% 0.9%Sept 2008 11.3% 3.7% 4.1% 20.7%Aug 2008 11.8% -4.7% -5.8% 0.9%July 2008 12.4% -1.4% 2.6% 13.2%June 2008 11.9% -1.2% 1.3% 12.7%May 2008 12.6% -4.1% -3.5% 4.0%April 2008 12.4% 6.5% 1.2% 19.8%Mar 2008 11.1% -11.5% 0.1% -1.4%Feb 2008 11.4% 0.3% 2.9% 14.8%Jan 2008 11.2% 0.8% -3.0% 7.7%Dec 2007 10.2% 3.5% 8.1% 23.0%Nov 2007 10.0% -3.9% -4.3% 0.8%Oct 2007 9.6% -0.9% 2.1% 11.0%Sept 2007 9.7% -4.7% 3.9% 9.4%Aug 2007 10.5% -2.3% 11.9% 22.1%July 2007 11.6% 5.0% 8.8% 27.7%June 2007 11.7% 2.0% 11.1% 26.2%May 2007 11.4% 1.2% 9.7% 23.7%April 2007 11.5% 9.6% 9.8% 34.3%Mar 2007 11.6% -1.8% 1.9% 11.6%Feb 2007 11.4% 7.4% -1.2% 16.8%Jan 2007 10.1% 3.5% 8.3% 22.1%Dec 2006 9.0% 0.8% 6.4% 16.5%Nov 2006 8.7% 1.7% 16.1% 28.2%Oct 2006 8.6% 6.0% 23.0% 41.6%

Percent Year-Over-YearThree Key Factors & Overall Revenue, Segments 1-3

20

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21

Supplement with “The Why KPIs”

• Include net revenue if have significant post-demand activity.

• Include potential “why” factors, as appropriate, such as:– Backorder/cancel rates.– Out-of-stocks.– Returns/exchange rates.– Order-to-shipment turnaround.– Complaint levels.– Circulation variations.– Product changes.

• For example, the case of the missing merchandise.

• Now, for Commandments 6 through 10…

22

Commandment #6:Data Semantics Must Be Complete, Consistent & Accurate

• Semantics = naming conventions & coding/classification schemes.– Beware of changes, and of different coding across divisions.

• A common problem area is merchandise classification.– For example, class-department-division-season combinations.– Often reworked, but often not historically.

• Add a customer point-of-view.– For example, a merchandise segmentation we did…

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23

Commandment #7:The Data Must Not Be Archived or Deleted

• Rolling off older data is a common phenomenon.– Disk space is cheap…so, why?

• For example, models built off 36 months of data…

24

Commandment #8:The Data Must Be Maintained at the Atomic Level

• Can always aggregate, but can never disaggregate.

• For example, thanks to atomic-level data being maintained, the serial killer was caught.

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25

Commandment #9:The Data Must Be Time-Stamped

• Re-creation requires going beyond the naturally-date-driven. – Address changes, progression of change statuses,

demographics, etc.

• Modeling and product progression analysis.

• “The Easter Monster” & other floating events that drive behavior.– The importance of relative analysis.

• The serial killer was caught, but what if:– Only the most current address had been saved?– The old addresses had not been date-stamped?

26

Commandment #10:The Data Must Not Be Overwritten

• After the financial services example, enough said!

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27

The Ten Commandments ofBest-Practices Marketing Database Content

• Customer hierarchies must be created and maintained.• Inquiry and demand transactions must be maintained.• Post-demand transactions must be maintained.• Promotion transactions must be maintained.• Supplemental sources must be considered.• Data semantics must be complete, consistent and accurate.• The data must not be archived or deleted.• The data must be maintained at the atomic level.• The data must be time-stamped.• The data must not be overwritten.

28

For Additional Information

• “Marketing Should Control the Marketing Database, Not IT,” Chief Marketer, April 15, 2011

• “True Marketing Databases Make Sophisticated Data Mining Possible,” Direct Newsline, August 19, 2010

• “How Marketing Databases Differ from Operational Databases,” Direct Newsline, June 29, 2010

• “The First Five Commandments of Database Content Management,” Multichannel Merchant, February 1, 2007

• “The Second Five Commandments of Database Content Management,” Multichannel Merchant, May 1, 2007

9/23/2013

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29

Deadly Sins and the Ten Commandments:How to Achieve Best-Practices

Database Contentand Key Metrics Reporting

Jim WheatonPrincipal, Wheaton Group

919-969-8859, [email protected]

1910 South Highland Avenue, Suite 103 Lombard, IL 60148-6129 www.wheatongroup.com

1

Why Marketing Should Control the Marketing Database, Not IT

By Jim Wheaton

Principal, Wheaton Group

Original version of an article that appeared in the April 15, 2011 issue of “Chief Marketer”

I have been a direct and database marketing consultant since 1984. In all that time, one consistent

verity is that most internal IT departments think they can – and should – be responsible for the

marketing database. In many instances, the IT department has no idea what it is talking about.

Why is this? I think it has to do with the term “marketing database.” IT professionals hear the word

“database,” and say, “Ah ha! That means a system, and systems are in my bailiwick.”

Well, the IT guys are partly right, but they are mostly wrong. This is because, for the majority of

direct marketers, the systems component of a marketing database is relatively trivial. Sure, there are

some multiple-terabyte systems with near-real time update cycles, and dozens of users who need

simultaneous access. But, most databases are much smaller, and with no more than one of two users

accessing it at any given point in time.

For these smaller applications, the real challenge lies with the content; that is, the “stuff” of which

the database is constituted. This “stuff” can be very difficult to render consistent and usable because

of three challenges, none of which lies within the bailiwick of an IT professional:

Challenge #1: Name and Address Processing

B2C account information must be aggregated to the individual and household levels. Likewise, B2B

account information must be aggregated to the individual, site and organizational levels. These

multiple levels of customer (and, when applicable, prospect) definition are required to:

Perform accurate analysis, scoring, promotional selections, and response attribution.

Properly allocate marketing-spend to each customer.

In order to pull all of this off:

First, address standardization, ZIP Code correction, parsing and unduplication technologies – guided

by carefully-constructed business rules – must be employed to match accounts on a combination of

names, company names, addresses, phone numbers, and – when applicable – bill-to/ship-to

relationships.

Then, the matches must be unified into a single non-circular cross-reference that:

Assigns each account to one and only one individual.

Assigns each individual to one and only one B2C household or B2B site.

For B2B, assigns each site to one and only one organizational entity.


2

Finally, all of this must be maintained over time so that it is easy to make adjustments and

enhancements, and re-consolidate the data, per ongoing quality assurance that is conducted on the

matches.

Challenge #2: Transaction Processing

Hopefully, your customers are doing lots of buying. Most likely, the purchases are taking place

across multiple channels. You almost certainly have at least one e-commerce site, and you probably

have an in-bound call center. If some of your revenue comes from B2B, then you are likely to have

an outbound sales team and/or field sales force.

The data from each of these sources will have its own structure and anomalies. Multiple divisions

often mean even more permutations of data structures and anomalies, especially when company

mergers have taken place.

The bottom line is that transactional data is not particularly usable in its raw format. In order to

make the data usable, the semantics must be rendered historically complete, consistent and accurate,

and correspond with core business concepts. Also, the data must be time-stamped and maintained

down to the atomic level, and must not be overwritten, archived or deleted. Finally, the following

must be included:

Demand, as opposed to “shipped” or “completed,” transactions.

Promotion transactions, even for those that did not result in a purchase.

The following, when applicable: inquiry and post-demand transactions, and supplemental

sources such as demographics, “firmographics” and social networks.

Challenge #3: The Creation of Past-Point-In-Time Views

A modern database must support – on-demand – any calculation, aggregation or subset that logically

can be generated from the underlying data. This requires a mechanism to allow the efficient and

rapid re-creation of multiple past-point-in-time (“time-zero” or “time-0”) views. Time-0 views are

necessary because all of the dimensions to be analyzed cannot be known and "frozen" in advance.

These views form the basis for virtually all meaningful analytics, by allowing customers to be

classified based on detailed histories only up to the appropriate past-points-in-time.

Cohort analysis such as lifetime value is an important example of data mining that depends on the re-

creation of multiple time-0 views. Likewise, the analysis and validation files required for predictive

models are based on time-0 views.

Another application of cohort analysis is the monitoring of changes in customer “inventories,” such

as fluctuations in segment sizes and performance over time. Still another is the analysis of historical

trends within subsets of promotional channels, products and services offered, etc.

Final Thoughts

How many internal IT departments have the chops to handle these three challenges of marketing

database content? Not many! Those that do are typically concentrated among companies in which

the scale of the application is such that it makes sense to hire a team of experienced professionals.


3

Things are different for smaller database applications in which it is not cost effective to hire multiple

experienced professionals, much less staff to the level of job-function redundancy required to

counteract the inevitable resignations and terminations. All of this, to return to my opening

statement, is why most IT departments have no idea what they talking about when they think they

can – and should – be responsible for the marketing database.

Jim Wheaton is a Principal at Wheaton Group (www.wheatongroup.com), and can be reached at

919-969-8859 or [email protected]. The firm specializes in direct marketing

consulting and data mining, data quality assessment and assurance, and the delivery of cost-effective

marketing databases

http://www.wheatongroup.com/

mailto:[email protected]


4

True Marketing Databases Make Sophisticated Data Mining Possible

By Jim Wheaton


Original version of an article that appeared in the August 16, 2010 issue of “Direct Listline”

(This topic was first covered in the June 18, 2010 Direct Listline article, “How Marketing Databases

Differ from Operational Databases.”)

There is a big difference between a Marketing Database and an Operational Database. A Marketing

Database supports sophisticated data mining and an Operational Database does not.

Sophisticated data mining, in turn, is impossible without the ability to recreate multiple past-point-in-

time (“time 0”) views. This is because data mining professionals work in the present, on the past, in

anticipation of the future. For example, multiple customer and house non-buyer “time 0” views

make it possible to:

Create the analysis and validation files required for statistics-based predictive models.

Generate the data for all cohort analysis, including lifetime value.

Monitor changes in customer inventories, such as fluctuations in segment sizes over time.

Multiple “time 0” views also support data mining to understand how lifecycle changes affect

consumer purchase behavior. Direct marketers are lucky because, as a natural consequence of

running their businesses, they receive all of the detailed order, item and promotion history required to

perform lifecycle analysis. Retailers are not so lucky, unless they have a mechanism for identifying

customers and tracking their behavior. That is where Loyalty Programs come into play.

Let’s take a vertical industry – publishing – and work through a hypothetical example. Keep in mind

that, although the specifics are peculiar to publishing, the general concepts are universal across

vertical industries. We’ll begin with two assumptions:

A publisher of a magazine that is targeted to people in their 20's and 30's wants to understand

how changes in lifecycle affect renewal rates.

The publisher hypothesizes that renewal rates are adversely affected as subscribers begin to

raise families.

If the publisher’s hypothesis is true, then we would expect to see a drop in renewal rates as

subscribers move from multiple family dwelling units ("MFDUs”) to single family dwelling units

("SFDUs"), or from urban to suburban locations. With a properly constructed Marketing Database,

multiple subscriber cohorts can be analyzed over time for such relationships; that is, from when they

first signed up for the magazine though all of their subsequent renewal cycles.

People in their 20's and 30's are notoriously mobile. For example, from the time I entered the

workforce in 1980 to when I purchased my first (SFDU) home in 1988, I lived in five different


5

apartments in three different cities and states. Without being able to recreate time-0 information, it

would be impossible to track this sort of customer movement.

The inability to track customer movement is the unfortunate outcome of any Marketing Database

designed such that, every time an address change is received, the previous address is over-written.

Such a database will never be able to support data mining to understand how lifecycle changes affect

customer purchase behavior, no matter how many years of history have been accumulated.

Does your Marketing Database over-write address information as notifications of customer

relocations are received? Are you even certain that you have a Marketing Database? Many

companies think they have a Marketing Database when, in fact, what they really have is an

Operational Database. I have seen this countless times when talking to prospective clients.

If you want to know if you have a true Marketing Database, then take the five-step data processing

test outlined in the June 18, 2010 Direct Listline article, “How Marketing Databases Differ from

Operational Databases”: http://directmag.com/lists/0622-lists-how/




marketing databases.

http://directmag.com/lists/0622-lists-how/




6

How Marketing Databases Differ from Operational Databases

By Jim Wheaton


Original version of an article that appeared in the June 29, 2010 issue of “Direct Newsline”

A Marketing Database must be able to perform all of the mission-critical analytical tasks required for

data-driven marketing. Many people think they have a Marketing Database when, in reality, what

they have is an Operational Database. An Operational Database supports essential “nuts and bolts”

tasks such as customer service, fulfillment and inventory management. But, it falls short in the

support of data-driven marketing analysis.

To determine if you have a Marketing Database, take the following data processing test. If you can

easily and rapidly execute the five tasks within the test, with no outside-the-system processing, then

you will know for sure that you have a Marketing Database:

FIRST: Examine the life-to-date detail for your customers as of June 1, 2009; that is, about a year

ago. This is known as a past-point-in-time (“time-0”) view, which will be impossible to recreate if

any of the following is true:

Some of your customers as of June 1, 2009 are no longer in the system.

Some of the historical data previous to June 1, 2009, for some or all of your customers, has

been deleted or overwritten.

You cannot exclude from your examination all historical data subsequent to June 1, 2009.

SECOND: Rank your customers from best to worst, as they would have been ranked on June 1,

2009. Do this by evaluating each customer’s year-ago view by whatever selection system you use;

that is, a statistics-based predictive model (or models), or some sort of rules-based logic such as

Recency/Frequency/Monetary (“RFM”) Cells.

THIRD: Divide the ranked customers into deciles; that is, into equal groups of ten.

FOURTH: For each decile, calculate the following subsequent performance; that is, from June 1,

2009 through May 31, 2010: Average Per-Customer Revenue and Average Per-Customer

Promotional Spend. Please note that the second will be impossible to calculate if you do not

maintain all-important promotion history for all your customers on a campaign-by-campaign basis,

regardless of whether a given customer did or did not respond to a given campaign.

If you can do all this, then you might have a Marketing Database. To know for sure, you

need to be able to do one last thing:

FIFTH: Simultaneously for each of three additional past-points-in-time – that is, June 1 for each of

the years 2008, 2007 and 2006 – create a standard File Inventory Report. The specifics will vary by

the type of business you are in, but invariably will include: 1) permutations of customer counts,

purchase rates and dollar amounts, and 2) year-over-year absolute as well as percent changes.


7

Components of your File Inventory Report should also double as Key Performance Indicators

(“KPI’s”) that are closely tracked throughout the organization.

If you can do all this, then you really do have an environment worthy of being called a Marketing

Database. The reasons a Marketing Database needs to be able to do these five tasks are because:

Database marketing is, by definition, driven by deep-dive data mining.

Deep-dive data mining, in turn, requires the ability to rapidly recreate past-point-in-time

(“time 0”) views, and then manipulate and report on the data within these views. In fact, it is

common for multiple such views to have to be simultaneously recreated.

Without this ability, you will not be able to efficiently execute any cohort analysis such as

lifetime value. Nor will you be able to easily construct any statistics-based predictive

models.

Whether or not the Marketing Database and the Operational Database should be the same physical

resource is an entirely different issue. And, an entirely different article.




marketing databases.




8

The First Five Commandments of Database Content Management

By Jim Wheaton


Original version of an article that appeared in the February 1, 2007 issue of

“Multichannel Merchant”

This is the commencement of a quarterly column that will focus on best practices in data mining. We

define data mining as all of the analytical methods that are available to transform data into insight.

Examples include statistics-based predictive models, homogeneous groupings (“clusters”), cohort

analyses such as lifetime value, quantitative approaches to optimizing contact strategies across

multiple channels, and the creation of report packages and key-metrics dashboards.

What this Column Will Not Be About

We will not spend a lot of time comparing predictive modeling techniques and software packages.

Much has been written, for example, about the merits of regression versus neural networks. Having

participated in countless model builds, I speak first-hand to the fact that technique plays only a

secondary role in the success or failure of a predictive model.

Discussions about modeling techniques have always reminded me of the theological debate that took

place many centuries ago about how many angels can dance on the head of a pin. Today’s data

miners are fixated on their own pins and angels when they wrangle about techniques!

A by-product of this wrangling is the fantastic claims made by proponents of some of these

techniques. Unfortunately, such claims are pabulum for the gullible. The inconvenient truth, to

borrow a phrase from a prominent national politician, is that technique has very little impact on

results. There is only so much variance in the data, and the stark reality is that new techniques are

not going to drastically improve the power of predictive models.

What this Column Will Be About

The focus will be on the truly important issues; namely, just about everything else having to do with

data mining. For example, this month’s topic will be the significant improvements that are possible

for optimizing the raw inputs to the data mining process. The ultimate goal is to perform data mining

off a platform that we at Wheaton Group refer to as Best Practices Marketing Database Content.

