adma data day marketing data strategy workshop

8/6/2019 ADMA Data Day marketing data strategy workshop

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> Marke(ng Data Strategy 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


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> Wide range of data services


DataPla ormsData 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

InsightsAnaly(csData mining and modelling

Customised dashboards

Tableau, Spo ire, SPSS, etc

Media a;ribu(on models

Market and compe(tor trends

Social media monitoring

Customer proling

Ac(onCampaignsData 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


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> Clients across all industries



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> 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



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Clive Humby: Data is the new oil


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> Major data categories


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

Customer data

Direct mail, call center, webanaly-cs, emails, surveys, etc

Consumer dataGeo demographics, search,social, 3rd party research, etc

Compe(tor dataSearch, social, ad spend, 3rd

party research, news, etc

Campaigns Customers

Compe(tors Consumers


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> Corporate data journey


Time, Control

S o p h i s - c a - o n

Stage 1

DataStage 2

InsightsStage 3

Ac(on

Third par-es control

most data, ad hocrepor-ng only, i.e.what happened?

Data is being broughtin house, shi] towardsinsights genera-on and

data mining, i.e. whydid it happen?

Data is fully ownedin house, advancedpredic-ve modellingand trigger basedmarke-ng, i.e. whatwill happen andmaking it happen!


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Oil and data come at a price


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> Google Ngram: Privacy



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May 2011 Datalicious Pty Ltd

Collec(ng data

for the sake of itor to add value

to customers?

13


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> Privacy vs. data benets 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 benets for the consumer Provide opt out and feedback op-ons Make opt outs a KPI not just opt ins

= Data benets and privacy policy



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Exercise: Marke(ng mix



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Marke(ng

Mix

Product

Price

Place

Promo(on

PhysicalEvidence

People

Process

Partners


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The right message

Via the right channelTo the right personAt the right -me

Targe(ng



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Capture internet trafficCapture 50 100% of fair market share of traffic

Increase consumer engagementExceed 50% of best compe-tors engagement rate

Capture qualied leads and sellConvert 10 15% to leads and of that 20% to sales

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

Increase online revenueEarn 10 20% incremental revenue online

> Increase revenue by 10-20%



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> New consumer decision journey


The consumer decision process is changing from linear to circular.


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> New consumer decision journey


The consumer decision process is changing from linear tocircular .

Change increasesthe importance ofexperience duringresearch phase.

Online research


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> Coordina(on across channels


Off-sitetarge(ng

On-sitetarge(ng

Proletarge(ng

Genera(ngawareness

Crea(ngengagement

Maximisingrevenue

TV, radio, print,outdoor, searchmarke-ng, displayads, performancenetworks, affiliates,social media, etc

Retail stores, in storekiosks, call centers,brochures, websites,mobile apps, onlinechat, social media, etc

Outbound calls, directmail, emails, socialmedia, SMS, mobileapps, etc


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Off sitetarge-ng

On site

targe-ng

Prole

targe-ng

> Combining targe(ng pla orms



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November 2010 Datalicious Pty Ltd 24


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Take a closerlook at ourcash owsolu(ons


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> Affinity re-targe(ng in ac(on


Different type ofvisitors respond todifferent ads. Byusing categoryaffinity targe-ng,response rates are

li]ed signicantlyacross products.

MessageCTR By Category Affinity

Postpay Prepay Broadb. Business

Blackberry Bold - - - +

5GB Mobile Broadband - - + -

Blackberry Storm + - + +

12 Month Caps - + - +

Google: vodafoneomniture case study

or h;p://bit.ly/de70b7


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> Ad-sequencing in ac(on


Marke-ng is abouttelling stories and

stories are not sta-cbut evolve over -me

Ad sequencing can help toevolve stories over -me themore users engage with ads


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> Prospect targe(ng parameters



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> Sample site visitor composi(on


30% exis(ng customers with extensive

prole including transac-onal history ofwhich maybe 50% can actually beiden-ed as individuals

30% new visitors with noprevious website historyaside from campaign orreferrer data of whichmaybe 50% is useful

10% serious

prospectswith limitedprole data

30% repeat visitors withreferral data and somewebsite history allowing50% to be segmented bycontent affinity


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> Search call to ac(on for offline



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> PURLs boos(ng DM response rates


Text


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> 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



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> 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



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> Jet Interac(ve phone call data



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> 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(oncan help shapethe customerexperience not

just evaluateresponses


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> The consumer data journey


To reten(on messagesTo transac(onal data

From suspect to Tocustomer

From behavioural data From awareness messages

TimeTime

prospect


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Campaign response data

> Combining data sources


Customer prole data

+ The whole is greaterthan the sum of its partsWebsite behavioural data


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> Transac(ons plus behaviours


+one off collec-on of demographical data

age, gender, address, etccustomer lifecycle metrics and key dates

protability, expira(on, etcpredic-ve models based on data mining

propensity to buy, churn, etchistorical data from previous transac-ons

average order value, points, etc

CRM Prole

Updated Occasionally

tracking of purchase funnel stage

browsing, checkout, etctracking of content preferences

products, brands, features, etctracking of external campaign responses

search terms, referrers, etctracking of internal promo-on responses

emails, internal search, etc

Site Behaviour

Updated Con(nuously


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> Customer proling in ac(on


Using website and email responsesto learn a li le bite more about

subscribers at everytouch point to keep

rening prolesand messages.


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> Online form best prac(ce


Maximise data integrityAge vs. year of birthFree text vs. op-ons

Use auto-completewherever possible


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Exercise: Enriching proles



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> Exercise: Enriching proles


+CRM Prole

?

