ADMA Marketing Data Strategy

> Marke(ng Data Strategy < 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 §  Driving industry best prac-ce (ADMA) §  Turning data into ac-onable insights §  Execu-ng smart data driven campaigns

May 2011 © Datalicious Pty Ltd 2

> Smart data driven marke(ng


Media A;ribu(on & Modeling

Op(mise channel mix, predict sales

Tes(ng & Op(misa(on Remove barriers, drive sales

Boost ROAS

Targeted Direct Marke(ng Increase relevance, reduce churn

> Wide range of data services


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

Insights Analy(cs Data mining and modelling Customised dashboards Tableau, SpoIire, SPSS, etc Media a;ribu(on models Market and compe(tor trends Social media monitoring Customer profiling

Ac(on Campaigns Data usage and applica(on Marke(ng automa(on Alterian, SiteCore, Inxmail, etc Targe(ng and merchandising Internal search op(misa(on CRM strategy and execu(on Tes(ng programs

> Clients across all industries


> Data driven marke(ng

§ What is data driven marke-ng? §  Self assessment: Your capabili-es §  Strategies for effec-ve data collec-on §  Campaign development and data integrity §  Effec-ve mul--‐channel campaign execu-on §  Analysis and performance measurement §  In-‐sourcing or outsourcing



Clive Humby: Data is the new oil

> Major data categories


Campaign data TV, print, call center, search, web analy-cs, ad serving, etc

Customer data Direct mail, call center, web analy-cs, emails, surveys, etc

Consumer data Geo-‐demographics, search, social, 3rd party research, etc

Compe(tor data Search, social, ad spend, 3rd party research, news, etc

Campaigns Customers

Compe(tors Consumers

> Corporate data journey


Time, Control

Soph

is-ca-o

n

Stage 1

Data Stage 2

Insights Stage 3 Ac(on

Third par-es control most data, ad hoc repor-ng only, i.e. what happened?

Data is being brought in-‐house, shi] towards insights genera-on and data mining, i.e. why did it happen?

Data is fully owned in-‐house, advanced predic-ve modelling and trigger based marke-ng, i.e. what will happen and making it happen!



Oil and data come at a price

> Google Ngram: Privacy


May 2011 © Datalicious Pty Ltd

Collec(ng data for the sake of it or to add value to customers?

13

> Privacy vs. data benefits policy

§  Do not hide behind small print §  Use plain English in your privacy policy §  Explain exactly what data you are recording §  Explain why you are recording the data §  Explain the benefits for the consumer §  Provide opt-‐out and feedback op-ons § Make opt-‐outs a KPI not just opt-‐ins = Data benefits and privacy policy


Exercise: Marke(ng mix


Marke(ng

Mix

Product

Price

Place

Promo(on

Physical Evidence

People

Process

Partners

The right message Via the right channel To the right person At the right -me

Targe(ng


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


> Coordina(on across channels


Off-‐site targe(ng

On-‐site targe(ng

Profile targe(ng

Genera(ng awareness

Crea(ng engagement

Maximising revenue

TV, radio, print, outdoor, search marke-ng, display ads, performance networks, affiliates, social media, etc

Retail stores, in-‐store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc

Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc

Off-‐site targe-ng

On-‐site targe-ng

Profile targe-ng

> Combining targe(ng plaIorms


November 2010 © Datalicious Pty Ltd 24


Take a closer look at our cash flow solu(ons

> Affinity re-‐targe(ng in ac(on


Different type of visitors respond to different ads. By using category affinity targe-ng, response rates are li]ed 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 h;p://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

> Prospect targe(ng parameters



> Sample site visitor composi(on


30% exis(ng customers with extensive profile including transac-onal history of which maybe 50% can actually be iden-fied as individuals

30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful

10% serious prospects with limited profile data

30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity

> Search call to ac(on for offline



> PURLs boos(ng DM response rates


Text

> Unique phone numbers

§  1 unique phone number –  Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interac-on unknown

§  2-‐10 unique phone numbers – Different numbers for different media channels –  Exclusive number(s) reserved for website use –  Call origin data more granular but not perfect – Difficult to rotate and pause numbers


> Unique phone numbers §  10+ unique phone numbers

– Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps –  Call origin becoming useful to shape call script –  Feasible to pause numbers to improve integrity