This, in turn, supports deep insight into the behavior patterns that form the foundation for data-driven

decision-making.

General Characteristics of Best Practices Marketing Database Content

For starters, Best Practices Marketing Database Content provides a consolidated view of all

customers and inquirers across all channels. Examples of channels include direct mail, e-commerce,

brick-and-mortar retail, telesales and field sales. Sometimes – and particularly in Business-to-

Business and Business-to-Institution environments – prospects are included.


9

Best Practices Marketing Database Content is as robust as the underlying methods of data collection

are capable of supporting. The complete history of transactional detail must be captured. Everything

within reason must be kept, even if its value is not immediately apparent. For example:

One multi-channel marketer failed to forward non-cash transactions from its brick-and-mortar

operation to the marketing database. This became a problem when a test was done to determine the

effectiveness of coupons sent to customers, which were good for free samples of selected

merchandise. The goal was to determine whether these coupons would economically stimulate store

traffic. But, because the corresponding transactions did not involve cash, there was no way to mine

the database for insights into which customers had taken advantage of the offer, and what the

corresponding effect was on long-term demand.

The Ten Commandments of Best Practices Marketing Database Content

There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database

Content. Five are discussed this month, and the balance will be covered in the next column:

#1: The Data Must Be Maintained at the Atomic Level

All customer events such as the purchase of products and services must be maintained at the lowest

feasible level. This is important because, although you can always aggregate, you can never

disaggregate. Robust event detail provides the necessary input for seminal data mining exercises

such as product affinity analysis.

“Buckets” and other accumulations created from the data should be avoided. This is particularly

important for businesses that are rapidly expanding, where it can be impossible to audit and maintain

summary data approaches across ever-increasing numbers of divisions.

One firm learned the hard way about the need to maintain atomic-level detail when it discovered that

its aggregated merchandise data did not support deep-dive product affinity analysis. This is because,

by definition, it was impossible to understand purchase patterns within each aggregated merchandise

category. For example, with no detail beyond “Jewelry,” there was no way to identify patterns across

subcategories such as Watches, Fine/Fashion Merchandise, Bridal Diamonds, Fashion Diamonds,

Pearls/Stones, Accessories and Loose Goods.

#2: The Data Must Not Be Archived or Deleted

Within reason, data must not be archived. Likewise, it must not be deleted except under rare

circumstances. Ideally, even ancient data must be retained because you never know when you might

need it. Rolling off older data is perhaps the most common shortcoming of today’s marketing

databases; an ironic development because, unlike ten or twenty years ago, disk space is cheap.

Data mining can be severely hampered when the data does not extend significantly back in time.

One database marketing firm experienced this when it tried to build a model to predict which

customers would respond to a Holiday promotion. Unfortunately, all data content older than thirty-

six months was rolled off the database on a regular basis. Remarkably, it was not even archived. For


10

example, the database would only reflect three years of history for a customer who had been

purchasing for ten years.

The only way to build the Holiday model, of course, was to go back to the previous Holiday

promotion. This reduced to twenty-four months the historical data available to drive the model.

More problematic was the need to validate the model off another Holiday promotion; the most recent

of which had – by definition – taken place two years earlier. This, in turn, reduced to twelve months

the amount of available data. As you can imagine, the resulting model was far from optimal in its

effectiveness!

#3: The Data Must Be Time-Stamped

The use of time-stamped data to describe phenomena such as orders, items and promotions facilitates

an understanding of the sequence of progression for customers who have been cross-sold. This is

also true if customers are found to have purchased across multiple divisions during the incorporation

of acquired companies. Corresponding data mining applications include product affinity analysis and

next-most-likely-purchase modeling.

#4: The Semantics of the Data Must Be Consistent and Accurate

Descriptive information on products and services must be easily identifiable over time despite any

changes that might have taken place in naming conventions. Consider how untenable analysis would

be if the data semantics were so inconsistent that – say – “item number 1956” referenced a type of

necktie several years ago but umbrellas now. Also, the reconciliation of different product and

services coding schemes must be appropriate to the data-driven marketing needs of the overall

business, and not merely to the individual divisions.

#5: The Data Must Not Be Over-Written

Deep dive data mining is predicated upon the re-creation of past-point-in-time “views.” For

example, a model to predict who is most likely to respond to a Summer Clearance offer will be based

on the historical information available at the time of an earlier Summer Clearance promotion. The

re-creation of point-in-time views is problematic when data is overwritten.

A major financial institution learned this in conjunction with a comprehensive database that it built to

facilitate prospecting. After months of work, the prospect database was ready to launch. The

internal sponsors of the project, anxious to display immediate payback to senior management,

convened a two-day summit meeting to develop a comprehensive, data-driven strategy.

One hour into the meeting, the brainstorming came to an abrupt and premature end. The technical

folks, in their quest for processing efficiency, had not included in the database a running history of

several fields that were critical to the execution of any data mining work. Instead, the values

comprising these fields were over-written during each update cycle.

The incorporation of this running history necessitated a redesign of the prospect database. The

unfortunate result was a two-month delay, a loss of credibility in the eyes of senior management, and

a substantial decline in momentum.


11

Final Thoughts

The next column will focus on Commandments Six through Ten of Best Practices Marketing

Database Content. In the meantime, consider whether your marketing database violates any of the

first five Commandments. The extent to which it does is the extent to which your firm’s revenues

and profits are being artificially limited.




marketing databases




12

The Second Five Commandments of Database Content Management

By Jim Wheaton


Original version of an article that appeared in the May 1, 2007 issue of

“Multichannel Merchant”

There are Ten Commandments of marketing database content management. This first five were

outlined in my February 1, 2007 column. This month, we will focus on the remaining five. But first,

a synopsis of the February column:

Data mining is enhanced, and often dramatically, when the source data is improved. The ultimate

goal is for data mining to be performed off a platform that we at Wheaton Group refer to as Best

Practices Marketing Database Content. This, in turn, supports deep insight into the behavior patterns

that form the foundation for data-driven decision-making.

Best Practices Marketing Database Content provides a consolidated view of all customers and

inquirers across all channels. The complete history of transactional detail must be captured.

Everything within reason must be kept, even if its value is not immediately apparent.

There are Ten Commandments that, if followed, will ensure Best Practices Marketing Database

Content. The first five as discussed in the February column are:

#1 – The data must be maintained at the atomic level.

#2 – The data must not be archived or deleted.

#3 – The data must be time-stamped.

#4 – The semantics of the data must be consistent and accurate.

#5 – The data must not be over-written.

The following are the balance of the Ten Commandments:

#6: Post-Demand Transaction Activity Must Be Kept

Post-demand transaction activity can include cancels, rebates, refunds, returns, exchanges,

allowances and write-offs. These are essential for important exercises such as the identification of

customers who will be less likely to make future purchases without remedial action. After all,

customers who are disappointed by unavailable, ill-fitting or damaged merchandise, or poorly-

conceived and improperly functioning services, will be less likely to purchase in the future. One

common data mining application is attrition modeling.

The capture of post-demand activity is particularly important in environments such as high fashion

women’s apparel where return rates can be as high as 40%. Often, customers with similar gross

purchase volume can have very different return rates. This, in turn, can make the difference between

a profitable customer and one who is a continuous money-loser. It makes sense for predictive

models to take such discrepancies into account when rank-ordering customers on expected behavior.


13

Tracking post-demand transactions can be a challenge because it requires the transactions to be

retained by the underlying operational systems that feed the marketing database. Unfortunately,

many operational systems are not equipped for this task. Instead, post-demand transactions vanish

subsequent to a change in shipping status. For example, a “backorder” status will disappear once the

corresponding item has been shipped. The following hypothetical sequence of events illustrates why

this is problematic:

Assume that an operational system feeds a marketing database update process on the first and

fifteenth of every month. Also assume that a backorder is generated on June 2, and that the

corresponding shipment takes place on June 14. By definition, the customer had to wait twelve days

for the merchandise to shipped, which certainly is not ideal from a CRM perspective. If the

operational system does not retain backorder statuses, then the June 1 and June 15 “snapshots” that

feed the marketing database will fail to reflect the twelve-day wait. With only the June 12 shipment

reflected, an important aspect of the customer relationship will have been lost!

#7: Ship-To/Bill-To Linkages Must Be Maintained

Often, these correspond to gift-giver/receiver relationships. Ship-to/bill-to linkages allow targeted

promotions to extend the customer universe beyond those who made the original purchase. In fact,

savvy database marketers look upon giftees as qualified prospects. In this way, customer databases

can be used to drive targeted prospecting promotions, and often with formal data mining techniques.

#8: All Promotional History Must Be Kept

All promotional contacts across all available channels must be retained. This is necessary to rapidly

and accurately create the past-point-in-time “views” required for most data mining projects,

including predictive models. For multi-divisional firms, and especially those that have acquired

other companies, it is important to appropriately handle different coding practices.

One marquee, multi-billion dollar retailer with a substantial catalog/e-commerce division learned the

hard way the importance of including promotion history. Although it spends seven figures a year on

its CRM system, the underlying marketing database does not contain promotion history. As a result,

most data mining projects take a week longer than they should, because of the extraneous processing

required to overcome the lack of promotion history when creating analysis files.

#9: Proper Linkages Across Multiple Database Levels Must Be Maintained

For Business-to-Consumer (“B-to-C”) environments, individuals must be properly linked to

households. For Business-to-Business (“B-to-B”) and Business-to-Institution (“B-to-I”)

environments, individuals must be linked to sites, and sites to organizations. This allows the

calculation of accurate performance metrics such as promotional financials, and for understanding

the true nature of multi-buyers.

Such links also enable the tracking of pass-along response, and for innovative targeting programs.

For example, B-to-B and B-to-I direct marketers can monitor contract compliance across multiple

sites within large client organizations. In such instances, discounted pricing is predicated on

purchases not being made from the competition. With Best Practices Marketing Database Content,


14

sites within client organizations can be identified that have not received any mission-critical

merchandise. Such sites may be out of contract compliance.

#10: Overlay Data Must Be Included, As Appropriate

For B-to-C, overlay data can be appended to create a complete view of customers, inquirers and,

when applicable, prospects. Likewise for B-to-B and B-to-I, “firmagraphics” can be added to create

a complete view of customers, inquirers, sites and organizations.

One form of B-to-C overlay data is demographics for existing individuals and households on the

marketing database, including date of birth, age, gender, marital status and presence of children.

Another is the identity of additional adults within households on the database, along with their

corresponding individual-level demographics.

For B-to-B and B-to-I, firmographics include SIC or NAICS Code, Number of Employees, and

Revenue. Also, additional individuals can be appended to sites that are resident on the database, and

additional sites to organizations.

One primary data mining application is the creation of profiles to “paint a picture” of customers and

inquirers. However, the possibilities go far beyond that, and are limited only by the imagination. For

example, date of birth can be employed to support birthday offers. Specifically, individuals with

upcoming birthdays can be targeted with offers of special savings to “treat themselves.” Also,

suitable gifts can be promoted to significant-others within the households. Such programs are

especially lucrative for retailers.

A Case Study of What Not to Do

Last year, Wheaton Group was approached about a potential data mining project by a well-known

gift-oriented, multi-billion dollar retail and direct marketing company that has been in decline. It

soon became apparent that the firm’s marketing database content would support neither the project

nor any other form of meaningful data mining. This is because:

Data is archived after 36 months and is difficult to resurrect. Some portions of the database are

maintained at the surname (“last name”) level and others at the individual level. For surname-level

database records, only one individual’s identity is retained. This means that if a husband orders the

first time, and then the wife orders – say – five subsequent times, the database will reflect six orders

from the husband. This is particularly problematic for a gift-oriented business. To complicate

matters, the database does not track bill-to/ship-to linkages and the corresponding gift relationships

that these imply, nor does it contain gender codes.

Often, the acquisition source is inaccurate, which renders problematic many worthwhile analyses

such as long-term value. Also, merchandise coding discipline does not exist, the Website does not

allow source codes to be entered, and customer records generally do not reflect post-demand

transactions such as merchandise returns.


15

Promotion history is essentially unusable because the database tracks massive amounts of “spurious”

activity; for example, “event occurrences” such as records that have been sent to the service bureau

for National Change of Address (“NCOA”) processing. Also, there are significant problems with

tying promotion history to specific names and addresses, and email promotions are not tracked at all.

Finally, on the Retail side, distance-to-store calculations are based on imprecise ZIP-to-ZIP

Centroids. And, they reflect only the nearest store, not where the actual purchase activity has taken

place.

Clearly, unless the company rectifies the appalling state of its marketing database content, it will

have little chance of reversing its decline!

Final Thoughts

Consider whether you are working with Best Practices Marketing Database Content. The extent to

which you are not is the extent to which you are artificially limiting the size of your firm’s revenues

and profits. Also consider what methods you might employ to improve database content by

enhancing the functionality of your operational systems. There are all sorts of ways to do this. But,

that is the topic of a future article.




marketing databases



Introduction to Wheaton Group

Wheaton Group LLC, launched in 1989 as “Strategic Insight” and renamed in January 2000, is a direct and database marketing services firm led by four Principals with over 120 years of experience across well over 100 clients and spanning:

Business-to-business, business-to-consumer and B2B/B2C hybrids.

Many vertical industries including catalog, consumer package goods, financial services, non-profit, publishing and telecommunications.

All major selling and distribution channels including retail, direct (mail, phone and e-commerce) and field sales.

Wheaton Group’s work is grounded in a continuous focus on data quality assessment and assurance. The firm’s core competencies include:

The creation of marketing databases that offer the best-practices content required to support the most advanced forms of analytics, and hosted and maintained either by us or the client.

Robust data management services including the execution of selects for multi-channel promotional campaigns.

The leveraging of marketing database content through advanced analytics, reporting and quantitatively-grounded consulting.

Wheaton Group also provides its services through the B2BMarketing.com joint venture.

Biographies of Wheaton Group’s Four Principals

Jim Wheaton has been a direct and database marketer since 1981. He began in line management. Then, he was a consultant at Kestnbaum & Company, Vice President of Research & Consulting at Wiland Services, Senior Vice President of Strategic Consulting at KnowledgeBase Marketing, and Co-Founder of Wheaton Group. Jim has authored well over 200 articles and speeches, is former Chairman of The DMA Analytics Council, and holds an MBA from The University of Chicago and a BA from Brown University.

Cynthia Wheaton has been a direct and database marketer since 1978. She began in line management, spearheading new venture development at Sara Lee Direct and then at World Book Encyclopedia. One such venture was “Just My Size,” the national retail brand. Cynthia later served as VP of Marketing for GRI Corp. She became a consultant in 1986 at Kestnbaum & Company. In 1989, she launched Strategic Insight, the precursor to Wheaton Group. Cynthia has an MBA from the University of North Carolina at Chapel Hill as well as a BA in English.

Boris Gendelev has specialized in marketing data warehousing, software development and analytics since joining the direct and database marketing consulting profession in 1983. He began at Foote Cone & Belding Direct Marketing Systems. Then, Boris was a Vice President at Precision Marketing, a position that he maintained throughout the Direct Marketing Technology (“Direct Tech”) and Experian acquisitions. Boris joined Wheaton Group in 2002 as a Principal. He has an MBA from The University of Chicago as well as a BS in Computer Science.

Leo Sterk has specialized in strategic analytics since joining the direct and database marketing consulting profession in 1984. He began in the industry as a consultant at Kestnbaum & Company. Then, Leo was a Vice President at Precision Marketing, a position that he maintained throughout the Direct Tech and Experian acquisitions. Leo joined Wheaton Group in 2004 as a Principal. He has an MBA from The University of Chicago, and bachelors and masters degrees from the University of Illinois-Urbana in the field of urban planning.

For more information, contact Jim Wheaton (919-969-8859; [email protected]).

