Smart Marketing

> Smart Marke+ng < Smart data driven marke-ng

> Short but sharp history

§  Datalicious was founded late 2007 §  Strong Omniture web analy-cs history §  Now 360 data agency with specialist team §  Combina-on of analysts and developers §  Carefully selected best of breed partners §  Evangelizing smart data driven marke-ng § Making data accessible and ac-onable §  Driving industry best prac-ce (ADMA)

March 2011 © Datalicious Pty Ltd 2

> Clients across all industries


> Wide range of data services


Data Pla@orms Data collec+on and processing Web analy+cs solu+ons Omniture, Google Analy+cs, etc Tag-‐less online data capture End-‐to-‐end data pla@orms IVR and call center repor+ng Single customer view

Insights Repor+ng Data mining and modelling Customised dashboards Media aLribu+on models Market and compe+tor trends Social media monitoring Online surveys and polls Customer profiling

Ac+on Applica+ons Data usage and applica+on Marke+ng automa+on Alterian, Trac+on, Inxmail, etc Targe+ng and merchandising Internal search op+misa+on CRM strategy and execu+on Tes+ng programs

> Smart data driven marke+ng


Media ALribu+on

Op+mise channel mix

Tes+ng Improve usability

$$$

Targe+ng Increase relevance

Metric

s Framew

ork

Benchm

arking and

tren

ding

Metrics Fram

ework

Benchmarking and trending

> Metrics framework

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Awareness Interest Desire Ac+on Sa+sfac+on

> AIDA and AIDAS formulas


Social media

New media

Old media

Reach (Awareness)

Engagement (Interest & Desire)

Conversion (Ac-on)

+Buzz (Sa-sfac-on)

> Simplified AIDAS funnel


People reached

People engaged

People converted

People delighted

> Marke+ng is about people


40% 10% 1%

People reached

People engaged

People converted

People delighted

> Addi+onal funnel breakdowns


40% 10% 1%

New prospects vs. exis-ng customers

Brand vs. direct response campaign

AU/NZ vs. rest of world

> Importance of calendar events


Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless

Calendar events to add context


> Addi+onal success metrics


Click Through

Add To Cart

Click Through

Page Bounce

Click Through $

Click Through

Call back request

Store Search ? $

$

$ Cart Checkout

Page Views

?

Product Views

Level Reach Engagement Conversion +Buzz

Level 1, people

Level 2, strategic

Level 3, tac+cal

Funnel breakdowns

> Exercise: Metrics framework


> Single source of truth repor+ng


Insights Repor+ng

> Manual repor+ng s+ll prevails


> Media aLribu+on

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Direct mail, email, etc

Facebook TwiLer, etc

> Complex campaign flows


POS kiosks, loyalty cards, etc

CRM program

Home pages, portals, etc

YouTube, blog, etc

Paid search

Organic search

Landing pages, offers, etc

PR, WOM, events, etc

TV, print, radio, etc

= Paid media

= Viral elements

Call center, retail stores, etc

= Sales channels

Display ads, affiliates, etc

> Duplica+on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email Pla@orm

Web Analy+cs

$

$

$

> Cookie expira+on impact


Banner Ad Click

Email Blast

Paid Search

Organic Search

Bid Mgmt

Ad Server

Email Pla@orm

Google Analy+cs

$

$

$

$

Expira+on

Banner Ad View

Central Analy+cs Pla@orm

$

$

$

> De-‐duplica+on across channels


Banner Ads

Email Blast

Paid Search

Organic Search

$

> Exercise: Duplica+on impact §  Double-‐coun-ng of conversions across channels can

have a significant impact on key metrics, especially CPA §  Example: Display ads and paid search

–  Total media budget of $10,000 of which 50% is spend on paid search and 50% on display ads

–  Total of 100 conversions across both channels with a channel overlap of 50%, i.e. both channels claim 100% of conversions based on their own repor-ng but once de-‐duplicated they each only contributed 50% of conversions

–  What are the ini-al CPA values and what is the true CPA? §  Solu-on: $50 ini-al CPA and $100 true CPA

–  $5,000 / 100 = $50 ini-al CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase)


> First and last click aLribu+on


Chart shows percentage of channel touch points that lead to a conversion.

Neither first nor last-‐click measurement would provide true picture

Paid/Organic Search

Emails/Shopping Engines

Closer

SEM Generic

Banner View

TV Ad

> Full path to purchase


Influencer Influencer $

Banner Click Online

SEO Generic

Affiliate Click Offline

SEO Branded

Direct Visit

Email Update Abandon

Direct Visit

Social Media

SEO Branded

Introducer

> Understanding channel mix


> Adjus+ng for offline impact


+15 +5 +10 -‐15 -‐5 -‐10

> Targe+ng and tes+ng

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Capture internet traffic Capture 50-‐100% of fair market share of traffic

Increase consumer engagement Exceed 50% of best compe-tor’s engagement rate

Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to sales

Building consumer loyalty Build 60% loyalty rate and 40% sales conversion

Increase online revenue Earn 10-‐20% incremental revenue online

> Increase revenue by 10-‐20%


> New consumer decision journey


The consumer decision process is changing from linear to circular.

> New consumer decision journey


The consumer decision process is changing from linear to circular.

Change increases the importance of experience during research phase.

Online research

Off-‐site targe-ng

On-‐site targe-ng

Profile targe-ng

> Combining targe+ng pla@orms


On-‐site segments

Off-‐site segments

> Combining technology


CRM

> SuperTag code architecture


§  Central JavaScript container tag § One tag for all sites and plagorms §  Hosted internally or externally §  Faster tag implementa-on/updates §  Eliminates JavaScript caching §  Enables code tes-ng on live site §  Enables heat map implementa-on §  Enables redirects for A/B tes-ng §  Enables network wide re-‐targe-ng §  Enables live chat implementa-on

Campaign response data

> Combining data sets


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Maximise iden+fica+on points

20%

40%

60%

80%

100%

120%

140%

160%

0 4 8 12 16 20 24 28 32 36 40 44 48

Weeks

−−− Probability of iden-fica-on through Cookies

March 2011 36 © Datalicious Pty Ltd

> Sample customer level data


> Affinity re-‐targe+ng in ac+on


Different type of visitors respond to different ads. By using category affinity targe-ng, response rates are lijed significantly across products.

Message CTR By Category Affinity

Postpay Prepay Broadb. Business

Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +

Google: “vodafone omniture case study” or hLp://bit.ly/de70b7

> Ad-‐sequencing in ac+on


Marke-ng is about telling stories and

stories are not sta-c but evolve over -me

Ad-‐sequencing can help to evolve stories over -me the more users engage with ads

Purchase Cycle

Segments: Colour, price, product affinity, etc

Media Channels

Data Points

Default, awareness

Research, considera+on

Purchase intent

Reten+on, up/cross-‐sell

> Exercise: Targe+ng matrix


> Quality content is key

Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour

targe;ng pla<orm ;ck, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […. You feed your BT system crap and it will quickly and efficiently target crap to your

customers. Faster then you could ever have yourself.”


> ClickTale tes+ng case study


Test Segment Content KPIs Poten+al Results

> Exercise: Tes+ng matrix



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Data > Insights > Ac+on

Smart Marketing

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