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The presentation discusses the impact of data driven targeting in marketing campaigns.
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Data driven marke-ng Increasing campaign response rates
through data driven targe3ng
Datalicious company history • Datalicious was founded in 2007 • Strong Omniture web analy3cs history, now • One-‐stop data agency with specialist team • Combina3on of analysts and developers • Making data accessible and ac3onable • Driving industry best prac3ce • Evangelizing use of data
August 2010 © Datalicious Pty Ltd 2
Data driven marke-ng
August 2010 © Datalicious Pty Ltd 3
Media a8ribu-on
Op-mising channel mix
Tes-ng Improving usability
$$$
Targe-ng Increasing relevance
Increase revenue by 10-‐20%
August 2010 © Datalicious Pty Ltd 4
By coordina-ng the consumer’s end-‐to-‐end experience, companies could enjoy revenue increases of 10-‐20%.
Google: “get more value from digital marke-ng” or h8p://bit.ly/cAtSUN
Source: McKinsey Quarterly, 2010
The consumer data journey
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To reten-on messages To transac-onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
Coordina-on across channels
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Off-‐site targe-ng
On-‐site targe-ng
Profile targe-ng
Genera-ng awareness
Crea-ng engagement
Maximising revenue
TV, radio, print, outdoor, search marke3ng, display ads, performance networks, affiliates, social media, etc
Retail stores, call centers, brochures, websites, landing pages, mobile apps, online chat, etc
Outbound calls, direct mail, emails, SMS, etc
Off-‐site targe3ng
On-‐site targe3ng
Profile targe3ng
Combining targe-ng plaXorms
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On-‐site segments
Off-‐site segments
Combining technology plaXorms
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On and off-‐site targe-ng plaXorms should use iden-cal triggers to sort visitors into segments
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Campaign response data
Combining data sets
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
Behaviours plus transac-ons
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one-‐off collec3on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira-on, etc predic3ve models based on data mining
propensity to buy, churn, etc historical data from previous transac3ons
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 promo3on responses
emails, internal search, etc
Site Behaviour
UPDATED CONTINUOUSLY
Facebook as subscrip-on op-on
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Facebook Connect gives your company the following data and more with just one click! Email address, first name, last name, middle name, picture, affilia3ons, last profile update, 3me zone, religion, poli3cal interests, interests, sex, birthday, a\racted to which sex, why they want to meet someone, home town, rela3onship status, current loca3on, ac3vi3es, music interests, tv show interests, educa3on history, work history, family and ID
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Flowtown social profiling Name, age, gender, occupa-on, loca-on, social profiles and influencer ranking based on email
(influencers only)
(all contacts)
The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-‐in users, they provided a robust sample to compare IP-‐based and cookie-‐based analysis against. The results were staggering, for example an IP-‐based approach overes3mated visitors by up to 7.6 3mes whilst a cookie-‐based approach overes-mated visitors by up to 2.3 -mes. Google: ”red eye cookie report pdf” or h8p://bit.ly/cszp2o
Overes-ma-ng unique visitors
Source: White Paper, RedEye, 2007
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 iden3fica3on through Cookies
Sample site visitor composi-on
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30% exis-ng customers with extensive profile including transac3onal history of which maybe 50% can actually be iden3fied 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
Phase Segment A Segment B Channels
Awareness
Considera-on
Purchase Intent
Up/Cross-‐Sell
Developing a targe-ng matrix
Phase Segment A Segment B Channels
Awareness Seen this? Social, display, search, etc
Considera-on Great feature! Social, search, website, etc
Purchase Intent Great value! Search, site, emails, etc
Up/Cross-‐Sell Add this! Direct mail, emails, etc
Developing a targe-ng matrix
Affinity targe-ng in ac-on
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Different type of visitors respond to different ads. By using category affinity targe3ng, response rates are liied 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 h8p://bit.ly/de70b7
Poten-al newsle8er layout
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Closest stores, offers etc
Rule based header theme
Data verifica-on
Rule based offer
Profile based offer
Using data on website behaviour imported into the email delivery plajorm to build business rules to customise content delivery.
NPS
Poten-al landing page layout
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Branded header
Email or campaign message match
Targeted offers
Passing data on user preferences through to the website via parameters in email click-‐through URLs to customise content delivery.
Call to ac-on
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.”
Quality content is key
Google: “change one word double conversion” or h8p://bit.ly/bpyqFp
Tes-ng case study
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1. Define success metrics 2. Define and validate segments 3. Develop targe3ng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targe3ng and automate 7. Keep tes3ng and refining 8. Communicate results
Keys to effec-ve targe-ng
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August 2010 © Datalicious Pty Ltd 26
ADMA short course “Analyse to op-mise”
In Melbourne & Sydney October/November
By Datalicious
August 2010 © Datalicious Pty Ltd 27
Email me [email protected]
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