9/23/2013

1

Leveraging Your Database: Reporting, Templates and Strategic Applications

Database Intensive: Part 4

Al Bessin, Bessin Consulting

The Big Points

• Background• Who is My Customer? • Customer Balance Sheet• Media Reporting• Performance Measures • Putting It All Together• Q&A

9/23/2013

2

Premise: Marketing Database is Just A Tool

• Need a Platform to support marketing– Objectives– Planning– Execution– Analysis

Goal: To Maximize Customer Value• Relevant & timely communication increases value

• Understand customer purchasing velocity• Proactively adjust your marketing and message

9/23/2013

3

Identifying Your Customer

Starting with a Clean Customer File

• Normalizing to the unique customer is key to accurate analysis

9/23/2013

4

Customer Balance Sheet• Measuring changes in customer mix is an essential exercise• Take snapshots on a quarterly basis to account for seasonality

Customer Balance Sheet Reporting• Summarize changes in customer file composition for better view of trends

• Combine the Balance Sheet with the “Income Statement” or view of what happened in the period

9/23/2013

5

Customer File Views

• Use views that support your business trends and objectives

2009 2010 2011Q1 210,926 233,993 259,835 New 23,278 24,067 25,263 Reactivated 10,751 12,372 13,545 Retained 176,897 197,554 221,027 Q2 213,272 231,955 259,211 New 18,808 16,815 16,260 Reactivated 7,657 8,653 9,225 Retained 186,807 206,487 233,726 Q3 214,355 235,929 256,320 New 14,374 17,568 15,486 Reactivated 9,002 9,996 9,854 Retained 190,979 208,365 230,980 Q4 229,730 255,929 - New 54,041 62,980 - Reactivated 21,169 24,637 - Retained 154,520 168,312 -

Last Order 2006 2007 2008 2009 2010 2011 Total2006 86,692 - - - - - 86,692 2007 16,615 72,524 - - - - 89,139 2008 15,344 14,111 65,794 - - - 95,249 2009 17,094 13,616 14,059 73,842 - - 118,611 2010 23,647 16,380 14,822 19,017 94,520 - 168,386 2011 41,249 22,889 18,767 20,790 29,011 91,927 224,633 Total 200,641 139,520 113,442 113,649 123,531 91,927 782,710

First Order Year

Media Reporting

9/23/2013

6

The Challenge

• Number and types of marketing media have exploded• Consumer behavior continues to evolve • Campaign analysis is increasingly complex • Intense competition for marketing $s• Results are overstated Actual

Total Demand

Paid Search

Catalogs

Email

Organic Search

Social Marketing

CSEs

60%

Mass Media

Don’t use 160% of actual results to justify campaigns!

Attribution Methodologies

• Vendor reporting alone does not work• Matchback to contact files

– Assumes all demand within windows is driven by the one contact– Only factors in push campaigns

• Web order referring source (last touch)– Credits only the incoming medium – may simply be convenient

• Last touch online plus matchback– Simple to implement, normalizes total demand– Can use weighting and fractional allocation

• Multi‐touch attribution– Very complex, but still a model– Perils of cumulative errors for smaller populations

9/23/2013

7

Resolve Demand Across Media

• Most important – develop a holistic view of demand across all media• Start simply, if necessary, and then evolve

Strive for a holistic view of marketing

Order�Management�Feed Allocation�Model�OutputReporting�Channel Demand Allocated�Demand�by�Medium DemandCatalog $371,587 Catalog $527,742Website $322,694 Email $108,128Amazon $18,245 Paid�Search�‐�Brand $7,843Email $89,435 Paid�Search�‐�Competitive $45,889Total $801,961 Natural�Search�‐�Brand $8,457

Natural�Search�‐�Competitive $86,384Comparison�Shopping�Engines $0

Google�Analytics�Feed Marketplaces $17,518Allocated�Demand�by�Medium Demand Total $801,961Paid�Search $85,971Natural�Search $134,674Comparison�Shopping�Engine $0Other $191,484Total $412,129

Resolve

Test to Determine Validity• For email, direct mail and telemarketing, holdout campaigns

are ideal tests– Simple – measure total purchases by each population– Sometimes management resists

Tips:• Test over sufficient time• Hold test groups constant• Ensure sample size is sufficient to get statistical significance

9/23/2013

8

Performance Measures

Background Overview of Financial Metrics

• Financial Metrics– Sales– Gross Margin (differentiate between product margins and gross

margin)– S&H Contribution (Expense)– Variable Transaction Expenses– Marketing Expenses– Semi Variable Expenses– Fixed Expenses

• Contribution Analysis– Typically to make incremental decisions

• Solve for the cost to drive n+1 revenue

– Relevant expenses are variable

9/23/2013

9

Defining Order Contribution• Order contribution

analysis is critical to evaluating marketing effectiveness

• May require data external to a marketing database to complete

Marketing Contribution and Breakeven• Solve for the demand needed to cover variable marketing expense

– Catalog/Direct Mail/Email examples are simplest

9/23/2013

10

Customer Value Analysis

• Defining “Lifetime”– Financial ROI windows are typically one to two years– Buyer behavior typically falls off rapidly after a few years

• Application of Value Analysis– Establish metric for acquisition cost– Basis to compare different media and quality of acquisitions

“Lifetime Value” is the variable contribution in the first and second years after initial purchase

• Lifetime Value = Sum of Order Gross Margin less Variable Transaction Expense less Marketing Expense for orders in a one‐ or tw0‐year window after the initial order

Example: Traditional vs. Digital Media LTV

9/23/2013

11

Media Evaluation

• Evaluate marketing programs based on– Cost of new buyer account acquisition– Comparative value of acquired buyers– Contribution across retained buyers– Marketing ROI

• Tailor for maximum effectiveness for each target group– Contact type– Contact cadence

Media Performance

• Report on Media rather than by Order Method– It is much more relevant for marketing (and more predictive)

Demand�by�Promotional�MediaPromotional�Media Month�TY Month�LY ∆%�TY/LYCatalog $1,282,145 $1,155,720 10.9%Natural�Search�‐�Branded $124,778 $41,710 199.2%Natural�Search�‐�Non�Branded $58,486 $14,586 301.0%Paid�Search�‐�Branded $52,313 $0 �Paid�Search�‐�Non�Branded $26,032 $200 12915.9%Web�(Other) $277,457 $307,173 ‐9.7%Email $179,419 $114,522 56.7%Grand�Total $2,000,630 $1,633,911 22.4%

$0��

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Catalog� Natural�Search�‐�Branded�

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Allocated�Demand�by�Marke ng�Medium�

Month�TY� Month�LY�

9/23/2013

12

Acquisition Costs by Marketing Media

• Cost per acquired new buyer is part of the equation

TY�1st�Order�Contribution

LY�1st�Order�Contribution

TY�New�Buyers

LY�New�Buyers #%�TY/LY ∆%�TY/LY

Catalog (4.32)$�� (4.75)$�� 2,345�� 2,568�� (223)�� ‐9%Email 14.20$�� 16.19$�� 212�� 84�� 128�� 152%SEM�‐�Branded 8.56$�� 10.02$�� 217�� 21�� 196�� 933%SEM�‐�Competit (1.31)$�� (0.05)$�� 648�� 500�� 148�� 30%SEO�‐�Branded 8.56$�� 10.86$�� 213�� 123�� 90�� 73%SEO�‐�Competit 10.31$�� 10.21$�� 875�� 722�� 153�� 21%Comparison�Sh 6.32$�� 7.52$�� 236�� ‐�� 236�� ‐Marketplace 5.17$�� 6.23$�� 145�� ‐�� 145�� ‐Total 1.43$�� (0.48)$�� 4,891�� 4,018�� 873�� 22%

Customer Value by Acquiring Media

• Understand the real value of media by looking at the downstream value of buyers – that is the rest of the equation

1st�Time�Buyer�12�Month�Activity�Post�Initial�Purchase

1st�Order�Promotional�Media12M�1st�Time�

Custs1st�Order�Demand

1st�Order�AOV

Subsequent�12�Mo�Orders

Subsequent�12�Mo�Demand

Orders/New�Customer

Demand/New�Customer

Catalog 22,111�� $1,587,013 72$�� $5,820 $468,445 0.26�� $21.19Natural�Search�‐�Branded 5,906�� $443,547 75$�� $1,195 $118,929 0.20�� $20.14Natural�Search�‐�Non�Branded 4,961�� $287,175 58$�� $786 $65,575 0.16�� $13.22Paid�Search�‐�Branded 966�� $78,075 81$�� $95 $10,680 0.10�� $11.06Paid�Search�‐�Non�Branded 935�� $57,866 62$�� $54 $2,890 0.06�� $3.09Advertising 18,637�� $1,319,660 71$�� $4,063 $411,634 0.22�� $22.09Email 7,054�� $782,065 111$�� $1,554 $231,113 0.22�� $32.76Grand�Total 60,570�� $4,555,400 75$�� $13,567 $1,309,265 0.22�� $21.62

�22,111��

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�966�� 935��

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9/23/2013

13

Downstream Media Response

• Identify variances in how customer acquired by different media respond to other media downstream

Campaign Performance

• Compare campaigns across different media using the same metrics

Customer

9/23/2013

14

Product Performance by Media

• Compare merchandise sales by different mediaDemand�by�Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other�Online TotalAppliances $283,477 $23,247 $17,886 $3,028 $31,093 $28,883 $132,816 $520,430Food/Cooking $609,122 $67,180 $37,970 $37,705 $481,807 $147,761 $107,151 $1,488,696Garden $167,907 $16,453 $5,999 $6,823 $9,928 $32,600 $22,997 $262,707Books $75,566 $7,158 $2,877 $2,951 $1,159 $15,994 $12,774 $118,479Other $76,382 $9,357 $1,807 $2,653 $463 $9,446 $24,909 $125,017Personal�Care/Clothing $88,309 $5,332 $2,866 $2,756 $349 $13,677 $9,047 $122,335Books/DIY $12,610 $1,388 $267 $421 $1,005 $6,167 $7,109 $28,967Gifts $42,168 $2,222 $1,912 $1,399 $2,075 $6,726 $4,588 $61,091Housewares $240,118 $27,985 $20,271 $12,776 $8,244 $56,617 $39,067 $405,077Tools $173,669 $21,974 $15,946 $9,247 $14,575 $50,377 $33,193 $318,981Lighting $167,641 $24,718 $17,882 $14,796 $3,585 $55,212 $37,360 $321,194Toys $22,422 $2,301 $1,787 $1,059 $564 $6,596 $2,717 $37,445Total $1,959,391 $209,315 $127,468 $95,614 $554,847 $430,057 $433,727 $3,810,419

Percent�of�Total�for�Media Catalog SEO‐Brand SEO‐Other SEM‐Brand SEM‐Other Email Other�Online TotalAppliances 14.5% 11.1% 14.0% 3.2% 5.6% 6.7% 30.6% 13.7%Food/Cooking 31.1% 32.1% 29.8% 39.4% 86.8% 34.4% 24.7% 39.1%Garden 8.6% 7.9% 4.7% 7.1% 1.8% 7.6% 5.3% 6.9%Books 3.9% 3.4% 2.3% 3.1% 0.2% 3.7% 2.9% 3.1%Other 3.9% 4.5% 1.4% 2.8% 0.1% 2.2% 5.7% 3.3%Personal�Care/Clothing 4.5% 2.5% 2.2% 2.9% 0.1% 3.2% 2.1% 3.2%Books/DIY 0.6% 0.7% 0.2% 0.4% 0.2% 1.4% 1.6% 0.8%Gifts 2.2% 1.1% 1.5% 1.5% 0.4% 1.6% 1.1% 1.6%Housewares 12.3% 13.4% 15.9% 13.4% 1.5% 13.2% 9.0% 10.6%Tools 8.9% 10.5% 12.5% 9.7% 2.6% 11.7% 7.7% 8.4%Lighting 8.6% 11.8% 14.0% 15.5% 0.6% 12.8% 8.6% 8.4%Toys 1.1% 1.1% 1.4% 1.1% 0.1% 1.5% 0.6% 1.0%Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Catalog�54%�

SEO‐Brand�4%�

SEO‐Other�3%�

SEM‐Brand�1%�

SEM‐Other�6%�

Email�6%�

Other�Online�26%�

Appliances�

Product Purchase Propensity

• Marketing Databases with order detail contain a wealth of predictive value– Note that product

categorization useful for marketing is often not the same as categorization used by merchants

9/23/2013

15

Order Value Analysis• Replace “Average Order” with Order Value profiles for added

insight• Averages don’t tell the story• Compare different media

Putting It Together

9/23/2013

16

Top Down Reporting• Create a high level marketing dashboard relevant to your business• Detail can be provided in a series of supporting reports, as needed

Campaign Support

• Classic Predictive Response – Recency, Frequency, Monetary– Simple Bracketing– Model Scoring

• Order Method – Store, Website, Call Center– Less predictive for direct channels than in the past

• Media Responsiveness– Predictive, but hard to measure– Test to validate measurement criteria

• Product Preferences– Categorization should be customer‐driven– Often not the same categories as merchants use

9/23/2013

17

Reporting and Tools• Business Intelligence – Classic Approach

– Business Objects– Cognos– SAS

• Analysis Tools –Interactive ApproachTableau

Tips

• Remember, tailor your reporting and target metrics to your business

• Behavioral science requires both qualitative and quantitative analysis

• Don’t be afraid to apply an 80:20 solution –remember opportunity cost

• Start at the top and then drill down– When stuck go up a level

• When presenting to management, start minimalist • Transactional costs not reported through your

marketing database can be estimated from a P&L and added as a cost per order

9/23/2013

18

The Future• Understanding the full consumer picture – moving from unknown to known behaviors

Acquire Engage Convert Maximize

Clicked on a handbag

display ad

Searched for shoes

Browsed shoes, pants

High-value shoe content /

offer

Email cross-sell pants, handbag

Target site content pants,

handbag

Purchased shoes Purchased

handbag

Loyalty club invite – high-value offer

• Display click: pants• Search on shoes• Site visit: shoes, pants• Displaying high-value

browsing behavior

• Interest in shoes, pants, handbags

• Purchased shoes• Email & location• High value

Questions and Answers

Competitive advantage in the future will live in how effectively an organization can understand,

track, engage, measure & influence consumer behavior.

Al BessinPrincipal, Bessin [email protected]

9/23/2013

1

Modeling & AnalyticsDatabase Marketing Intensive Part 5

Steven C. KimVP, Quantitative Marketing Group

Merkle

How can you turn your kid into a world‐class footballer?

9/23/2013

2

Analytics defined……the extensive use data, statistical and quantitative

analysis, explanatory and predictive models, and fact‐based management to drive decisions and actions. The analytics may be input for human decisions or may drive fully automated decisions. Analytics are a subset of what has come to be called business intelligence: a set of technologies and processes that use data to understand and analyze business performance (Davenport, 2007).

There are six core CRM capabilities that must be mastered in order to create sustainable competitive advantage

Insights Interactions Measurement Optimization

Segmentationenterprise | behavioral |

needs | lifestage | affluence

Predictive Modelingregression | latent class |

bayesian | neural networks

Attributionmatchback | cross-channel

| probabilistic | digital

Marketing Mixmedia data | mix models |

media plans

Customer Valuelifetime value |

engagement | social influence

Personalizationoffer & content arbitration |

recommender systems

Test & Learnexperimental design |

testing roadmaps | controls

Forecastingtime-series | simulation |

scenario planning

Customer Lifecycledefinitions | historical

analysis | extrapolations

Contact Strategytiming models | investment

decisions | cadence

Reporting & BIdashboards | visualization | diagnostics & benchmarks

Optimizationgoal maximization | optimized scenarios C

ore

Analy

tic P

illa

rsCore

Analy

tic P

illa

rs

Information

Customer Profilingdescriptive comparisons |

opportunity analysis

Data Miningexploratory analysis | text

mining | visualization

Researchmarket research | conjoint |

hierarchical value maps

Agility

Competitionmarket sizing | share of wallet | share of voice

Organizationalgoal alignment | decision

making requirements

Improvementlearning roadmaps |

business case development

9/23/2013

3

So what is a model?

OR

Or this?

OR

9/23/2013

4

Modeling defined……a formalization of relationships between variables

in the form of mathematical equations. A model describes how one or more random variables are related to one or more other variables.

In other words, we take a bunch of data, do some complex math, in order to predict some cool stuff

Inputs Modeling Outputs

Data Math Prediction

Price Regression Model Sales

Web Activity Logistic Regression Churn

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5

Regression Model

Total Sales = w + bSE

TV GRPs

Sales

w

b

1 unit

1. If there is no TV

investment, you still have

“W” sales

2. The slope of the

relationship is the

coefficient that predicts

“b”. Or said another way,

it’s the increase in sales

due to an additional unit

of sponsorship spend

3. Coefficient also predicts

the decrease in sales

due to decreased

sponsorship spend

So how can you predict box office receipts?

GrossCostROI

$1.2 Billion$200 Million$1 Billion

$243 Million$225 Million$18 Million

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6

Neural Network Model

Source: R. Sharda, D. Delen / Expert Systems with Applications 30 (2006) 243–254

What have you predicted already and what do you want to predict going forward?

Inputs Modeling Outputs

Data Math Prediction

? ?Table

Discussion

9/23/2013

7

The Case for Customer Centricity

Q1

The Case for Customer Centricity

Q1

Customer Segmentation as the basis for

Customer Strategy

Incremental Measurement to drive Decision

MakingAlignment on Customer Value

Source: Big Data’s Biggest Role Aligning The CMO & CIO Greater Partnership Drives Enterprise‐Wide Customer Centricity March 2013

9/23/2013

8

Why It’s Hard

What is Currency?

