ADMA Forum Behavioural Targeting Seminar

1. [ Data driven marketing ]
Reducing waste and increasing
relevance through targeting

2. [ Increase revenue by 10-20% ]
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By coordinating the consumer’s end-to-end experience,
companies could enjoy revenue increases of 10-20%.
Google: “get more value from digital marketing”
or http://bit.ly/cAtSUN
Source: McKinsey Quarterly, 2010

3. [ The consumer data journey ]
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To retention messagesTo transactional data
From suspect to To customer
From behavioural data From awareness messages
TimeTime
prospect

4. [ Coordination across channels ]
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Off-site targeting On-site targeting
Profile
targeting
Generating awareness Creating engagement Maximising revenue
TV, radio, print,
outdoor, search
marketing, 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

5. Off-site
targeting
On-site
targeting
Profile
targeting
[ Combining targeting platforms ]
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6. [ Targeting platforms ]
§ Off-site targeting
– Ad networks: Google, Yahoo, ValueClick, etc
– Ad servers: DoubleClick, Eyeblaster, Atlas, etc
§ On-site targeting
– Paid: Omniture Test&Target (Offermatica, TouchClarity),
Memetrics (Accenture), Optimost (Autonomy), Kefta
(Acxiom), AudienceScience, Maxymiser, Amadesa, etc
– Free: BTBuckets, Google Analytics, etc
§ Profile targeting
– Email platforms: Inxmail, Traction, Returnity, etc
– Marketing automation: Aprimo, Unica, Eloqua, etc
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7. On-site
segments
Off-site
segments
[ Combining technology platforms ]
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On and off-site targeting platforms should use
identical triggers to sort visitors into segments

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10. Customer data
[ Combining data sets ]
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3rd party data
+ The whole is greater
than the sum of its parts
Web analytics data

11. [ Behaviours plus transactions ]
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one-off collection of demographical data
age, gender, address, etc
customer lifecycle metrics and key dates
profitability, expiration, etc
predictive models based on data mining
propensity to buy, churn, etc
historical data from previous transactions
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 promotion responses
emails, internal search, etc
Site Behaviour
UPDATED CONTINUOUSLY

12. 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
overestimated visitors by up to 7.6 times whilst a cookie-based
approach overestimated visitors by up to 2.3 times.
Google: ”red eye cookie report pdf” or http://bit.ly/cszp2o
[ Overestimating unique visitors ]
Source: White Paper, RedEye, 2007

13. [ Maximise identification points ]
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
Cam
paign
response
Em
ailsubscription
Online
purchase
Repeatpurchase
Confirm
ation
em
ail
Em
ailnew
sletter
W
ebsite
login
Online
billpaym
ent
−−− Probability of identification through Cookies

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15. [ Sample site visitor composition ]
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30% existing customers with extensive
profile including transactional history of
which maybe 50% can actually be
identified 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

16. Phase Segment A Segment B Channels
Awareness
Consideration
Purchase Intent
Up/Cross-Sell
[ Developing a targeting matrix ]

17. Phase Segment A Segment B Channels
Awareness Seen this?
Social, display,
search, etc
Consideration 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 targeting matrix ]

18. Avinash Kaushik: “The principle of garbage in,
garbage out applies here. […] what makes a
behaviour targeting platform tick, 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 ]

19. 1. Define success metrics
2. Define and validate segments
3. Develop targeting and message matrix
4. Transform matrix into business rules
5. Develop and test content
6. Start targeting and automate
7. Keep testing and refining
8. Communicate results
[ Keys to effective targeting ]
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20. Google: “change one word double conversion”
or http://bit.ly/bpyqFp
[ ClickTale testing case study ]
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Email me
cbartens@datalicious.com
Talk to us
ADMA Forum Stand 347
Learn more
www.datalicious.com