Data Driven Targeting - Behavioural Targeting
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[ Data driven marke.ng ] Reducing waste and increasing relevance through targe3ng
[ Using data to reduce waste ]
August 2010 © Datalicious Pty Ltd 2
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 3
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 ]
August 2010 © Datalicious Pty Ltd 4
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 ]
August 2010 © Datalicious Pty Ltd 5
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 plaZorms ]
August 2010 © Datalicious Pty Ltd 6
[ Targe.ng plaZorms ]
§ Off-‐site targe3ng – Ad networks: Google, Yahoo, ValueClick, etc – Ad servers: DoubleClick, Eyeblaster, Atlas, etc
§ On-‐site targe3ng – Paid: Omniture Test&Target (Offerma3ca, TouchClarity), Memetrics (Accenture), Op3most (Autonomy), Ke[a (Acxiom), AudienceScience, Maxymiser, Amadesa, etc
– Free: BTBuckets, Google Analy3cs, etc § Profile targe3ng – Email pla^orms: Inxmail, Trac3on, Returnity, etc – Marke3ng automa3on: Aprimo, Unica, Eloqua, etc
August 2010 © Datalicious Pty Ltd 7
On-‐site segments
Off-‐site segments
[ Combining technology plaZorms ]
August 2010 © Datalicious Pty Ltd 8
On and off-‐site targe.ng plaZorms should use iden.cal triggers to sort visitors into segments
August 2010 © Datalicious Pty Ltd 9
August 2010 © Datalicious Pty Ltd 10
Customer data
[ Combining data sets ]
August 2010 © Datalicious Pty Ltd 11
3rd party data
+ The whole is greater than the sum of its parts
Web analy.cs data
[ Behaviours plus transac.ons ]
August 2010 © Datalicious Pty Ltd 12
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
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
August 2010 © Datalicious Pty Ltd 15
Datalicious SuperCookie Persistent Flash cookie that cannot be deleted
August 2010 © Datalicious Pty Ltd 16
[ Sample site visitor composi.on ]
August 2010 © Datalicious Pty Ltd 17
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 ]
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 ]
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 ]
August 2010 © Datalicious Pty Ltd 21
Google: “change one word double conversion” or h8p://bit.ly/bpyqFp
[ ClickTale tes.ng case study ]
August 2010 © Datalicious Pty Ltd 22
August 2010 © Datalicious Pty Ltd 23
ADMA short course “Analyse to op.mise”
In Melbourne & Sydney October/November
By Datalicious
August 2010 © Datalicious Pty Ltd 24
Email me [email protected]
Talk to us
ADMA Forum Stand 347
Learn more www.datalicious.com