Currency, n. (pl. –cies)

1. Medium of exchange that is in current use in a particular country

2. General acceptance or circulation; prevalence

9/23/2013

9

What is Customer Currency?

The Role of Customer Currency

A common definition of the customer is in order to define the desired market and a differentiated value proposition to those customers

Segmentation

A common methodology that defines the value of a customer in order to be used for measurementand investment decisions

Customer Value

A system to determine the true return and impact of an event or action in order to drive fact‐based decision making

Incremental Measurement

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10

Why Customer Value?

“If you can't sort out your customers ‐‐ if you can't look at them and know who is good and who is bad ‐‐ then you can't be customer centric. That's step one.”

Peter FaderWharton Marketing Professor

Customer Value Phenomenon

20%

40%

60%

80%

100%

120%

20% 40% 60% 80% 100%

~20% of customers drive 80% of

value

~85% of customers contribute positively to aggregate value

~15% of customers are value detractors

High Value

Medium toLow Value

Value Detractors

cumulative % of p

rofits

9/23/2013

11

Customer Value Example

Value Tier% of

Customers% of

Revenue% of

Margin

Rev /Customer (Index)

Mar / Customer(Index)

Diamond 5% 29% 32% 572 644

Platinum 15% 36% 39% 237 262

Gold 60% 36% 35% 56 59

Silver 15% 1% 1% 7 5

Bronze 5% 1% ‐8% 25 ‐155

Total 100% 100% 100% 100 100

Prioritization of customers to invest in versus those to minimize spend

Customer Value Model

Customer Value

MonetaryValue

EngagementValue= +

Logical components of the customer value equation

+ MonetaryValue

EngagementValue+

Life‐to‐Date Expected Future

DeterministicInclude direct revenue and costs of servicing client

PredictedModeled based on factors such

as prior behavior

Monetary Value: Variable revenues and costs associated with a customerEngagement Value: Non‐monetary customer interactions with the brand

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12

Customer Value Applications

Previous View of NPS

NPS through Customer Value

Understanding the value of each customer and differentiating marketing, sales, and service experience accordingly, can ideally grow individual current and future potential value of each customer to the individually optimal level

Customer Value ApplicationsAffiliat

e

Index Compared to Overall

4 Star 3 Star 2 Star 1 Star 0 Star

#1 143 156 157 120 39

#2 167 146 132 106 47

#3 129 120 103 91 86

#4 108 120 119 96 86

#5 117 98 98 97 96

#6 98 92 92 96 108

#7 81 98 98 93 112

#8 75 79 93 97 119

#9 70 71 76 97 129

#10 62 67 74 90 138

• Affiliates drove a significant number of online transactions and orders

• Lack of insight and consistent measurement of affiliate performance

• Applied customer value lens to the actual affiliate performance

• Top affiliates bring in five times as many 4‐Stars and one‐fifth as many 0‐Stars as the bottom affiliates

9/23/2013

13

Segmentation Role

CUSTOMER CENTRICITY

CustomerSegmentation

CustomerStrategy

StrategyExecution

segmentbrief

customerinitiatives

How to Segment?

Behavioral(what are my customers

doing)

Attitudinal(understand how my

customers feel)

Motivational(understand WHY my customers make

decisions to engage)

9/23/2013

14

Means End TheoryMeans‐End Theory proposes consumers make purchasing decisions not simply because of the product features, but more specifically how those features provide benefits that get them to deeply seated personal goals

Most research does not address the Emotional & Personal Values layer and thus cannot create personal relevance bridges between product attributes and personal values

The things directly associated with the product or brandExample: Dropped call guarantees from mobile provider

The positive aspects of the attributes, why they are importantExample: Call quality and service

The emotional benefits of the functional benefitsExample: Good cell service makes me feel safe

The personal values why those emotions are important to us Example: Safety links to my value of family security Personal Values

EmotionsPsychosocial Consequences

BenefitsFunctional Consequences

Brand / Product Attributes

MAVEN Approach

Personal Values

EmotionsPsychosocial Consequences

BenefitsFunctional Consequences

Brand / Product Attributes

MappingAttitudes Values &EmotionalNeeds

Applying means‐end theory to segmentation enables us to first identify the personal relevance bridges of motivational drivers of consumer choice

Segmentation then focuses on splitting based on motivational drivers, not simply our attitudes or behaviors with a brand – WHY we do what we do

Segment A

Segment B

9/23/2013

15

Consumer Decision Making

EmotionsPsychosocialConsequences

BenefitsFunctionalConsequences

Brand / ProductAttributes

Personal ValuesSelf‐Esteem

Personal Accomplishment

CompetitiveAdvantage

Earn More Money

MoreProductive

Real‐Time Recordof Package Handling

Get PromotedSaves Timeand Effort

Makes MeLook GoodConvenient

Drop Box Package TrackingSoftware

Reliable

On‐Time Delivery

Not Responsible for Someone Else’s Error

Less Worry about Unknowns

Peace of Mind

Personal Control

The optimal segmentation brings primary research and database together

Meet… Darby Susan Angela Christopher

DemographicsYounger

Upper‐mid incomeMature

Upper‐mid incomeYoung/middle age

Mid incomeYoung/middle ageLower‐mid income

Motivated by

• Tech savvy, early adopter

• Status & recognition

•Driven, risk taker

• Personal pride

•Decision‐maker in family

• Researcher, knowledgeable

• Family oriented

• Child‐focused lifestyle

• Price sensitive

•Means to an end

• Recent graduate

• Price conscious

Shopping Behavior • Shops at Nordstrom • Shops online at Amazon • Shops at Target • Shops at Walmart

Young Trendsetter

Very Rich Actionable

Primary Research• View of US• Attitudes, Behaviors, Psychographics• Brand Relationships, and Sentiment

Mapping• Tie to each individuals in US• Demographics• Customer Behaviors• Syndicated Data

Time Savvy Mom ThrifterEnlightened Consumer

9/23/2013

16

Segment Brief

PRODUCT PRICE CHANNEL MEDIA

Mobile phone accessories

Ahead of the curve

Exclusive with value

Increased product exclusivity while

retaining relatively low price points

Social media, email, message boards

Sharing product information, getting feedback from peers

$29M5% Direct mail52% Email

14% TV/radio29% Social media

32% share | Customer Loyalty: 90 | Up Sell: 160 | Retention: 85

Increase social media exposure (sharing = additional unpaid advertisement)Lower in‐store prices slightly for increased value proposition

20% increase in in‐store traffic= 150% increase in revenueImpacts other segments through word of mouth, aspiration

PERFORMANCE

ACTION PLAN

FINANCIAL IMPACT

Young Trendsetter

Monetizing SegmentationDetermine where investments should be made

ROIC

EBITDA

Capital Charge

Marketing Expenses

Operating Expenses

Revenue

Other Expenses

Basket Size

Invested Capital

# of Trips

Cost of Capital

Number of Trips

Cost per trip

Promo Dollars

COGS

Fixed

Working

Business Case FrameworkOpportunity: Increase

Segment Traffic

Incremental $48 million LTV

‐

‐‐

x

x

x

Assumptions• Average LTV: $1,000 • Discount rate: 11%• In store conversion: 58%• Segment mix from 32% 50%

32% 50%

9/23/2013

17

Got ROI?

9/23/2013

18

Incremental Measurement• Incremental measurement is the evaluation of changes in

KPIs given a decision or event• An Incremental Metric is the difference between the KPI

given you made a decision minus the KPI had you not made that decision

• Why Incremental?• Asking what benefit would be realized from a decision applies

universally, whether talking about a choice of media, what new product to launch, or whether an ecommerce site should be launched

• When combined with a standard metric of customer value any decision within an organization can be evaluated based on the question, “If we did this, what would be the impact on total customer value?”

The Fundamentals

Predictive model based creation of baseline measures

Scientific based experimental testing using controls

There are really only two ways to measure things and both involve the creation of a baseline metric to

measure against to determine what would’ve happened

ObservedExperiments

PredictiveEstimation

9/23/2013

19

Observed Experimentation

Desired Learning

A|B Test

Multivariate Test

Single Factor

Multiple Factors

Analysis MethodTesting Type Benefits & Uses

Creation of single control group for two way comparisons

Design of experiments enabling measurement of multiple factors or testing elements at once

T‐Test

Multivariate Analysis(anova / ancova)

• Proper test and learn strategies require a statistical based approach to testing design

• Effectively there are two types of scientific tests:

Predictive EstimationThe use of predictive modeling for measurement purposes typically involved

econometric‐based modeling methods, generally forecasting and mix modeling

0

20

40

60

80

100

120

1 2 3 4 5 6 7 8 9 10 11 12

Sales Vo

lume

Time Periods

Actual Baseline

Measurement

Area betweenthe lines is incremental

predicted

actual

9/23/2013

20

Holy Grail of Marketing• From a marketing perspective, the greatest application

for incremental measurement is attribution• True attribution of marketing tactics is not simple as

return on marketing is a function of two dimensions:• The ability to properly allocate media dollars by vehicle

type• The ability to properly target and personalize that media

dollar into relevant experiences • The ability to incrementally measure both of the above

requires both observed and predictive methodologies

True attribution is not easy

First/Last Click Ap

proach

Advanced

Analytic

App

roach

The customer purchase path has become more complex

Data proliferation has made data mining increasingly difficult

Organizations lack support to drive deeper marketing insights

Source: Forrester Research “Cross‐channel attribution presents a clear path to marketing ROI” September 20, 2012

9/23/2013

21

Attribution defined…

TV viewDirect mail Newspaper view Display view Social visitWebsite visit Paid search click

Mass and Offline Digital

Day 8‐30 Day 1‐7 Day 0‐1

New Customer

Actual experience $

0% 100%

Credit over applied to bottom of funnel touches; other touches often ‘invisible’Creates flawed financial view of performance

Direct or Rules Based

3% 14% 3% 5% 5% 5% 15% 5% 5% 40%Modeled

Model‐adjusted interaction

Most accurate and actionable

…assess media performance by measuring the incremental impact of each marketing activity

How to do it

National media (TV & radio)$140

Local media (TV & radio)$200

Direct mail$180

Digital$83

Cost per inquiry by tactic

Top‐down media mix model(Traditional media mix model: DMA by week level, 12+ months of data)

Calibration layerBottom‐up customer modeling

(Consumer level, machine learning algorithm, 6+ weeks of data

Display/ Video$60

Email$80

Paid Search$91

Social$113

Campaign $

Cost per inquiry by tactic

Direct mail$180

Placement $

Creative $

Digital AttributionAnonymous (Cookie Level)

Direct AttributionPII (Email, Physical Address)+

@

Network $

Program $

Creative $

Segment $

Engine $

Branded $

Keyword Segment $

Campaign $

Program $

Campaign $

Segment $

Creative $

Program $

Campaign $

Offer $

Creative $

9/23/2013

22

Calculate your own ROI

Assess how you are currently measuring ROI within and across all marketing channelsStart the conversation with your organization

9/23/2013

1

NAVIGATING THE DATA MAZE

Database Marketing Post Intensive: Part 6

Presented by:Joanne Branscum & Doug Christiansen

Acxiom Global Consulting

Leveraging the ability to collect, connect, analyze & act on massive amounts of messy data to make money (or do good)

About Acxiom

• Acxiom is an enterprise data, analytics and software as a service company that uniquely fuses trust, experience and scale to fuel data‐driven results

• For over 40 years, Acxiom has been an innovator in harnessing the most important sources and uses of data to strengthen connections between people, businesses and their partners

• Utilizing a channel and media neutral approach, we leverage cutting‐edge, data‐oriented products and services to maximize customer value

• Every week, Acxiom powers more than a trillion transactions that enable better living for people and better results for our 7,000+ global clients

9/23/2013

2

Roadmap For The Discussion

The Definition Of Big Data

Challenges and Opportunities

Group ParticipationDiscussion

Data is becoming the new rawmaterial of business: an economicinput almost on a par with capital andlabor.

“Every day I wake up and ask, ‘howcan I flow data better, manage databetter, analyze data better?”

Rollin Ford – CIO of Wal‐Mart.

9/23/2013

3

What Is Big Data? How 6 Blind Men Describe an Elephant

The REAL Definition of “Big Data”

Big Data is about the abilityto collect, connect, analyze,and act on massive amountsof messy data to makemoney (or do good)

9/23/2013

4

DRIVERS

FEATURES

What’s Driving Big Data

BIG DATA

NON‐TRADITIONAL DATA TYPES

DATA VOLUMES

DATA SOURCES

TOOLS AND TECHNOLOGIES

STORAGE AND COMPUTE ECONOMICS

BUSINESS INSIGHTS AND OPPORTUNITIES

It’s not about technology

The business opportunities drive the data

It’s about making the data usable

You have to have a plan

It is not a once and done project

Our View On Big Data

You’ve been doing ‘big data’‐‐ it’s just gotten more interesting

Change takes time

9/23/2013

5

The Components of Big Data

CONNECTTHE INFORMATION

ANALYZETO CREATE INSIGHTS

ACTTO DRIVE VALUE

COLLECTTHE DATA

ORGANIZETO INITIATE ACTION





9/23/2013

6

Acxiom’s Big Data Capabilities Model

Dimensions (Distinctive feature of a capability)

Capabilities (Ability to perform strategic actions)

CONNECTTHE INFORMATION

ANALYZETO CREATE INSIGHTS

ACTTO DRIVE VALUE

COLLECTTHE DATA

ORGANIZETO INITIATE ACTION

People and Places

Products and Services

Interactions and Outcomes

Anonymous Entities to Known Individuals

Business LevelAlgorithms

People ResourceCapabilities

Analytic Processes

Tools and Technology

Consumer Decision Process

Preferences and Propensities

Channel Agnostic Delivery

Optimized Marketing Actions

1.1

1.2

1.3

Privacy and Data Usage Rights

1.4

2.1

Data Stewardship and Governance

InformationSupply Chain

2.2

InformationAssets and Flows

2.3

3.1

3.2

3.3

3.4

4.1

4.2

4.3

4.4

Organizational Alignment

Strategic Clarity

Organizational Change Quotient

Innovation to Action

5.1

5.2

5.3

5.42.4

Group Participation Discussion

9/23/2013

7





Harnessing large and diverse data sets

Navigating privacy concerns

Making the data actionable

Bridging digital and traditional sides of the business

Known and anonymous data linkage

Key Big Data Challenges

Noise – “needle in the haystack”

Dealing with all the partners and changing ecosystems

9/23/2013

8

Guardrails aren’t intended to stop us from driving, just prevent us from going over the edge…

…the same applies to privacy compliance and Big Data.

Newer Sources of Data Are Changing The Landscape

Imagine The PossibilitiesExperiment beyond the current scale

Shorten the learning cycles

Improve the consumer experience

Create a “frictionless” experience

Optimize marketing spend & decision making

Innovate new business models, products & services

9/23/2013

9

Have a Plan!Change the Game!

Big Data, Big Deal

Better Connections. Better Results.

By: Jed Mole, David McKee and Ian Fremaux

Five steps to identifying the most valuable 5% of your data — and making Big Data a Big Deal for your business

An Acxiom White Paper

1

IntroductionThe premise of this white paper is: accept Big Data is real; but don’t just believe the hype, focus on the desired short- and long-term results and work back from those objectives. It also sets out a five-step plan that will help marketers and brands generate real competitive advantage from Big Data.

What is Big Data? To paraphrase an old advertising adage1 modern marketers could be forgiven for regularly thinking, “I know up to 95% of data is of little value to me; I just don’t know how to find the most valuable 5%.”

Data has been getting exponentially bigger in terms of rapidly increasing volumes for many years, but Big Data has emerged as a buzzword relatively recently. So what’s changed?

When the term Big Data is used today, it generally refers to data that has one or more of three commonly accepted attributes. Some analysts and organisations add others but the accepted three are: Volume, Velocity and Variability.

Volume is the most obvious of the “three Vs” but also the most deceptive. Of course, there is an exponential growth in volume caused by the consumer increasingly emailing, surfing, tweeting, blogging and the like but how much of it is real and interesting? How many of the estimated 4.5 petabytes of videos added online in 2011 (see diagram) were either (a) relevant to the marketer or (b) copies? The fact the individual has copied it may be of interest to the marketer but it’s not new content. Big volume is not new in other fields. The largest single coherent data store in the world is the World Data Centre for Climate at 220 petabytes. It is not volume alone which is changing the world, but volume combined with the other two Vs.

Velocity is a key driver of change in approach brought about by Big Data; the essence of this is that the more interesting behavioural data (think product search) has a limited shelf life and must be acted upon quickly.

Variability of data is unprecedented and a real game changer. Some is transient, some structured and an enormous amount now unstructured yet incredibly powerful. There is a great challenge in taking raw data from the huge variety of possible sources, integrating these into systems and producing actionable insights which feed operational systems.