§  100+ unique phone numbers – Different numbers for different website visitors –  Call origin and -me stamp enable individual match –  Call conversions matched back to search terms


> Jet Interac(ve phone call data


> Poten(al calls to ac(on §  Unique click-‐through URLs §  Unique vanity domains or URLs §  Unique phone numbers §  Unique search terms §  Unique email addresses §  Unique personal URLs (PURLs) §  Unique SMS numbers, QR codes §  Unique promo-onal codes, vouchers §  Geographic loca-on (Facebook, FourSquare) §  Plus regression analysis of cause and effect


Calls to ac(on can help shape the customer experience not just evaluate responses

> The consumer data journey


To reten(on messages To transac(onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

Campaign response data

> Combining data sources


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Transac(ons plus behaviours


+ one-‐off collec-on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira(on, etc predic-ve models based on data mining

propensity to buy, churn, etc historical data from previous transac-ons

average order value, points, etc

CRM Profile

Updated Occasionally

tracking of purchase funnel stage

browsing, checkout, etc tracking of content preferences

products, brands, features, etc tracking of external campaign responses

search terms, referrers, etc tracking of internal promo-on responses

emails, internal search, etc

Site Behaviour

Updated Con(nuously

> Customer profiling in ac(on


Using website and email responses to learn a lille bite more about

subscribers at every touch point to keep

refining profiles and messages.

> Online form best prac(ce


Maximise data integrity Age vs. year of birth Free text vs. op-ons

Use auto-‐complete wherever possible

Exercise: Enriching profiles


> Exercise: Enriching profiles


+ CRM Profile

?

Site Behaviour

?

Exercise: Customer IDs


> Exercise: Customer IDs


To reten(on messages To transac(onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

Geo-‐demographic data

> Enhancing data sources


3rd party data

+ The whole is greater than the sum of its parts

Customer profile data

> Geo-‐demographic segments




Event sponsor presenta(on

transcape

data solu,ons

Magazine Subscribers Mail Order Catalog Buyers E-‐commerce customers

Buyer File

1 Buyer File

2

Buyer File

3

Buyer File

4 Buyer File

5

Buyer File

6

Buyer File

7

transcape

"IMP have been working with Alliance Data ever since they launched and have using their Australian & NZ data with great success across a range of products"

Victoria Coleman

Media Manager

International Masters Publishers

transcape Selectable by:

Recency Money Frequency

Recency Count0 to 6 mo. 371,0126 to 12 mo. 269,45712 to 18 mo. 295,60118 to 24 mo. 397,162Total 1,333,232

Frequency Count1 x 734,4362 x 206,2573x 110,7514+ 281,788Total 1,333,232

Spend CountLess Than $25 138,346$25 -‐ $50 131,671$50 -‐ $100 324,512$100 -‐ $250 329,338$250+ 409,365Total 1,333,232

transcape Selectable by:

Female Male

Gender Age Income

0-‐6 mo. 7-‐12 mo. 13-‐24 mo. 25-‐36 mo. 37mo.+

<$10

$10-‐$24

$25-‐$49

$50-‐$99

$100-‐$249

$250+

0.10%

3.00%+

2.50%

2.00%

1.80%

1.50%

1.20% 0.30% 0.50% 0.70%

2.20%

2.10%

2.00%

1.70%

1.50%

1.40%

1.20%

1.20%

1.10%

0.70%

0.90%

1.00%

0.80%

0.50%

0.70%

0.50%

0.40%

0.40%

0.30%

0.20%

450,000 Buyers

RFM Segmenta(on (house file)

50,000 Buyers

50,000 Buyers

1 .4 million names

Last bought from YOU 25-‐36 mo., $25-‐$49

Response Rate = 0.50%

35,000 matches

transcape

Last bought from you 25-‐36 mo., $25-‐$49

Response Rate =

Universe =

0.50%

50,000

Have also bought elsewhere

35,000

Recency

Frequency =

Value 0-‐12 mo. 12-‐24 mo. 25+ mo.

