Quant5 planning ness-050613_final

© 2013 Quant5, Inc.

How to apply Predictive Analytics

to Marketing Challenges

For the Planning-ness Conference May 10th, 2013

Doug Levin | [email protected]


Agenda

Planning-ness Approach ~Time Allocation (Minutes)

Teaching 45

Putting teaching into practice 45 – 60

Evaluation and discussion 20-30

2


Teaching

3


Challenges Facing Marketing

• Frequently make critical decisions without:

– The information they need

– The insights in their business & environment they need

– Access to data in other parts of their organization

– A supporting cast & crew (aka data scientists)

• Hours are spent each week searching for data

– “Connecting silos”


Challenges Facing Marketing Departments

• Roadblocks to Success:

– Being asked to come up with brilliant new insights • Shortage of data scientists to do statistics, math, etc.

• No tools

– An avalanche of data from mobile, social media and other sources… and growing by the minute!

– Data located in legacy systems run by IT • Have to connecting silos through organizational means not direct

reporting authority

5


How ambitious are you? • Do you want to have a “data centric” business? Business decisions no longer based on gut instinct

• Do you want to have a “data centric” marketing dept.?

Fact-driven relying on measurement & feedback

Real-time data

At the point of impact

Everyone’s Involved & Connected

Ubiquitous optimization

Automated

Relying on predictive analytics & validation

Characteristics

6


Data in your organization that can help you… discover trends and opportunities

• The top 5 sources of data tagged for predictive analytics: • 54% Sales

• 67% Marketing

• 69% Customer

• 55% Product

• 51% Financial

In addition, 40% of companies surveyed indicated that “Social (Facebook, Twitter & LinkedIn) had potential value in predictive analytics

Source: SAP Analytics 02/08/13

All related to revenue

7


Persistent, Deep Questions

Who are our • core customers? • frequent buyers? • best customers? • poorest payers? • Best sales guys?

The Who?

How do we: • Attract the best customers to buy

more? • Reduce the cost of customer

acquisition? • increase first purchase size? • increase subsequent purchase size? • increase cross-product purchases? • Reduce fraud

The How?

8


Predictive Analytics CAN help? A LOT!

• Predict market trends • Predict customer needs • Predict price volatility • Create customized offers for

each segment and channel • Predict changes in demand

and supply across the entire supply chain

9


Use Predictive Analytics… When your spreadsheet runs out of gas

Data

Variables

Logic

Speed

10


Predictive Analytics Solutions

Horizontal Vertical

Embedded

Database Analytics

•Hadoop

•Unstructured and Structured Databases

Consulting Services

11

© 2013 Quant5, Inc. © 2013 Quant5, Inc.

Functional / Business Unit Outcomes Goal: More new & incremental sales

Sales

Goal: ROI + efficiencies + incremental rev’s

Marketing

Goal: Better products, prices & competitiveness

Product

12

• Customer Analytics • Prospect analytics • Sales cycle analytics • Price analytics • Competitive Prospects

& Intelligence • Industry trends


Functional / Business Unit Outcomes Goal: More new & incremental sales

Sales


Marketing


Product

13

• Market trends & Drivers • Competitors, threats &

vulnerabilities • Opportunities & Budget

Optimization • Improving positioning &

messaging • New products, markets

and partners • Marketing activity

optimizations • Business risks • Threat detection


Functional / Business Unit Outcomes

Goal: More new & incremental sales

Sales


Marketing


Product

14

• Product Management Analytics

• Actionable Product Intelligence

• Competitive Analysis • Partner Analysis • Supply Chain Analysis • Launch Plans & Positioning • Price Analytics


Steps to Successful Predictive Analytics

Design Implement Measure

• Goal setting • Resource

Assessment • Questions to

be answered

• Tests • Deployment(s) • Feedback

• Assessment of KPIs • Improvements • Validation

15


Non-Obvious Knowledge

and Probabilities

Predictive Analytics for Business

Analyze current and historical data in order to better understand customers,

products and partners, and identify potential risks and

opportunities

16


Putting the teaching into Practice

17


Situation Analysis

• Lucy Couture: – A 3-year old eRetailer

• High-end “Juicy” couture

– Bags, business attire, dresses, intimate apparel, parts, shirts, shoes, skirts

– Demographic: • Women (25+)

• In College (19-25)

• Other (gift purchasers)

– Generates a couple of millions in gross revenues p/year

– Has 15 employees

– Limited data centricity

Marketer What are the prices sensitivities?