1Depending on which source you believe, this line was originally used by US retail guru John Wanamaker or British household goods magnate Lord Leverhulme: “I know half of my advertising budget is wasted — I just don’t know which half.”

Jun-

07

Aug

-07

Oct

-07

Dec

-07

Feb-

08

Apr

-08

Jun-

08

Aug

-08

Oct

-08

Dec

-08

Feb-

09

Apr

-09

Jun-

09

Aug

-09

Oct

-09

Jun-

10

Aug

-10

Oct

-10

Dec

-09

Feb-

10

Apr

-10

40

Hours of video

35

30

25

20

15

10

5

0

Source: YouTube Global

HOURS OF VIDEO UPLOADED PER MINUTE

2

Big Data, Big Deal

5 exabytes of data were created between the dawn of civilisation and 2003 — that much

information now becomes available every two days.

As Big Data trends ever higher as a topic, it is of concern that many of the recommended solutions involve enormous and urgent investments in IT; the idea being that if you can capture and store all of the data then you stand a chance of generating value from it. The opinion of this white paper is that this is not the answer for all organisations, rather a more efficient solution lies in every marketer asking “so what?” and first understanding how relevant Big Data is to them, how much it can support their business goals and then how to turn potential into reality.

So why should marketers care? The fact is the explosion of data has been caused by the aforementioned changes in consumer behaviour and consumption; the way we shop, work and relax. The growth in numbers and kinds of channels, devices such as Smartphones and all the real-time data they provide through Apps and social networking, products and services has led to Big Data. Consumers have created it, the same people who buy goods and services from the world’s brands. Marketers absolutely must care — this is their space.

Most professionals using data with privacy as a design principle to understand and engage consumers accept a great deal of data is of limited worth. The problem is how to separate the signal from the noise. Somewhere within the avalanche of data are buried significant patterns and behaviours which signpost buying, churn, brand support or aversion. A successful strategy needs to have a mechanism for uncovering these patterns and signals and adapt the operational systems to respond to them.

To achieve the end goal of making their brands more successful by better understanding, targeting, engaging and serving consumers using Big Data, marketers should consider the analogy of human vision which features both ‘focused’ and ‘peripheral’ abilities. We walk down the forest track looking at the path ahead, not seeing every leaf around us, but when we sense movement our eyes immediately turn to the source and respond to the threat or opportunity. Marketers need to create the ability to achieve exactly this, to manage signals but continuously be aware of new signals within the noise and react appropriately.

In the face of much hyperbole around Big Data, marketers and brands first need to avoid panic responses — new trends will always bring big statistics. Instead, think differently about the multidimensional insight Big Data affords. Many businesses have already proven they are excellent at using what consumer insights they have. But could even Apple say it’s driving the maximum possible value from customer data across all its devices and consumer touchpoints? The challenge is going from Big Data potential to the reality of improved business results.

If harnessed properly, all kinds of data can be used to improve a brand’s ability to win, service, keep and grow customers. For example, customer service or product usage data may not traditionally be termed marketing, but in tomorrow’s world, the marketer must want to bring this data to bear having identified its previously unimagined potential value.

Marketing is perfectly placed to make Big Data a business imperative that can serve the entire organisation, not just its own function or the consumer. The modern-day volume, velocity and variability and greater need for validity of data can be managed to make clear the benefits of Big Data far beyond the walls of the marketing department.

The starting point isn’t always clear. However, by implementing the following five-step plan, you will generate quick wins and put the business on a strategic path to better results by making Big Data deliver significant, measurable benefits.

Big Data, Big Deal

3

Step 1 Put the Consumer First Agree the consumer-related business problems you’re trying to address with Big Data

Every marketer knows that for a brand to be successful, it has to have a compelling offer for consumers delivered via the right channel at the right time: the classic Four Ps and the expanded Seven Ps of services marketing. Big Data does not change this, but creates new opportunities. Data can be used to enhance the product, improve the price and make the promotion far more relevant to the place.

One of the challenges is deciding where to begin. Some big organisations have started by loading their largest datasets into emerging technology platforms that solve the volume and variability challenges for their analysts, which allows them to mine these super-size datasets for new insights. Although this is a great way of learning the technology it only provides part of the information you need to define a Big Data roadmap.

The first step of a journey into the fast-moving world of Big Data should be to ensure that you are

aligning your efforts to your business objectives.

So, step one is to identify some of the key issues and opportunities that affect your target market today and their relationship with your brand. A brainstorming workshop involving a range of business stakeholders can be used to establish a list of up to 10 priority items to be addressed. This workshop should ensure the business’s strategic goals and key initiatives are considered, taking care to identify which involve the consumer and brand and have the potential to be affected by Big Data. Also, a mixed group of stakeholders will provide ideas for this list from people with different perspectives of your consumers and how you can better align your products and services to their needs.

An estimated incremental revenue or cost saving should be associated with each item on the list. This ROI estimate only needs to be approximate and shouldn’t require an in-depth period of analysis.

At this stage it is not necessary to predict whether the challenges identified can be resolved through a Big Data initiative — that will come later. The tangible deliverable from step one is a list documented in the form of a project brief, with a problem statement describing each item on the list and an impact statement to demonstrate the potential value to the business.

4

Big Data, Big Deal

Step 2 Define the Starting Line Audit the data you have and identify the data you need

In addition to defining the problems and opportunities that Big Data could impact, it is also necessary to have a good view of what Big Data your organisation has. The way to approach this task is by conducting a consumer-centric data audit.

Who should be involved in the process? Outside marketing, IT should definitely play a part as the department is likely to be most at home with data, and may be able to help uncover silos of data unknown to the rest of the team. Data analysts should also be involved as they are already familiar with combining and using disparate data sources and will ask the right questions of the data owners when compiling the data audit. The privacy team should also be engaged to ensure the boundaries of data usage are properly respected.

The audit should create a register of all the touchpoints consumers have with your brand and, for each, should ask questions including:• Whatdataisgenerated• Isitcapturedanywhere• Doyouhaveaccesstoit• Doyouhavepermissiontouseit• Doesanydocumentationexistaboutthedata’sstructureandcontent• Isitstructured,semi-structuredorunstructured• CanIjoinittoanyothercustomerdatasources• CanIgetasnapshotofthisdataforuseinaproofofconcept?

As recently as five years ago, Acxiom saw an average of 15 to 25 data sources for complex marketing solutions. Now, even mid-tier companies are arriving with 50 to 100 data silos which require integration.

Since the advent of social media it is even possible to start capturing information about what your consumers think of your indirect marketing campaigns.

The next step is to collate more detailed information about the sources you can access. Hopefully for some of these you’ll have lots of information, but others may be reliant upon key expert users, and some will be relatively unknown. At this stage you should aim to obtain a profile of the main data entities and attributes from each system — the ‘data dictionary’ — which includes the spread of values and their frequencies for the attributes. Software tools can be used to automate some of this work. This process will highlight where data generated at a consumer touchpoint is unavailable. Filling in these data gaps is something that could be included in the strategic roadmap later in the process at step five.

Each data source should be scored on a number of measures that can be used to assess whether it is a valuable source for inclusion in a proof of concept. For example, your web analytics data might score high on granularity of information available but low on completeness if you can only access the last few days’ worth.

The final thing to include is data sources that you would need to have a better understanding about your customers and prospects.

The specific deliverable from step two is production of a comprehensive list of the raw materials you have available for any Big Data initiatives and identification of the gaps where the data is unavailable.

Big Data, Big Deal

5

Step 3Create ‘Plan A’ — A Proportionate ResponsePlan to test and invest, proportional to the strength of your analysis and findings

Having understood the objectives and the current landscape in steps one and two you must now build a strategy for managing and making Big Data actionable — a ‘Plan A’. The challenges here will be familiar if you’ve ‘sweated blood’ building your corporate data warehouse, but are magnified in the context of Big Data by the volumes and variety of data involved. Furthermore, the velocity factor means that unless the data gets processed and actioned rapidly then the data quickly becomes stale.

Throwing all of the data into a new platform like a large Hadoop cluster is not really going to solve the problem in isolation. Yes, you are going to need some hardware and processing power, but a strategy for doing something useful with raw data is critical. Enlisting the skills of an entrepreneurial senior analyst at this stage could prove vital.

Solutions will vary from brand to brand but any Big Data plan must address how to:

• rapidlyon-boardnewdatasources

• analyserawdatatodeterminewhenandhowitcanbeused

• makedataoperationalquicklyandefficiently

• identify,andifnecessarydiscard,junkdatawhichwillclogthesystem

• automatedecisioningtoaccommodatethevarietyandvelocityofBigData

• ensureallactivityisexecutedinaprivacy-compliantmanner.

Consider the enormous volumes of website tracking information generated by a large consumer site. Some facts will be useful, if only for a short time. For example, a recommendation engine may take a real-time feed of product searches to determine which adverts to display to users; an abandoned basket is a trigger for retargeting. The problem is much of this data is just noise. However, data mining can identify patterns or activities which are predictive of some desired behaviour and once the pattern is identified the data can be actioned by using it to feed the campaign or decision system.

It is the agility in the face of an everchanging data landscape, and not just volumes, which distinguishes

Big Data solutions from previous incarnations.

By bringing together the learnings from steps 1 and 2 with an appreciation of the possibilities offered by the new technologies, the specific deliverable from this step should be a practical, initial vision and plan of how Big Data can work for you and your organisation.

Traditionally, this is the beginning of the tender or RFP stage where firm decisions are made about hardware, software and service providers. However, given the scale of investment, and to ensure ‘the Emperor really is wearing some clothes’, the next step is a Big Data reality check.

6

Big Data, Big Deal

STEP 4Test Big DataDeploy a proof of concept to test and learn

Clearly there may be a significant investment required to establish a Big Data environment, not just in terms of hardware but also in terms of skills. Demonstrating ROI is therefore mandatory in securing sponsorship from the business.

One of the paradoxes of Big Data is that many of the use cases do not actually require Big Data. The volumes are so large that the decisioning necessarily becomes simplified. With the exception of some parts of the automated decisioning aspect, there is nothing fundamentally new about Big Data. It’s just a new mindset that’s required.

What this means is that it should be perfectly possible to conceive of and execute Big Data use cases without building

a full-blown Big Data environment — especially for a proof of concept.

It’s fair to say that if you can’t demonstrate a handful of use cases from a marketing viewpoint, then it’s unlikely that any Big Data environment will pay its way. Consider the SETI@home project (see below). It was necessary to prove the model worked first with a small number of networked computers in a lab before upscaling to cover 3 million computers worldwide.

The resources and effort required to prove a use case will vary. In some cases smaller volumes of static data may suffice, in others production-scale volume may be required. A partner with an existing large-scale analysis environment may be able to help you look for the early wins which will justify the business spend.The objectives of step four include:• establishingwhichdatasourcesofferuseful,predictiveinformation.Thiscanbedonewithstatic physicaldumpsoflikelysources(sales,web-logs,callcentreetc)

• establishingwhichexternalsourcesarerequiredandmayneedtobebroughtintothemix (socio-orgeo-demographicdata)

• providingmetricsforanypredicteduplift,basedonextrapolationfromtheproofofconcept• manuallyexecutingtheintegrationsteps—whichmustbeautomatedinliveenvironments— sotheyareunderstoodandconformtoprivacyregulations.

Typically, the activities at this stage will include statistical analysis (mining), searching for predictive patterns within the data and then attempting to turn these into processes which can be tested in real-world scenarios. Once again, this is where third parties can help. A good external partner should be able to provide resources such as data scientists and technologists with a grasp of the new software and platforms, to fast-track learning at this stage.

Moving forward from this step, the specific deliverable should be a solid body of evidence to support the business case, and a clear understanding of the resources and processes required to underpin it. You ought to be in a position to present ROI cases to leadership, and to begin validating and adapting ‘Plan A’ from step 3, producing a strategy for Big Data that will be delivered through a practical roadmap.

SETI@home was the second large-scale distributed computer project set up in the 1990s (Berkeley University & US Space Labs). It had the following objectives:• todousefulscientificworkbysupportinganobservationalanalysistodetect

intelligent life outside Earth, and• toprovetheviabilityandpracticalityofthe‘volunteercomputing’conceptand

conform to privacy regulations.Whilst the first objective has not yet been met the second was an almost complete success — demonstrating how home computers could be used to process huge amounts of data in a collaborative environment. SETI is one of the fathers of cloud computing.

Big Data, Big Deal

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Step 5Emulate Human Vision — Focused and PeripheralCreate a roadmap to operationalise proven approaches, and identify and test new ones

Finally, you are ready to build on the results of the proof of concept. As previously described, the ideal state is to achieve both focused and peripheral vision: the ability to focus on the data that matters most while being aware of other data, and be constantly on the lookout for new or previously unavailable data.

Step five is about creating a strategic roadmap. This will outline how any proof of concept tests can be operationalised, part of ‘business as usual’ and how the organisation will move forward with subsequent proofs of concept already defined. It will also implement your peripheral vision.

Steps three and four will hopefully have armed you with information to build the business case to move forward; an incremental return on marketing investment with well-managed risk should have the business’s budget holders happy to ask: what’s next?

The effort required to make progress should not be underestimated but likewise needn’t be a cause for alarm. While Big Data can be ephemeral and difficult to tackle head-on and while the business will need to invest to make Big Data proof of concepts operational, the fact remains that the majority of what the marketer is trying to achieve, is getting the right data in the right place at the right time.

The quickest, and arguably best, return will almost certainly come from a marketer’s ability to bring high-value data to

bear in existing marketing systems and programmes.

To this end, priority consideration should be given to what Big Data can most quickly be captured and translated into these existing environments. You need to conduct a value analysis and prioritise. For example, compare the investment in the capability to take unstructured data and turn it into large volumes of structured insight against the expenditure needed to capture a more modest volume of structured data. The yield of each option must be identified and the answer may be to do both. It is worth remembering that much of this capability can be implemented between the consumer-generated Big Data and the existing systems without the need to completely rebuild existing architectures.

It should be clear by this stage that the five-step approach offered is one that allows businesses to avoid a crippling ‘knee-jerk’ investment in reaction to the opportunity and threat Big Data represents. A proportional response that takes practical, informed steps to generate significant results is vital. Once you have a clear roadmap to operationalise the initial proofs of concept and deploy the next batch - the focused vision - attention needs to be given to how the marketer achieves peripheral vision.

The good news is that most of the activities undertaken in steps one to four contain much of what the marketer needs to deliver peripheral vision. You must put in place a programme of continuous activity, indeed for many organisations this should entail a separate team that specialises in scrutinising the evolving Big Data universe to identify and evaluate potentially powerful sources of Big Data. This team is like weather forecasters, always taking readings and measurements, using a range of permanent and ad hoc activities and tools to predict what’s coming next.

The very nature of Big Data means the roadmap for organisational change should always be based on testing and refining. This team needs to consider the potential of the data from an analytical perspective but must also apply pragmatism around the challenge they are going to present to their colleagues tasked with trying to operationalise the data. They should also be able to assess the likely impact from a marketing systems point of view and be capable of framing their findings and recommendations in terms of return on marketing investment.

The key deliverable from the final step is a strategic and tactical plan, including a roadmap that operationalises Big Data trials, generates results, enables learnings and identifies potential opportunities so that marketing, the brand and the consumer continuously benefit from the exploitation of Big Data.

8

Big Data, Big Deal

ConclusionIf Big Data was already present in the past, albeit with its own fast-growing volumes, it was based on what now seem narrow variables such as location, age and salary. These days, the layers and levels are almost limitless; where are people located when they use HTML5 mobile applications, how many brands do they purchase from and through which channels, what do people think and how do they feel?

Big Data is real and represents both an opportunity and a threat. The threat comes in the form of putting prime focus on getting and hosting as much data as possible without regard to how it can be most profitably used. Like the donkey chasing the carrot that’s always out of reach, the pursuit of being able to catch all the data available as it evolves is always likely to result in disappointment.

The opportunity becomes more realistic and tangible once a business shifts from trying to solve all of its problems with all of the data available. Opportunity comes from focusing on what problems Big Data stands the best chance of solving, understanding where the organisation is today, creating a ‘Plan A’, testing and then building a roadmap that gives the marketer both the focused and peripheral vision to make Big Data deliver incremental return on marketing investment.

Given consumers create Big Data and brands need to engage, serve and delight them to be successful, it follows that marketers are best-placed to plot the course towards finding that golden 5% of your Big Data, ensuring it delivers benefits to both the consumers and the brand.

Big Data has been evolving and growing in the background for some time. Now it’s here, can you afford not to take it on? Just make sure you take it on in the right way and on your terms.

Acxiom and Big DataAcxiom can help marketers achieve results by exploiting the potential of Big Data with deep understanding of how to identify the right data and put it to profitable use. Combining marketing and consumer expertise, technology and data, Acxiom provides solutions that are fully compliant with privacy regulations and support the five-step plan, grounded in a ‘proportional response’ approach, and can evidence the benefits with case studies that showcase more than 40 years of data expertise.