<$25

$25-‐49

$50-‐$99

$100+

0.10% 0.30% 0.50%

0.70%

0.90%

1.10%

1x 2x 3x 1+

0.50% 0.30%

0.70% 0.50%

0.90% 0.70%

20,000

0.90%

Further op(mise your house file segments

Transac(onal Data Demographic Data Geographic Data

Non RFM Transactional Profile

Geographic Profile

State transcape % Client % Standard

IndexNormalised

IndexACT 1.66 0.00 0.00NSW 32.60 0.13 0.39 -7.80NT 1.06 0.00 0.00QLD 21.23 0.06 0.30 -4.12SA 7.97 0.00 0.00TAS 2.33 0.00 0.00VIC 21.82 0.13 0.58 -5.91WA 11.33 99.68 879.72 778.90

Metro / Rural transcape % Client % Standard

IndexNormalised

IndexMetro 62.32 87.90 141.03 122.21Rural 37.68 12.10 32.13 -54.25

Other Indicators

Gender transcape % Client % Standard

IndexNormalised

IndexFemale 66.29 63.94 96.45 -8.90Male 25.98 30.10 115.86 10.82Unknown 7.73 5.96 77.10 -4.04

Geodemographic Profile

GeoSmart Groups

# Description transcape % Client % Standard

IndexNormalised

Index1 High Status Stronger Family 15.11 34.28 226.88 44.392 High Status Weaker family 5.18 5.51 106.38 0.763 Mid Status Stronger Family 24.90 25.54 102.55 1.644 Mid Status Weaker family 4.83 6.97 144.43 4.795 Low Status Stronger Family 25.02 10.46 41.79 -31.286 Low Status Weaker family 9.51 11.47 120.66 4.617 Disadvantaged 13.70 4.69 34.24 -16.888 Unclassified 1.75 1.08 61.50 -1.44

GeoSmart Segments

# Description transcape % Client % Standard

IndexNormalised

Index1 Prestige 1.41 5.45 387.36 6.462 High Status Urban 1.11 0.63 56.99 -1.003 Desirable Suburban 2.36 7.35 310.86 8.824 Affluent Family 1.95 3.68 188.64 3.575 High Density Urban 1.07 0.44 41.39 -1.196 Urban Bohemian 1.25 0.32 25.28 -1.457 Affluent Multicultural 1.23 3.11 252.98 3.528 High Status Suburban 2.39 7.48 313.22 8.999 Coastal Emplty Nest & Retirement 1.92 1.77 92.26 -0.34

10 Desirable Urban 1.75 4.12 235.94 4.5911 High Status Family 2.80 0.63 22.65 -3.2212 Mature Affluent Suburban 1.06 4.82 456.31 5.5713 Aspiring Family 2.89 3.11 107.37 0.4814 Mid Status Family Starter 1.69 1.65 97.77 -0.0815 Affluent Seachange 1.64 1.20 73.50 -0.9616 Established Multicultural Suburban 2.70 1.90 70.43 -1.7517 Urban Lifestyle 0.68 0.70 102.68 0.0418 Mid Status Suburban 3.01 5.32 176.69 4.9019 Provincial Fringe 2.11 0.82 39.08 -2.3920 Metro Fringe 0.87 1.90 218.14 2.0221 Mid Status Urban 1.66 4.75 285.75 5.5822 Mixed Multicultural Suburban 0.87 0.13 14.49 -0.8923 Mining 1.41 1.52 107.92 0.2524 Mid Status Young Family 3.21 1.39 43.40 -3.5325 Mature Mid Status Family 4.50 6.59 146.45 4.6526 Multicultural Urban Lifestyle 0.57 0.00 0.0027 University Enclaves 0.36 0.51 139.57 0.3128 Holiday Lifestyle 0.45 0.25 56.21 -0.4129 Multicultural Mixed Urban 1.10 0.76 69.17 -0.7430 Establishing Multicultural Family 1.98 0.82 41.66 -2.1931 Elderly Enclaves 1.15 0.51 44.19 -1.2432 Establishing Provincial family 2.91 1.20 41.44 -3.2433 New Age Lifestyle 0.91 0.95 105.02 0.1034 Mature Provincial Suburban 4.30 1.01 23.60 -5.0135 Mixed Suburban 0.96 0.19 19.82 -1.0736 Inland Rural Fringe 1.43 3.36 234.37 3.7237 Established Multicultural family 1.31 0.13 9.69 -1.1638 Provincial Mixed Urban 2.17 0.82 37.94 -2.4839 Low Status Rural Fringe 1.93 0.51 26.25 -2.2540 Family Achiever 1.91 0.51 26.59 -2.2341 Old European Blue Collar 1.03 0.95 91.95 -0.1942 Established Blue Collar Suburban 3.04 6.59 217.10 7.1543 Blue Collar Family 3.10 2.60 83.72 -1.1444 Provincial Blue Collar Suburban 5.65 1.77 31.43 -6.7345 Middle Eastern Multicultural 0.77 0.00 0.0046 Poor Mixed Urban 1.14 0.82 72.07 -0.7047 Low Status Mixed Multicultural 1.39 0.70 50.00 -1.4048 Small Town Blue Collar Suburban 4.39 1.58 36.10 -5.1249 Established Asian 0.60 0.00 0.0050 Mobile Holiday Accommodation 0.21 0.13 60.52 -0.1751 Elderly Provincial Urban 2.04 0.70 34.12 -2.3852 Provincial Battler 2.93 0.76 25.98 -3.4353 High Density Welfare 0.16 0.00 0.0054 Suburban Welfare 0.83 0.00 0.0055 Indigenous & Remote 1.62 1.08 66.49 -1.1756 Unclassified 0.13 0.00