Product Manager

What are the product relationships?

Marketer What are the demographics (age cohorts) of purchases?

Marketer What sort of financial data can be used?

18


Situation Analysis

• You are the Director of Marketing – With a marketing manager (“marketer”) with an MBA reporting

to you • He/she is not a data scientist

• The Marketing Department – Maintains the website

– Uses ConstantContact as a email campaign management system

– Has access to: • POS data & the customer database

– Has a Limited budget

– Has limited data centricity but a desire to transform the company into a data centric culture

19


Your goals:

1. Increase revenue 2. Increase efficiencies of marketing activities 3. Improve customer communications 4. Evolve into a data centric organization

• Here are the steps involved:

1. Gather data from current systems 2. Determine the product relationships 3. Determine the customer set that is most & least receptive 4. Determine the next product and price to be promoted via

email 5. Integrate back into current systems 6. Measure & improve results

20


Your Marketing Mix

4P’s

• Price

• Product

• Promotion

• Place

Which element(s) of the marketing mix is most effective increasing revenue?

21


Your Marketing Mix

4P’s

• Price

• Product

• Promotion

• Place

Which type of promotion is going to be

most effective?

Direct or indirect sales Advertising Marketing Promotions Events Direct marketing PR

Email !

Lucy has email addresses from all kinds of potential customers and a relatively small number of

actual customers

22


Which data to use?

• Do not use data from:

– Social Media • Facebook, Twitter, Blogs,

surveys, etc.

• Customer Sentiment

– Mobile Data

– Machine Data • RFID, sensors, etc.

– Images • Video, audio, emails

– “Real Time”

• Use data from:

– Customer transactions

– Legacy systems

– Web site: Google Analytics

23


Can A Spreadsheet Do The Analytics?

24


Prospect Customer Scores – Step Two

• Data needed

– ( Any demographic data is ok here, we can take advantage of a lot of disparate types of information)

– Customer ID

– Age

– Household income

Equation: Machine learning algorithm which mines customer demographic and descriptive data to determine which characteristics are indicators of success.

25


Product Relationships – Step Three

• Data needed

– Product transactions data

– Transactions in common

Equation: Determine which groups of products are highly correlated and purchased together.

26


Targeted Offers –

Step Four

Available Data:

• 10,880 email addresses

• 1,360 customers

– ∑ 4,080 transactions

(3 per customer)

– x transaction = $138.00 (2013)

• Days since last purchase • Purchase prices by product,

category and SKU number

27


Targeted Offers– Step Four

• Equation: Determines the similarity of market baskets by analyzing past customer behavior, and determining which products are most likely to be purchased next by each customer.

28


Targeted Offers – Step Four • Promotional offer:

– Who should receive these targeted offers? A select group of established customers

– How should the customer info be presented? Customer names and customer IDs

– What other information would be helpful to know? Lifetime value

Risk of Churn

• Validation

– Past KPIs (# of emails per period, opens, sales)

– Closed loop?

29


Demo

30


Discussion & Assessment

31


How YOU can apply Predictive Analytics

to Marketing Challenges

For the Planning-ness Conference May 10th, 2013

Doug Levin [email protected]

Quant5 planning ness-050613_final

Business

data centric business

avalanche of data

sources of data

data centric marketing

sap analytics

data scientists hours

business decisions

predictive analyticssource