Contact detailsFor more information call 020 7526 5265. [email protected]

AC-0309-13 3/13

© 2013 Acxiom Corporation. All rights reserved Acxiom and InfoBase are registered trademarks of Acxiom Corporation. All other trademarks and service marks mentioned herein are property of their respective owners.

17HatfieldsLondonSE18DJacxiom.co.uk

Acxiom Acxiom is an enterprise data, analytics and software as a service company that uniquely fuses trust, experience and scale to fuel data-driven results. For more than 40 years, Acxiom has been an innovator in harnessing the most important sources and uses of data to strengthen connections between people, businesses and their partners. Utilizing a channel and media neutral approach, we leverage cutting-edge, data-oriented products and services to maximize customer value. Every week, Acxiom powers more than a trillion transactions that enable better living for people and better results for our 7,000+ global clients.

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An Acxiom White Paper

At this moment, a number of factors are creating opportunities that will transform marketing: increasingly addressable media, enhanced data aggregation and recognition, and the increased appetite of “C” leaders to drive improved shareholder value and accountability into marketing investments. Direct marketing leaders need to seize the opportunity to define the value of their assets within the new, digitally connected world and communicate this capability within their organization. Collaboration with digital marketers will enable the blending of rich insight and will be indispensable to achieving the multichannel vision shared by more than two-thirds of marketing organizations. Data-savvy marketers are now the cool kids on the block!

addressable media inherently enables the identification and connection of individual marketing stimulus to response,

thereby making it possible to link audience interaction across multiple sources. Examples include: email, home

address, or phone number or mobile number.

Leaders crave multichannel solutions today but they can’t execute68 percent of U.S.-based e-business managers say that their company desires a vision for a consistent, non-fragmented, cross-channel experience, but only 29 percent feel they have the ability to follow through.1 Within the same organizations, data-driven marketers have been investing in the aggregation, delivery and optimization of programs across an alternate set of channels for generations: call center, point of sale, direct mail and others. Globally, most firms are focused on understanding and enhancing multichannel communication to ensure digital and offline communication support and improve one another in order to provide optimized customer engagement, but challenges remain.

Similar to the dawn of household-level direct marketing which became addressable in the 1980s, digital media holds a similar promise of ushering new, bigger marketing opportunities into multiple channels and should be seen as a similar enabler of direct marketing principles. These new digital channels are becoming increasingly addressable, the sweet spot of direct marketers.

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Direct marketing systems optimize marketing investment, regardless of channelAs mentioned, direct marketers already operate in a multichannel world. While the channels may not be directly comparable to digital, the concepts of identification, recognition and engagement are consistent and, in many cases, already in place. What is compelling is the ability of these systems to deliver results in a responsible, predictable and cost-effective manner. With nearly three times the channel spend ($45B) compared to digital channels ($17B),2 and with per-impression costs that are dramatically high, direct marketers have demonstrated a strong competence to proactively identify high-value audiences, measure stimulus to response and optimize programs to a high performance standard. For example, the DMA reported in 2010 that catalog efforts delivered an order at an average cost of $75.32, compared to paid search costs of $99.47 per order.3

This isn’t a point to refute digital marketing. Rather, it is an opportunity to demonstrate the effectiveness, predictability and efficiency of direct marketing systems to operate in a multichannel world. If this scenario sounds familiar and is important in your organization, how then can you repurpose and reposition direct marketing assets so that others in the organization will understand and begin to use them with addressable, digital media?

How to position your direct marketing assetsData-driven marketers should evangelize their assets as necessary to ensure their target audience will have a seamless, comprehensive brand experience and to help shape perceptions, expectations and usage of their direct marketing assets in the new, digitally-connected world. Digital marketing IS direct marketing. What was old is new again!

In order for your organization to succeed in the new digitally connected world, you must; Communicate the value of the data insight present in your marketing database — Your team has the data on prospects and customers and the ability to leverage that insight to deliver, measure and optimize highly relevant communications. Digital marketing efforts will need these assets and capabilities to successfully execute a multichannel customer engagement vision.

Advocate the value of adjoining this data with other sources of valuable insight — Combining data found in digital channels leads to better focused, more intelligent marketing efforts across all channels. Examine the data and response within email, web logs and analytics, cookies, customer support, surveys, coupons and social interactions (if accessible). Completing an accurate picture of customer and prospect interaction with your marketing efforts across these channels will enable richer insight, better segmentation and set the table for enabling coordination of messages.

So, how do you take your marketing database to the next phase of utility in the digitally connected marketing world? You’ll want to build a system which acts and reacts much like a central nervous system in that it is a series of synapses that will send and receive signals about customer behavior and is then able to intelligently recalibrate based on what they do or don’t do. The goal is to remember every interaction and learn. This evolved marketing system has broad ambitions — to not only send signals that influence customers, but also to sense behavior and intelligently respond. This has significant technology implications. When customer behavior is influenced or sensed, marketing leaders deploy automated decision technology to optimize the outcome.


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Viewing and transforming your systems for the digital worldBut how does one get started? First off, resist the temptation to embark on a multi-year data warehouse project. This could consume precious cycle time to be in-market with the right solution. In an environment where 37 percent of all advertising is wasted4, this is your opportunity to show incremental gains in the near term. Utilize the data where it resides; call it from its native source.

Assembling all of the piecesAcxiom suggests that you build this “marketing central nervous system” based upon the following three principles and their respective implementation steps.

1. Build a Data Mart — A data mart approach helps to focus on delivering results with an ROI that can be realized in the near term. This will resound internally and focus the conversation on the value your systems already deliver and ensure you can be in-market with a solution in a matter of months.

Consider the following high-level approach to this data mart that will serve as a multichannel marketing central nervous system:

Gain access to the data by building a logical layer to access it from its source. This goes to the heart of direct marketing as a practice — connection of stimulus to response and the recalibration of efforts designed to optimize response. Integrate non-transactional data. The data mart will benefit greatly by having access to information that includes customer discussion threads, blogs, chat, social and other insights that may be helpful to capturing dynamic indexes of customers and customer segments. Integrate your email database with those who have opted in for email communications and connect it with the CRM data.

Augment your database with the right fields to track individuals as well as campaign elements (campaign ID, incentive used/promotional code, etc.). Build templates with pre-filled forms which the user can correct if necessary. This smaller, data mart approach is more adept and leaves ample flexibility to incorporate additional data sets while remaining nimble enough to continue executing campaigns with the data and insight you have.

Use ETL (extract, transform and load) tools when you only care about certain fields, international domains and as you specify the certain data you care about as you bring it in. This makes the path to insight a lot quicker and a lot less expensive.

Plug in your systems that handle analytics, reports, campaigns and marketing execution data. Take all results and lay them out in a visual dashboard to display real-time results. Cleanse and edit the data mart on a regular basis. This will be critical to keeping the system vibrant and meaningful.

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2. Leverage Data Beyond the Data — The stakes are higher; your system will be viewed by all parties managing customer and prospect-touching programs. Integrating data across channels is a priority for trying to amass the insight necessary to deliver upon cross-channel coordination. Also, as a single point of reference, this system will become the source for segmentation and optimization.

While even the world’s largest brands can only capture a sliver of each person’s life, maybe 1 or 2 percent, information is available that can help fill in the gaps of understanding and increase accuracy in identification and segmentation. Sources will include digital channels as well as third-party insight you may already leverage on the direct side of the house. With a bigger view of each member of your target audience and their response across channels, this enriched asset will be indispensable. Ensure that your digital marketing colleagues can see and understand how to use this insight. This is critical to delivering a consistent and engaging customer experience.

Perform contact sequencing where you plan out the pace of communications you’ll do in each channel. You need to have some view of your target audience around customer segments such as where they spend time, profitability and predictive analytics so that you can assess propensities, and so forth. It’s presumed that you’ll have a base idea of customer value which you should use to select within segments to develop your contact sequencing as it will give you the ability to drive profitability.

Work with your digital counterparts to help understand how audience lists are built (compiled vs. subscriber lists). Consider quality versus quantity. Invest in this process with great scrutiny. You will want to ensure consistency and collaboration.

Employ analytics and insight via third-party, non-SQL data to expand the knowledge of your customers and target audience while building customer intelligence and insight. Using your regular customer database isn’t enough. While soliciting third parties, work with knowledgeable, reputable data vendors who can offer multiple, flexible options.

3. Assemble the Execution Engine — Here’s where you’ll connect your marketing systems and bring everything to life. Identify the channel pieces you need to have working in tandem; focus initially on low-risk campaigns and have a vision for expansion. With the need for near real-time processing and incorporation of new data as it becomes available, avoid a “big bang” or a calendar-driven approach to capitalize on new assets in favor of a much more dynamic model required to play and add value in the digital space. Focus on being in-market with integration of the highest value deployment channels first.

Multichannel efforts integrate information from other channels in order to better target and engage your audience through their preferred channels. You’ll want to start monitoring, logging and correlating interactions in order to identify patterns which will help you to establish behavior and preferences. This in turn will help you create better offers and timing of those offers, and increasingly refine and hone your marketing efforts to optimize outcomes. Discuss and brainstorm scenarios and examples of where they will be used. For example, you’ll want to understand how direct mail and email campaigns work together simultaneously and how these systems are fully fused where you can fuel the stimulus to the audience, measure their response and take the next step with the right channel.


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Test, measure, refinePerform an active test and run a set of strategies around multiple channels. This is a new tool for your organization. Ensure that all involved parties can see the vision and ability to coordinate the customer experience. Send out one offer to a random set of people and see what happens. Send a multi-wave campaign with an offer first through one channel, followed up by another channel and another, always measuring the response. You can test campaigns against customer-initiated conversations, measure them against your best customer profiles and map out new customer segments. Test and learn the strategy that you’ve been doing and when you figure out what kind of conversations work for you, bake that into your campaign design. The possibilities will continue to expand as newer features, channels and devices are adopted.

These recommendations can help build a marketing system that pays off remarkably quickly. Several major brands have been able to deploy robust, fully functioning solutions in as little as 90-180 days.

CUSTOMER DATA SOURCES

MARKETING CENTRAL NERVOUS SYSTEM

EnhancementsSegmentation

3rd PartyData Transactional

Data

Closed-loopResponseAnalysis

& Reporting

Insight Mgmt.Analytic Tools& Data Marts

CampaignProcesses &

3rd Party Apps

ClientMarketingDatabases

Call the data from its source

Integrated partner ecosystem

Integrate customer data sources and touch points

Send and receive signals that influence consumer behaviorCorrelate consumer behavior with marketing/advertising

across channels over time

Direct Mail

Mobile

Website Email

Display Text Mobile Apps

In-storeMobile

SocialMedia

Call Center

In-storeNetworks

ConnectedDevices

ITV

CustomMedia

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ResultsSavings Bank Life Insurance of Massachusetts blended customer data with third-party insight to help craft complex “life stage” segments of their target audience. Engaging their audience in a coordinated, multichannel fashion with a personalized communications approach helped them exceed their leads-to-customers by 123 percent at a lower cost per lead against budget cuts in marketing and sales while also introducing prospects earlier in the buying process. They reduced cost per lead and increased profit in less than 120 days.

Rodale is an American publisher that executed the right sequence of campaigns to their base by blending offline and online data with flexible business rules. They ran a series of 100 or more email campaigns simultaneously, seamlessly moving customers from new acquisition campaigns to cross-sell/up-sell opportunities and into maintenance or reactivation campaigns. Each customer experiences a unique conversation, tailored to their individual interests and the system suppresses products they’ve purchased or opted not to receive offers on. Rodale defines a unique rhythm of customer contacts and “rests” for each campaign, providing the ability to communicate daily with new acquisitions and less frequently but still regularly with longer-term customers. This segmentation allowed them to increase the number of products they promote from 23 per month to 151 per day while seeing an increase in gross orders of 44 percent.

MOMENT OF TRUTH: Download a new white paper (www.acxiom.com/mot) that examines the overall strategy of capabilities necessary for marketing organizations to compete in the new, digitally connected world.

ConclusionDirect marketing’s role in digital marketing efforts will evolve as organizations connect the concepts of addressable media to the systems they already have in place. While multichannel marketing is a desire of today’s leaders, direct marketers need to learn these trends and take the responsibility for evangelizing their capabilities within their organization. Indeed, the value of your marketing assets and processes should be evaluated to include benefits outside of the typical direct mail and customer service channels.

Data marketers have great opportunities to build upon their skills, help their organizations succeed in the digital age and advance their careers by taking practical, incremental steps. They can get started now by building a logical marketing system as part of a set of capabilities that marketing will need to embrace in order to succeed in the digital age.

To learn how Acxiom can work for you, call 1.888.3ACXIOM (1.888.322.9466) or visit www.acxiom.com.

AC-0258-13 2/13

(1) Forrester – Using Digital Channels to Create Breakthrough Multichannel Relationships, Feb 2010 (2) Winterberry report from Bruce Biegler (3) DMA 2010 Response Report (4) Briggs, Rex and Stuart, Greg. What Sticks: Why Most Advertising Fails and How to Guarantee Yours Succeeds, Kaplan Business, September 1, 2006

601 E. Third, Little Rock, AR 72201acxiom.com1.888.3acxiom

©2013 Acxiom Corporation. All rights reserved. Acxiom is a registered trademark of Acxiom Corporation.

9/23/2013

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#DMA2013 Marketing Synergy, Inc.

Integrating Digital Media with your Marketing DatabaseDatabase Marketing Intensive: Part 7

Randy HlavacLecturer Professor

Northwestern University, Medill IMCCEO ‐Marketing Synergy


Social Marketing with bottom line impact

Cracking the code to Social ROI

Randy HlavacLecturer, Medill IMC Program

Northwestern UniversityCEO, Marketing Synergy [email protected]

630.328.9550

SOURCE: Medill IMCAGE: 39 [I dye my hair gray for effect]DIFFICULTY: 4FIGHTING STYLE: Set High Expectations & Exceed ThemTWITTER: @randyhlavacEMAIL: R‐[email protected]: #NUSocialIMCSPECIAL MOVE: Shout of Earth(Left, Right, Up, Down, A, A, A, B, B, B)

RATING: Awesome

RANDY

9/23/2013

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#DMA2013 Marketing Synergy, Inc.#DMA2013 Marketing Synergy, Inc.

According to IBM, managing data & social media is what keeps CMOs “up at night”

3

IBM Global CMO Study 2012


And data management & social are key pain points for companies today

IBM Global CMO Study 2012

9/23/2013

3


Even CEOs are concerned about connections, engagement & relationships

IBM Global CEO study 2013


Our Goals are Clear

What is Big Data & how do I process it for my business?

What do I need to know to capture it?

How is it being used today?

What tools can I use?

9/23/2013

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WHAT IS BIG DATA?Identifying the data which drives your organization today


You need to address 4 characteristics of Big Data…the 4 V’s

8

IBM Academic Initiative 2013

9/23/2013

5


When you think about Big Data, think WATER!

OceansOceans RiversRivers LakesLakes


Water helps understand the nature & velocity of data within your organization

Big Data

Data at Rest

Data in Motion

Data in SilosOceans

Rivers

Lakes

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Data at rest is your transactional and customer data

• The Calm of the Ocean– It is data periodically collected and processed– Data at rest represents:

• Past purchase history• Past contact history• Predictive and behavioral measures• Demographic and lifestyle data• Life Stage and Lifestyle Clusters

• We can use this type of data to service customer requests, analyze and classify customers, and predict future behaviors


Data at Rest is periodically processed and added to the other “Ocean” of

data

Merge/purge update

Purchase data

Customer service

Marketing programs

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Data in Lakes

• Siloed information– Sales data– Product data– Silos specific information – telecenter, social networks, etc.

• From a Big Data perspective, you need to:– Know where it is at– Be able to get to it when needed– Generally, not a concern


Data in Rivers

• Sources of rivers of data– Social– Website– Mobile

• These data sources are data rich and always moving

• Allows for real‐time and historical analysis• Also has some interesting and very powerful marketing opportunities

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Let’s look at an example of the power of rivers of data

• www.wefeelfine.org


River of Data issues• Acquiring it

– You either need APIs to gather the data yourself or a vendor

– One of the best is Boardreader• www.boardreader.com• They are specialists in deep diving data across the world

– You then need to analyze and classify it• Lexalytics is a good place to learn

– You then need to append it to your database• This is the hard part of the process

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To really capture & process rivers of data, you need different integrated systems

IBM Big Data Management Conference 2013


WHAT DO I NEED TO KNOW TO CAPTURE IT?

Understand your marketing and social strategies

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Big Data needs links• The key to successfully using Big Data is to get the links to your marketing and management systems

• To do that, you need to understand the way your business markets and develops its social and mobile programs


What are the links marketing built into your marketing and contact systems?