transcape

data solu,ons

Thank you!

Exercise: Targe(ng matrix


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


Purchase Cycle

Segments: Colour, price, product affinity, etc

Media Channels

Data Points

Default, awareness

Have you seen A?

Have you seen B?

Display, search, etc Default

Research, considera(on

A has great features!

B has great features!

Search, website, etc

Ad clicks, prod views

Purchase intent

A delivers great value!

B delivers great value!

Website, emails, etc

Cart adds, checkouts

Reten(on, up/cross-‐sell

Why not buy B?

Why not buy A?

Direct mails, emails, etc

Email clicks, logins, etc

> Exercise: Targe(ng matrix






Exercise: Marke(ng automa(on



> 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.”


Plan to fail … May 2011 © Datalicious Pty Ltd 72

Test Segment Content KPIs Poten(al Results

> Develop a tes(ng matrix


Test Segment Content KPIs Poten(al Results

Test #1A New prospects

Conversion form A

Next step, order, etc ? ?

Test #1B New prospects

Conversion form B


Test #1N New prospects

Conversion form N


? ? ? ? ? ?

> Develop a tes(ng matrix


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


New vs. returning visitors


AU/NZ vs. rest of world

> Poten(al funnel breakdowns §  Brand vs. direct response campaign §  New prospects vs. exis-ng customers §  Baseline vs. incremental conversions §  Compe--ve ac-vity, i.e. none, a lot, etc §  Segments, i.e. age, loca-on, influence, etc §  Channels, i.e. search, display, social, etc §  Campaigns, i.e. this/last week, month, year, etc §  Products and brands, i.e. iphone, htc, etc §  Offers, i.e. free minutes, free handset, etc §  Devices, i.e. home, office, mobile, tablet, etc May 2011 © Datalicious Pty Ltd 81

Level Reach Engagement Conversion +Buzz

Level 1, people

Level 2, strategic

Level 3, tac(cal

Funnel breakdowns

> Developing a metrics framework


Level Reach Engagement Conversion +Buzz

Level 1 People

People reached

People engaged

People converted

People delighted

Level 2 Strategic

Display impressions ? ? ?

Level 3 Tac(cal

Interac(on rate, etc ? ? ?

Funnel Breakdowns Exis(ng customers vs. new prospects, products, etc

> Developing a metrics framework


> Establishing a baseline


Switch all adver-sing off for a period of -me (unlikely) or establish a smaller control group that is representa-ve of the en-re popula-on (i.e. search term, geography, etc) and switch off selected channels one at a -me to minimise impact on overall conversions.

> Importance of calendar events


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

> Out-‐sourcing or in-‐sourcing?


Time, Control

Degree of in-‐ho

use control and

soph

is-ca-o

n

Year 1

PlaIorms Year 2

Training Year 3 Support

Engage third par-es with more experience to get started and to implement technology

Start taking control of technology and data, shi] vendor focus to enhancements and the provision of training for internal resources

Reduce vendor reliance to absolute minimum but consider the value of support agreements for both maintenance as well as updates on market innova-ons and new features.


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Data > Insights > Ac(on

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ADMA Marketing Data Strategy