Why Data is Sexy

[ Why data is sexy ] No really, it is!

Campaign data

Customer data

Compe5tor data

Consumer data

[ Main data categories ]

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Campaign data

Awareness Interest Desire Ac5on Sa5sfac5on

Online surveys, site polls

Web analy5cs

[ Defining analy:cs strategy ]


Social media

Search, display ads

Social media

[ Single source of truth ]

data

ROAS 14/11/12 5 © Datalicious Pty Ltd

[ De-‐duplica:on across channels ]


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaGorm

Google Analy:cs

$

$

$

Omniture PlaGorm

$

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$

[ De-‐duplica:on across channels ]


[ Success aIribu:on models ]

Banner Ad $100

Email Blast

Paid Search $100

Banner Ad $100

Affiliate Referral $100

Success $100

Success $100

Banner Ad

Paid Search

Organic Search $100

Success $100

Last channel gets all credit

First channel gets all credit

All channels get equal credit

Print Ad $33

Social Media $33

Paid Search $33

Success $100

All channels get par:al credit

Paid Search

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[ Campaign stacking by channel ]


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

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

[ Search call to ac:on ]

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[ Analyse channel overlap ]


Total Ac:ons: 2,023

Display Media 1669  (61.2%)

$125 -‐ $200 CPA

Paid Search 301 / 11.1%

$200 -‐ $500 CPA

189 (62%)

112 (38%)

Straight to Site 1208 (44.3%)

621 (52%)

587 (48%)

DM & EDMs

$200 -‐ $400 CTA

Radio &

Other ATL

Organic Search 493 (18%)

179 (37%)

314 (63%)

Partners

Call Centre

[ Cross-‐channel impact ]


[ Email iden:fica:on points ]

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Adver5sing Campaign

Cookie ID

Vodafone.com.au Research

Credit Check Fulfilment

Phone Conversion

Retail Conversion

Online Conversion





Online Order Confirma5on

Online Receipt Confirma5on



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Customer data

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[ Store locator searches ]


[ Reasons for store drop-‐out ]


About a third of those dropping out intend to come back again. 1 in 5 will go away and think more about their purchase. Only 10% have inten5ons purely to use the store for research.

[ Targe:ng framework ]


-‐12 -‐11 -‐10 -‐9 -‐8 -‐7 -‐6 -‐5 -‐4 -‐3 -‐2 -‐1 0 1 2 3 4 5 6 7 8 9 10 11 12

Weeks

Reten5on

Considera5on

Customer Profile

Prospect

Visitor Behaviour

Customer

[ Prospect targe:ng parameters ]


[ Affinity targe:ng in ac:on ] Different types of visitors respond to different ads. By using category affinity targe5ng, response rates are liced significantly across products.

Message Click-‐Through Rate By Category Affinity

Postpay Prepay Broadb. Business

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

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On-‐site segments

Off-‐site segments

[ Matching segments are key ]


On and off-‐site targe5ng plaeorms should use iden5cal triggers to sort visitors into segments

[ Combining data sets ]


one-‐off collec5on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira:on, etc predic5ve models based on data mining

propensity to buy, churn, etc historical data from previous transac5ons

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 promo5on responses

emails, internal search, etc

Site Behaviour

UPDATED CONTINUOUSLY

Omniture Test&Target

Google Analy5cs

Omniture DataWarehouse

Vodafone Terradata

Omniture DataWarehouse

Eyeblaster

Omniture Discover OnPremise

Online behavioural data stored in Omniture for anonymous prospects and known customers

Customer status and product affinity for prospects and customers passed into Test&Target as well as Eyeblaster for advanced on and off-‐site targe5ng (e.g. help/support messages for

exis5ng customers, acquisi5on messages with customized plans for prospects, etc)

Data on MyVodafone registra5on status to iden5fy non-‐users in order to encourage online self-‐service

Data on MyVodafone self-‐service usage to iden5fy users with poten5al issues in order to increase customer sa5sfac5on

Data on campaign performance to iden5fy most suitable message content and 5ming for each customer or segment

Data on product preferences and research behaviour to customize product offering and feed into churn modelling

Customer data on self-‐service usage, campaign responses and product preferences are filtered against Terradata in Discover OnDemand, contact lists are generated and customized messages delivered through the most suitable channels

[ Where is the money ]

[ october 2007 ]

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[ datalicious.com.au ]

[ Mosaic ]



Compe:tor data

[ Brand search volume ]


[ Market share trends ]


[ Australia vs. New Zealand ]

[ september 2007 ] [ datalicious.com.au ]

australia.com vs. newzealand.com


Consumer data

[ Search term volumes ]


[ Media planning ]


[ Leading lifestyles, 45+ ]


Campaign data

Customer data

Compe5tor data

Consumer data

[ Main data categories ]


Why Data is Sexy

Technology

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