Social Varies

Mobile Registration?

Website Registration?

Virtual Community

Registration

Telecenter Phone or Address

Email

Address

Mobile Phone

Password?

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Why do you want individual links to the rivers of data?

Personas & Personalization

Customers & Prospects

Profitability metrics & business justification

Intercept & real‐time marketing


BUSINESS APPLICATIONS USING BIG DATA

Why its important to link the up

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Companies deploy one of three social strategies

• Engagement Marketing– Anonymous marketing– Create Awesome & let it go viral– Key behavior is to tell others about the Awesome experience– Doesn’t attempt to target individual or add them to the database

• However, it does use social monitoring systems to engage with participants in real time• For IT

– No impact on your database– Need social monitoring to determine who is talking about your & capture who they are

• Salesforce – Radian6


Engagement MarketingOld Spice Manly Manhttp://youtu.be/owGykVbfgUE

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They monitored social media to find interesting questions to respond to…and it worked

http://youtu.be/fD1WqPGn5Ag


Social Marketing is coming on strong• Social Marketing

– Create compelling & relevant content– Place it behind a wall– Have an information campaign – While there, also put a cookie on their computer to track other activities– Require an information exchange to make it work

• This is the link you are looking for• For IT

– You need a database what you capture in the campaign– Link to your marketing database to classify [persona] and develop a nurture campaign

• Identify their relationship with your company• Identify their product life cycle and where they are today in the process

– Integrate web and social to capture cookie information on future visits

• Trolling for targeted prospects• Linkage includes email & address from the form

• Also get key data to classify them• And gives them limited control of the process

• You also place a cookie on their computer to track their relationship on your website & virtual community sites

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Social marketing – acquire, classify & engage


Social IMC is the most databased of the social strategies

• Social IMC– Focus on Empowering a virtual community

• Give them what they want– Goal is to create a virtual community or totally empowering program

• Gives a virtual community exactly what it wants• REQUIRES a deep marketing database linkage

– Get email & password– Requires you give total control to the individual

• The database controls all aspects of the relationship – from the start– With Social IMC, you need to use your username and password to reap the benefits of the

program– You have total integration and total control of the relationship

• For IT– You need an integrated marketing database– Real time data from the virtual community you are creating– Link to your marketing database system

• Same metrics and classification as with Social Marketing– Everything moves through the information exchange

9/23/2013

15


Social IMCCarling Black Label South Africa

http://youtu.be/xyDNvbUEvHg


Social IMCNorthface China http://youtu.be/6ktKwssCH‐Y

9/23/2013

16


Summary of Social


Mobile Marketing

• Mobile Apps give you the perfect chance to get a registration

• Put it behind a short, focused registration wall• People will give you the linkage data –especially their mobile phone number and email if the mobile app is interesting

9/23/2013

17


Mobile MarketingiButterfly http://youtu.be/vEE6M0iW‐Nw


Social IMC with MobileCoke Chok Chok! http://youtu.be/pEDsERv‐rFA

9/23/2013

18


Gamification really integrates the app to the marketing database

http://youtu.be/qsl9NjyVpHY


Intercept Marketing is a strong application ‐ KLM

http://youtu.be/pqHWAE8GDEk

9/23/2013

19


WHAT ARE SOME FREE TOOLS TO USE?


Social Mention is the place to start

• Use it to check your company, your products, and your competition• Monitor sentiment over time and note major changes [Crisis!]• Monitor keywords & top users [influencers?] and that they are saying

9/23/2013

20


Let’s Look at Men’s Fashion


The left side of Social Mention gives you key information & the right side gives you the ability to

compare searches

9/23/2013

21


Advanced Search lets you focus on specific languages


Alltop is great for tracking subjects

• Alltop allows you to monitor topics [and create them] to identify who is active in the social cloud

9/23/2013

22


Checking out Men’s Clothing


Fashion gives us great results

9/23/2013

23


Your target audience follows key influencers at center of a community

• We follow identifies the key influencers by topic across twitter and other social networks


Wefollow let’s you search for experts by topic

9/23/2013

24


Google +


LinkedIn

9/23/2013

25


Big Data Requires you to think about data in new ways


Questions?

Cracking the code toRandy Hlavac

Lecturer, Medill IMC ProgramNorthwestern University

CEO, Marketing Synergy IncTwitter: @randyhlavac

Social IMC: #[email protected]

630.328.9550

9/23/2013

1

Marketing ROI:How to Ensure Political, Technical,

and Business Success for a Database Project


Pegg NadlerPresident, Pegg Nadler Associates Inc.

Pegg Nadler: Background• Database marketing consultant in media,

nonprofit, publishing & retail industries

• Experience: Headed DB operations at Smithsonian, Phillips Publishing, Consumers Union & HFMUS. Marketing & Sales for Metromail (now Experian), Abrams Books, Belvedere Press, The Fur Vault, Jindo Furs, Hadassah

• Clients: AT&T, China Post, Corporation for Public Broadcasting, Discovery Channel, Direct Marketing Association, HFMUS, Smithsonian Institution, THIRTEEN WNET, Time Life Books, US News & World Report

• Associations: Direct Marketing Club of New York Past President, DMA Ethics Policy Committee Member, DMA Annual Planning Conference Advisor, DMA Nonprofit Federation Former Advisory Council Chair

9/23/2013

2

Today’s Presentation

3

• The Changing Business Landscape• Keys to Database Success• War Stories• Success Stories• Lessons Learned• Recommendations

The New Business Reality

4

Integrated marketing communicationsReal time analytics & product offerings

Data generation explosion

Growth of online, mobile & social media

Audience fragmentation

Databases as key drivers to revenue

9/23/2013

3

Challenges Still Exist

Managing the customer multi-

channel experience is a

priority

Today’s customer databases are insufficient

to deliver the insight needed

Measurement is critical but

knowing what to measure & how to measure is a key investment theme

Top Concerns

Marketing’s changing

needs are not met by

internal IT departments

Push to reduce costs internally & externally

Improve ROIIntegrate

technologies across

channels

9/23/2013

4

The Big Question

7

How do we convince management to invest or reinvest in the database?

What is Key to Database Success?

8

An “intelligent” business strategy

The “right” team of players

A “decent” database system & adequate data

9/23/2013

5

#1: Key Business Issues

9

The competitive advantage comes from how analysis is handled

Address the problem, not the technical solution

What decisions need to be made to be successful?What questions do you want to answer to drive your sales & marketing

programs?

Begin with an intelligent business strategy

Not data, not technology, not tools

#2: A Database Champion

10

Database Leader

Marketing expert

Technically proficient

Statistically savvy

Politically astute

IT independent

Vendor & system

knowledge

9/23/2013

6

#3: The Right Team

11

Database Team

Marketing experts

Technically proficient DB

Analysts

Statistically savvy

Modelers

Politically astute DB

Leader

Senior Management

Support

DB Vendor & system experts

#4: Top Management’s Commitment

The Big C’s—CEO, CMO, COO, CFO,

CTO

Initial & ongoing financial backing

People power—

personnel for staffing

Mandatory compliance &

participation in DB projects

9/23/2013

7

#5: A Decent Database

13

Robust systems & capabilities

Budget to support ongoing

operations

Adequate & comprehensive

dataTimely

updates

Easy access to data by

database team

War Stories: Multi-Product Company

14

DB manager, no staff, multiple users

with little training around the company

Little DB knowledge, no standardized business rules

Lack of management commitment

Inadequate Funding Questionable ROI

IT drives DB vendor selection & build

Little or no funding for email, online or

social data

Opposition to use DB by various

departments

Modeling programs slow to test and/or

rollout

DB staff reductions

A failed database project

9/23/2013

8

Lessons Learned

• Absence of dedicated trained staff undermined project successNo time for novices

• No commitment from top C’s to override lack of DB cooperation throughout company. Big Guns Support

• Top C’s thought they could save $$ by using IT—major mistakes since IT does not know marketingThe Black Hole of IT

• Penny wise & pound foolish—the company must commit adequate $$ to fund project properlyMoney in the Bank

War Stories: Membership Organization

16

DB initiatives driven by CEO

CEO hires DB director

Limited experience DB

director

Fulfillment vendor used as DB

system provider“Black box”

models

Lack of DB knowledge

across company

Internal modeling team hired

Data & capabilities concerns

ROI unproven

New DB RFP issued

No budget approval

DB project stalled

9/23/2013

9

Lessons Learned

• Don’t let your CEO or management team hire an inexperienced database director. No time to be Green

• You get what you pay for. Spend what you need to hire expertise.

Penny wise Pound foolish

• Your fulfillment company should not serve as your database vendor. Find a DB provider with the expertise and services you require.

Experience counts

• Transparency in operations, analysis and modeling methodologies are necessary to encourage DB confidence, participation and success across a company.

Information is power

• Get the DB RFP requirements right the first time.Do Your Homework

Success Story: Hearst Magazines

18

No DB, use Fulfillment System

Modeling & Analytics done using disparate

systems

Senior Management Team makes

commitment to DBM

Select DB vendor Online & offline data integration

Commitment to modeling & analytics

VP DB Marketing hired

DB build begins ROI plan detailed

Program test and rollouts begin

Ongoing investment to improve DB &

marketing & real time online capabilities

9/23/2013

10

Lessons Learned

• Big C’s commitment to DB marketing for the short and long term success of the companyBig C’s Support

• Company objectives and goals clearly defined

Business Intelligence

• Build with MDB experts, not IT expertsDB Partnership

• Hiring a competent DB champion accounts for a quick start and continued success in DB programs

DB Champion

Demonstrating ROI: Hearst

Projected DB Investment

• Planned for 200% ROI in 3 years

• Increased mail efficiency, higher customer response rates, reduced marketing execution resources

• 30% more revenue from internet-sold subscriptions

• New models to produce 5% lift on response for mail

Actual ROI

• DB paid for itself in one year

• Consolidating information, getting clean data, buying better demographics and using online information for DM efforts

• Resulted in 25-30% offline response lift

• The database enabled reduction on outside lists by around 30%

9/23/2013

11

Taking Inventory

21

What is happening across the company that was not included in the initial DB build?

What is done in marketing, research, digital, social, editorial, customer service, email, mobile and finance?

What data and campaign information can you not integrate today?

What systems capture customer data across the company?

What are your company’s business and customer objectives?

What obstacles are in the way?

Building a Case for Senior Management

22

Gather case studies & success stories that pertain to your particular business & industry

Identify quick wins & gains vs. a long term detailed plan

Determine a reasonable budget for funding & operations

When necessary, think small using test databases & prototypes to gain approval

Don’t overbuild—meet your current & near future needs since technology & business change

9/23/2013

12

Critical Areas for Database Success

Key Business Issues Identified

A Savvy Database Champion

The Right Team of Players

Senior Management Commitment

A Decent Database & Adequate Data

Questions?

24

Thank you so very much!Please feel free to reach me at:

Pegg NadlerPresident

Pegg Nadler Associates, Inc.212-861-0846

[email protected]

OctOber 2009

targetmarketingmag.com

Pegg Nadler loves the unknown. Where others see challenges, she sees opportunities. Where others fear change, she fears boredom.

These are some of the qualities that have driven her 30-year direct marketing career, the bulk of which she’s spent advancing database marketing operations at commercial and nonprofit organizations and giving back to the direct marketing community. And they’re why she’s Target Marketing magazine’s Direct Marketer of the Year.

Speaking over the telephone on a recent Friday evening from her New York office, the vice president of database marketing for magazine publishing empire Hachette Filipacchi Media U.S. (HFMUS) quotes a saying from Hungarian Nobel laureate Albert von Szent-Györgyi Nagyrapolt that has verbally captured her world view since she studied English and art history at the University at Albany, State University of New York: “Discovery consists of seeing what everybody has seen and thinking what nobody has thought.”

“My approach to problem solving has actually always been the same,” Nadler says. “And it’s interesting how some people will find this a good approach and others will find that it could be maddening. It has always been very important for me to see the total scope of business in order to come to a decision. And this is probably one of the reasons why I love database marketing—because it really provides that wide picture.”

Falling Into Love Nadler began fusing her left and right hemispheres early.

The English and art history major entered direct marketing in 1979 by selling art and gift books for Harry N. Abrams.

“I fell into direct marketing,” Nadler says. “When I came to New York in the late ’70s, I landed a job at Harry Abrams … and I was first their advertising

By Heather Fletcher

Making sense and dollars out of database marketing

Direct Marketer of the Year:

PeggVice President, Database Marketing, Hachette Filipacchi Media U.S.

Nadler

COVER STORY

manager and then moved into an area called special sales, which was selling books into areas other than bookstores. And … really it was direct marketing: catalogs, book clubs, continuity programs. That was my first exposure into direct market-ing. And I thought that it was a little bit wacky, but that it was much more fun than selling books into bookstores. And it was something that I then stayed with for the rest of my life.”

From 1979 to 1990, her direct mar-keting career progressed from moving art books to selling facsimile editions of ancient manuscripts from the Vatican Library, then to hawking furs in a mostly pre-Internet, fully mid-animal rights move-ment era. “So being able to sell through the mail and through the phone became very important,” Nadler says of her 1988 to 1990 stint with Jindo Furs. Creatively working her way around the protester problem, she set up an 800 number for customers to call; secured accounts with the Home Shopping Network, Comp-U-Card, American Express and Diners Club;

and mailed catalogs. Catering to the jet set, Jindo placed computer terminals at kiosks in airport waiting areas so passengers could click to buy minks before boarding.

But her first taste of database market-ing, in 1990 at Metromail Corp. (now Experian), pulled her in to the direct marketing specialty. Within 18 months, she’d secured billings nearing $1 million for the marketing information, database and mail production company.

“I’ve certainly always been very sys-tematic,” Nadler says. “My attraction to English was that I think that speaking very clearly and getting your message across is an imperative. And probably what has attracted me to database marketing is that I’ve always … organized … I like to get projects done. And it probably is a very neat way of wrapping up the world.”

The Problem SolverSpeaking of the global picture, Nadler’s strengths include all aspects of database marketing—with the exception of in-depth statistical modeling, the implementation of

which she supervises. So when she accepts a new challenge, which is usually “directing startup operations, restructuring business operations and overhauling marketing departments,” she is either in charge of or overseeing every aspect of the solution.

“I’ve always been the person who can see the large business application and put the database together and then bring in the analytical people who will do the number crunching,” she says. “So I’m really a market-er who moved into database marketing. … While I’ve spent all these years doing direct and database marketing, in my heart of hearts I’m a marketing, product-development, business-development person.”

Since diving headfirst into database marketing in 1990, Nadler steadily has created and overhauled database systems and operations for some of the mightiest corporations and nonprofits in the country. Each situation is different and requires her to pull from her well-rounded direct marketing background as a vendor, con-sultant and client in the commercial and nonprofit worlds.

For instance, during the time she spent as a consultant at the Smithsonian Institution providing in-house database marketing expertise, Nadler managed operations first as a marketing database manager from 1992 to 1993, then as a marketing strat-egy director from 1993 to 1995. In that capacity, she analyzed the institution’s varied constituencies, including current and lapsed audiences.

Identifying those high-value donor prospects, proposing a list revenue pro-gram to double sales within the first year for rented database names, developing database user training programs and estab-lishing Smithsonian’s database marketing conferences probably already sound over-whelming.

But wait. There’s more. “Smithsonian had been using the data-

base, but not really to the best ability,” Nadler says. “So I came in, made tweaks to the database, worked with all of the dif-ferent parts of the Smithsonian Institution to really let them realize that they had a very good resource there. My one favorite story there at the Smithsonian, and this is really not unique to Smithsonian, is that Smithsonian had a database. It might’ve been 9 million [names] when I was there. And there were names which were not housed on the database, which were in each of the development offices, includ-C

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ing the central development office. And divisions didn’t want to share names. This is such a common occurrence. Not only in nonprofits, but in corporations: ‘Don’t want you to market to my names. Don’t want you to contact my names. Want to keep these names suppressed.’ And I really had to work, very carefully, to demonstrate that the names that were within these various development offices were most probably also on the main database.

“And by being able to overlay data, bring all of these names together, we would prob-ably have a much more effective develop-ment strategy if we were able to do that,” she continues. “Because we actually showed that the names that were housed in all of these different museums were already on the central database. And once we understood what the total correlation was from one area to another, we were able to make a much better fundraising pitch.”

Marketer for All SeasonsOf all the hats she’s worn during her direct marketing career, Nadler does have a favor-ite.

“I love a startup,” Nadler says. “And once the operation is going well, I’m bored. And that’s when I really like to turn it over. … That’s what I’ve done all along—startup, or revamp or overhaul. … And that’s why the consultant role is really a very good role for me, because that’s how I’ve always thought as I’ve gone into companies. And I’ve been with so many different companies that it really has provided me with a very good bird’s-eye view. And it’s so important to be able to step back and look at what’s going on.”