Site Behaviour

?


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Exercise: Customer IDs



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> Exercise: Customer IDs


To reten(on messagesTo transac(onal data

From suspect to Tocustomer

From behavioural data From awareness messages

TimeTime

prospect


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Geo-demographic data

> Enhancing data sources


3rd party data

+ The whole is greaterthan the sum of its partsCustomer prole data


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> Geo-demographic segments



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Event sponsor presenta(on


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trans capedata solu,ons

M i S b ib

M il O d C l B

E


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Magazine SubscribersMail Order Catalog BuyersE-commerce customers


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Buyer File

1Buyer File

2

Buyer File

3

Buyer File

4Buyer File

5

Buyer File

6

Buyer File

7

trans cape

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

Victoria Coleman

Media Manager International Masters Publishers


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trans capeSelectable by:

Recency MoneyFrequency

Recency Count

0 to 6 mo. 371,012

6 to 12 mo. 269,457

12 to 18 mo. 295,601

18 to 24 mo. 397,162Total 1,333,232

Frequency Count

1 x 734,436

2 x 206,257

3x 110,751

4+ 281,788Total 1,333,232

Spend Count

Less Than $25 138,346$25 - $50 131,671$50 - $100 324,512$100 - $250 329,338

$250+ 409,365Total 1,333,232


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trans capeSelectable by:

Female Male

GenderAgeIncome


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


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50,000 Buyers

1 .4 million names

Last bought from YOU25-36 mo., $25-$49

Response Rate = 0.50%

35,000matches

trans cape

0 500 0


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Last bought from you25-36 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.


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Transac(onal DataDemographic DataGeographic Data

d h f lGeoSmart Segments


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Geodemographic Profile

GeoSmart Groups

# Description trans cape % Client %Standard

IndexNormalised

Index1 High Status Stronger Family 15.11 34.28 226.88 44.39

2 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

# De scr ip ti on trans c ap e % C li ent %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


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trans capedata solu,onsThank you!


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Exercise: Targe(ng matrix



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PurchaseCycle

Segments: Colour, price,product affinity, etc

MediaChannels

DataPoints

Default,awareness

Research,considera(on

Purchaseintent

Reten(on,up/cross-sell

> Exercise: Targe(ng matrix



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PurchaseCycle

Segments: Colour, price,product affinity, etc

MediaChannels

DataPoints

Default,awareness

Have youseen A?

Have youseen B?

Display,search, etc Default

Research,considera(on

A has greatfeatures!

B has greatfeatures!

Search,website, etc

Ad clicks,prod views

Purchaseintent

A deliversgreat value!

B deliversgreat value!

Website,emails, etc

Cart adds,checkouts

Reten(on,up/cross-sell

Why notbuy B?

Why notbuy A?

Direct mails,emails, etc

Email clicks,logins, etc

> Exercise: Targe(ng matrix



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> Quality content is key

Avinash Kaushik:The principle of garbage in garbage outapplies here. [ what makes a behaviour

targe,ng pla=orm ,ck and produce results isnot 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 yourcustomers. Faster then you could

ever have yourself.



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Plan to fail May 2011 Datalicious Pty Ltd 72


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Test Segment Content KPIs Poten(al Results

> Develop a tes(ng matrix



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Test Segment Content KPIs Poten(al Results

Test #1ANew

prospectsConversion

form ANext step,order, etc ? ?

Test #1BNew

prospectsConversion

form BNext step,order, etc ? ?

Test #1NNew

prospectsConversion

form NNext step,order, etc ? ?

? ? ? ? ? ?

> Develop a tes(ng matrix



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Awareness Interest Desire Ac(on Sa(sfac(on

> AIDA and AIDAS formulas


Social media

New media

Old media


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Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted

> Marke(ng is about people


40% 10% 1%


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Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted

> Addi(onal funnel breakdowns


40% 10% 1%

New prospects vs. exis-ng customers

Brand vs. direct response campaign


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New vs. returning visitors


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AU/NZ vs. rest of world


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> 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, inuence, 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



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Level Reach Engagement Conversion +Buzz

Level 1,people

Level 2,strategic

Level 3,tac(cal

Funnelbreakdowns

> Developing a metrics framework



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Level Reach Engagement Conversion +Buzz

Level 1People

Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted

Level 2Strategic

Displayimpressions ? ? ?

Level 3Tac(cal

Interac(onrate, etc ? ? ?

FunnelBreakdowns Exis(ng customers vs. new prospects, products, etc

> Developing a metrics framework



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> Establishing a baseline


Switch all adver-sing off for a periodof -me (unlikely) or establish a smallercontrol group that is representa-ve of

the en-re popula-on (i.e. search term,geography, etc) and switch off selectedchannels one at a -me to minimiseimpact on overall conversions.


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> Importance of calendar events


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


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> Out-sourcing or in-sourcing?


Time, Control

D e g r e e o

f i n

h o u s e c o n t r o l a n d s o p h i s - c a - o n

Year 1

Pla ormsYear 2

TrainingYear 3

Support

Engage third par-eswith more experienceto get started and toimplement technology

Start taking control oftechnology and data,shi] vendor focus toenhancements and theprovision of trainingfor internal resources

Reduce vendor relianceto absolute minimumbut consider the valueof support agreementsfor both maintenanceas well as updates on

market innova-ons andnew features.


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Contact [email protected]

Learn moreblog.datalicious.com

Follow metwi;er.com/datalicious


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Data > Insights > Ac(on

adma data day marketing data strategy workshop

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