Pegg Nadler Associates Inc. of New York

appeared from 1997 to 1999, disappearing when Nadler accepted the full-time job of re-energizing “the marketing face” of Hadassah, a nonprofit, pro-Israel Jewish women’s organization. After a four-year stint as customer database services direc-tor for Consumers Union, publisher of Consumer Reports magazine, it was back to the milliner in 2004 to get refitted for the consultant hat.

The list of companies seeking her advice as a consultant is so long and so filled with the “Who’s Who” of brands and nonprof-its that it simply reads alphabetically, in small type, on her résumé: AT&T, B’nai B’rith Youth Organization, Corporation for Public Broadcasting, Discovery Channel, Hachette Filipacchi Media …

That’s where, in 2005, she met Hachette’s Philippe Guelton. The HFMUS executive vice president and COO had always wanted to build a database. “He had established a database when he was running Hachette’s operations in Japan,” Nadler explains.

Guelton hired Nadler as a consultant in 2005, and she worked on the Hachette project for two years, while mixing in other consulting projects and adjunct professorships at New York University and Baruch College, City University of New York. Finally, in 2007, Guelton successfully recruited her to work full time for Hachette so she could complete building and implementing the database operations.

“The last thing I wanted to do was give up my consulting,” she says. “It’s so much fun to be on the outside looking in and letting people tell you what really is troubling them. Because you’re outside

the whole political arena, and people will be very honest with you about what is truly making them unhappy and what their aspirations and dreams are. So, as I say, it was a big quantum leap to go from consulting back to working in a corporate environment [at Hachette]. But, as I said, it was certainly for a really good cause. And it’s been hard. It’s been challenging. And not for one day have I been bored.”

Grabbing Nadler’s attention for a few moments while she’s implementing data-base operations in an environment she clas-sifies as undergoing a revolution can feel like pulling a surgeon out of an operating room. (While headlines about the publish-ing industry have been less than flattering, reflecting widespread industry trauma—from editorial layoffs to magazines folding altogether—Nadler is energized about the future. She envisions a personalized multichannel experience that’s relevant to the consumer. More on that later.)

“We’re in the process of putting together a very strong operation,” she says during a quick call on a recent Monday, in between planning and budget meetings and search-ing for a director of analysis and modeling. Database operations, she says, are meant to determine “the new products, businesses and services Hachette should be offering. And that’s the most fun.”

“In today’s environment, a rich and fully developed database is imperative,” Guelton relates. “We are more effective in helping our advertisers target their prime audiences and ideal prospects and in providing our subscribers with new products and better services. Since joining us in 2005, Pegg Nadler has been key in leading our efforts to expand our database capabilities …”

‘… with the lowering of processing and technology costs, we are finally able to really improve our marketing to where everything is going to be measurable and really everything’s going to morph into direct. Which is why we’re calling it integrated marketing.’

—Pegg Nadler

COVER STORY

InfluencesMore than just DMRS Group President Bernice Grossman’s friendship and men-toring (see sidebar below) and the wisdom of von Szent-Györgyi Nagyrapolt have provided inspiration to Nadler during her long direct marketing career.

Nadler says her other direct mar-keting influences include Jack Kliger, former president and CEO of Hachette Filipacchi Media U.S. (who, as of press time, was reportedly taking over as act-ing CEO of TV Guide). Chairman of the Magazine Publishers of America from 2005 to 2007, Kliger took the unpopular stance that circulation metrics needed to change and magazine publishers needed to embrace digital technology instead of fighting it. “It is essential, I believe, that our industry moves to a more timely system of readership measurement—a system that shows the connection between distribution and readership more effectively,” according to a tran-script of Kliger’s “MPA Breakfast with a Leader” from Dec. 7, 2005.

“The whole notion of the measurable audience going beyond what had been the standard magazine circulation base is actu-ally something that Jack Kliger … began talking about … years ago,” Nadler says. “And I think when he first spoke about it, a lot of people thought that he was just off-base. And he really saw this years before a lot of other parts of media and ad agencies began to glean onto this. I think

he was just aware that suddenly there was a movement away from print and that the circulation counts weren’t really reflecting accurately how many people were involved with reading or being exposed to a certain product.”

To that end, Nadler says nonprofits were the first organizations to take methodical approaches to understanding their audienc-es, or members. During the ’60s, nonprofits were trouncing commercial enterprises with the exception of those like American Express and Reader’s Digest.

“What were nonprofits doing early on?” Nadler asks. “They were writing down all their donor information on index cards—the earliest form of database marketing. They got it so soon. … Survival. That was the only way that they were going to be able to keep the funding coming in.”

Commercial entities caught on to the retention concept later, she says, when aggressive acquisition campaigns no longer worked as easily. Nonprofits, which had been cultivating their existing donor bases all along and moving them up the giving pyramid one step at a time, served as a les-son to corporate America, Nadler says.

Enter the next set of visionaries Nadler cites: Don Peppers and Martha Rogers, the founding partners of Norwalk, Conn.-based customer-centric marketing strategy consultancy Peppers & Rogers Group. Nadler says the duo talks incessantly about one-to-one marketing. Or, as the group’s Web site attests, “treating different cus-

tomers differently” by using data to keep and grow customer relationships.

That creative rather than facts-only approach to database marketing points to the last influencer Nadler mentions: Arthur Middleton Hughes. Hughes is the founder of the Database Marketing Institute of Fort Lauderdale, Fla., and a senior strategist with Burlington, Mass.-based e-mail marketing firm e-Dialog. She interprets his stance as saying that there are two types of database marketers—constructors, who assemble lists and successfully build the database, and creators, who take those names and turn them into loyal, returning customers.

Finally, in Grossman’s case, the admira-tion is clearly mutual. Grossman describes Nadler as a politically savvy “overachiever” who has no use for “fluff” and will work as hard as she makes anyone else work.

“Pegg is a continual learner,” Grossman says. “She is always asking questions. And so, when she’s faced with whatever today’s surprise is, business surprise, she can go back to that knowledge store of hers and pull from it. Also, she’s a really good manager. People work for her for extended periods of time. I think that there’s something to be said for being a good manager; I don’t think it’s all that easy.

“I also think that in the competitive world of database marketing … she’s done extreme-ly well because she earned it,” Grossman adds. “… She has this … strategic ability, as opposed to a tactical functionality. She’s able to look at the big picture. [The] big picture is,

A few of the business leaders who have been influential to Pegg Nadler: Bernice Grossman, Arthur Middleton Hughes, and Don Peppers and Martha Rogers.

‘What I want to accomplish.’ And then she can go down and look at all of the different issues she has to address to see whether or not she can accomplish it. … I certainly think it’s helped her move forward.”

What It Is, What It Was and What It Shall BeNadler is called on to speak to industry leaders and college students alike, and often gives them the same introduction to the craft.

“Direct really demanded a response,” Nadler says of the historical difference between direct marketing and generic advertising. “Because you could actually track who was buying what and when. And, of course, database marketing then allowed us to ramp this up a notch, because we could be tracking what that individual customer was buying over time.

“I just feel that we’ve made a quantum leap, and I actually talk about database marketing being the great leap backward,” she says of the current state of database marketing. “Because I’ve always said that database marketing has allowed us to get to that personal level, which, of course, is

how all business transactions started years ago. [The transactions like] mom and pop shops knowing what color you liked and when you went out to buy a dress and what your favorite ice cream flavor was. But, as I said, with the lowering of processing and technology costs, we are finally able to really improve our market-ing to where everything is going to be measurable and really everything’s going to morph into direct. Which is why we’re calling it integrated marketing. I mean, even NYU, in their advanced program for direct marketing, they changed the name to integrated marketing to really reflect what was going on.”

Measurement and ROI are now para-mount to marketers, no matter what chan-nel they use, instead of following nebulous metrics like Web site page views and clicks, she says. “It means that we’re not talking nonsense anymore. We’re truly talking sense and dollars.” And advancing technol-ogy will only make that more important, she predicts. Direct mail will survive and be more relevant, mobile marketing will grow exponentially, and e-mail market-ing will be more targeted—but not before

consumers receive a lot more spam. Web sites will load instantly, and online video will load faster and be more fun.

Moving from the future of direct mar-keting to its specific future, as married to publishing, Nadler’s excited tone doesn’t change much.

“This is the most amazing time to be in what we like to say is publishing media, because it is changing dramatically,” she says. “We’re not talking about evolution anymore; this is revolution. And no one knows which species is going to make it in this catastrophic collision. Will the industry collapse? I don’t think so. I think that what we’re going to be left with will be a publishing medium that is so dynamic and so important that it’s going to go be that much better.”

So after accomplishing what she set out to do at Hachette—when database operations are running smoothly—what will the next decade bring for adventure-seeking Nadler? With a full-throated laugh, she answers: “I wish I could tell you. I wish I could tell you that.” yy

What does a database marketer do to have a good time? Why, hang out with other database marketers, of course.

From affiliations with the Direct Marketing Association, the Direct Marketing Clubs of New York and Washington, D.C., and the John Caples International Awards to her former professor-ships, it might not seem like Nadler has time to do much else.

For instance, Xenia “Senny” Boone, DMA’s senior vice president of corporate and social responsibility, harkens back to Nadler’s time as chairwoman of the advisory council of the DMA Nonprofit Federation (DMANF). From 2003 to 2005, Nadler led the committee while Boone was the DMANF execu-tive director.

“She really helped shape what we call the [Nonprofit] Leadership Summit,” Boone says. “This was one of her brain-childs. You can appreciate putting together events could be stressful, but she always was a believer in the need for senior-level events for the fundraiser and the marketer for the non-profit community and really threw herself into it and really was committed. And when it came to working with the volunteers to get them to the event … she was somebody who would pretty much do anything to inspire and cajole and get people

to attend this event and also to lead parts of the event.”Boone adds that Nadler remains active with the DMA,

specifically helping shape direct marketing ethical compliance guidelines.

Nadler does find time to spend with her mentor—Bernice Grossman, president and founder of data marketing consultancy DMRS Group of New York—whom she met 15 years ago at an industry event.

“We usually talk about the various types of software installa-tions,” Grossman says. “We talk about different kinds of campaign management software. We talk about what are the best ways to segment and target for ultimate acquisition and retention. We talk about data and its value as it relates to enhancing the intelligence of, in her case, subscribers, to be a better marketer.

“… Probably the most recent conversation would’ve been about the comparative evaluation of various software development cam-paign management tools and their effectiveness for the marketer,” Grossman continues. When asked if she could reveal that conversa-tion’s conclusions, she declines. Because they’re friends, Grossman says, she’ll provide Nadler with opinions “confidentially, for which I charge everybody else.”

Extracurriculars

COVER STORY

Reprinted from Target Marketing® October 2009 © Copyright 2009, North American Publishing Co., Philadelphia PA 19130

DMA 2013 Database Post Intensive

Recommended Sources for Database Marketing, CRM and Integrated Marketing

The following lists include Pegg Nadler’s personal recommendations for information and reference material in your day-to-day database marketing activities. Many of the books listed are a part of my standard professional library. Some of the older titles are DB classics and provide an excellent framework for solid database marketing, best practices and guidance on DBM processes.

Websites, Magazines, Newspapers & E-newsletters

Ad Age www.adage.com B to B www.btobonline.com Chief Direct Marketer www.chiefmarketer.com Colloquy www.colloquy.com CRM www.destinationcrm.com Customer Think www.customerthink.com Direct www.directmag.com Direct Marketing News www.dmnews.com DMA www.the-dma.org E-Consultancy www.econsultancy.com Marketing Profs www.marketingprofs.com Marketing Sherpa www.marketingsherpa.com 1 to 1 www.1to1.com Smart Data Collective www.smartdatacollective.com Target Marketing www.targetonline.com

http://www.adage.com/

http://www.btobonline.com/

http://www.chiefmarketer.com/

http://www.colloquy.com/

http://www.destinationcrm.com/

http://www.customerthink.com/

http://www.directmag.com/

http://www.dmnews.com/

http://www.the-dma.org/

http://www.econsultancy.com/

http://www.marketingprofs.com/

http://www.marketingsherpa.com/

http://www.1to1.com/

http://www.smartdatacollective.com/

http://www.targetonline.com/

Books

Arikan, Akin, Multichannel Marketing: Metrics and Methods for On and Offline Success, Sybex, 2008 Baier, Martin and Riuf, Kurtis and Chakraborty, Goutam, Contemporary Database Marketing, Racam Communications, 2002 Berry, Michael and Linoff, Gordon, Mastering Data Mining, Wiley, 2000 Brown, Stanley and Gulycz, Moosha, Performance Driven CRM, Wiley, 2002 Burnett, Ed, Database Marketing: The New Profit Frontier, Morris Lee Publishing, 1996 Cooper, Kenneth Carlton, The Relational Enterprise, American Management Association, 2002 Cross, Richard and Smith, Janet, Customer Bonding, NTC Business Books, 1995 Curry, Jany and Curry, Adam, The Customer Marketing Method, Free Press, 2000 Curry, Kay, Know Your Customers!, Kogan Page Ltd., 1992 Deloitte & Touche, Managing Database Marketing Technology for Success, Direct Marketing Association, 1992 Drozdenko, Ronald and Drake, Perry, Optimal Database Marketing, Sage Publications, 2002 Dyche, Jill, The CRM Handbook, Addison-Wesley, 2002 Paul W. Farris, Neil T. Bendle, Phillip E. Pfeifer and David J. Reibstein, Marketing Metrics: The Definitive Guide to Measuring Marketing Performance (2nd Edition), Pearson Prentice Hall, 2010 Francese, Peter, Capturing Customers, American Demographic Books, 1990 Franks, Bill, Taming the Big Data Tidal Wave, Wiley and SAS Business Series, 2012 Freeland, John, The Ultimate CRM Handbook, McGraw-Hill, 2003 Godin, Seth, Permission Marketing, Simon & Schuster, 1999 Gordon, Ian, Relationship Marketing, Wiley & Sons Canada, 1998 Greenberg, Paul, CRM at the Speed of Light, McGraw-Hill, 2002 Hartmann, Kenneth, Research and the Customer Lifecycle, Direct Marketing Association, 1995

Hughes, Arthur, The Customer Loyalty Solution, McGraw-Hill, 2003 Hughes, Arthur, Strategic Database Marketing (4th edition), McGraw Hill, 2012 release Jackson, Rob and Wang, Paul, Strategic Database Marketing, NTC Business Books, 1995 Jeffrey, Mark, Data-Driven Marketing, Wiley, 2010 Lee, Dick, The Customer Relationship Management Survival Guide, HYM Press, 2000 Mayer-Schonberger, Victor and Cukier, Kenneth, Big Data: A Revolution That Will Transform How We Live, Work, and Think, Houghton Mifflin Harcourt, 2013 Nash, Edward, Database Marketing, McGraw Hill, 1993 Newburg, Jay and Marcus, Claudio, Target Smart!, Oasis Press, 1996 Newell, Frederick, loyalty.com, McGraw Hill, 2000 Newell, Frederick, The New Rules of Marketing, McGraw Hill, 1997 Nykamp, Melinda, The Customer Differential, American Management Association, 2001 Peck, Mark, Integrated Account Management, American Management Association, 1997 Peppers, Don and Rogers, Martha, Enterprise One to One, Currency Doubleday, 1997 Peppers, Don and Rogers, Martha, Extreme Trust: Honesty as a Competitive Advantage, Portfolio, 2012 Peppers, Don and Rogers, Martha, Managing Customer Relationships: A Strategic Framework, Wiley, 2011 Peppers, Don and Rogers, Martha, The One to One Fieldbook, Currency Doubleday, 1999 Peppers, Don and Rogers, Martha, The One to One Future, Currency Doubleday, 1993 Pine II, B. Joseph and Gilmore, James, The Experience Economy, Harvard Business School Press, 1999 Raphel, Murray and Raphel, Neil, Up the Loyalty Ladder, HarperCollins, 1995 Roman, Ernan, Integrated Direct Marketing, NTC Business Books, 1995 Roman, Ernan, Voice of the Customer Marketing, McGraw Hill, 2010 Schmidt, Jack and Weber, Alan, Desktop Database Marketing, NTC Business Books, 1998

Shaver, Dick, The Next Step in Database Marketing, Wiley, 1996 Shepard, David, The New Direct Marketing, McGraw Hill, 1999 Seybold, Patricia, The Customer Revolution, Crown Business, 2001 Smith, Ellen Reid, e-loyalty, Harper Business, 2000 Swift, Ronald, Accelerating Customer Relationships, Prentice Hall PTR, 2001 Tooker, Richard, The Business of Database Marketing, Racom, 2006 Vavra, Terry, Aftermarketing: How to Keep Customers for Life Through Relationship Marketing, Irwin, 1992 Zikopoulos, Paul and Eaton, Chris, Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw Hill, 2012