1. Cutting through the NOISE!!
Applications of data mining and predictive analytics
A li ti fd t ii d di ti l ti
Neil Mason, Applied Insights
Emetrics, San Francisco, May 2008

2. The work we do in the online
space
Analytics strategy
development and
implementation
Systems selection &
implementation
Site and Customer
Analytics

3. The work we do in the online
space
Analytics strategy
development and
implementation
Systems selection &
implementation
Site and Customer
Analytics

4. The work we do in the online
space
Analytics strategy
development and
implementation
Systems selection &
implementation
Site and Customer
Analytics

5. The work we do in the online
space
Analytics strategy
development and
implementation
Systems selection &
implementation
Site and Customer
Analytics

6. The work we do in the online
space
Analytics strategy
development and
implementation
Systems selection &
implementation
Site and Customer
Analytics

7. The challenge…
Survey
Data
Promotion
Ad- Affiliates data
GRP data serving
data
Email
data
Customer
You
Performance
data
data
Transactions
T ti
ISP data
PPC data Web
analytics
y
Panel
data
Analyst Offline
data sales
data

8. Web
Survey Panel data Customer
analytics
d
data data
d
data

9. Rabbits in headlights…

10. Rabbits in headlights…

12. The response?
Data integration
Better query engines
Data mining and predictive
analytics
y

13. What do we mean by data mining
and predictive analytics?
Predictive
Data mining
analytics
Discovering previously
undetected patterns and
Applying historical patterns to
relationships in data
predict future outcomes

14. Application of predictive analytics
Number of
tracks
Day of
Country
presentation
Length of Time of
conference presentation
Expected
Size of
size of After lunch?
conference
audience

15. Application of predictive analytics
Number of
tracks
Day of
Country
presentation
Length of Time of
conference presentation
4
Size of
After lunch?
conference

16. Predictive Analytics - Techniques
• Statistics
• e.g. Regression
• Artificial intelligence
• e.g. N
Neural N t
l Networks
k
• Hybrid
• e.g. D i i t
Decision trees
• Optimisation
• e g Monte Carlo Simulation
e.g. Simulation,

17. The data mining process
(CRISP-DM)
Business
Data
Understanding
Understanding
Data
Preparation
Deployment
Modelling
Evaluation

18. The data mining process
(CRISP-DM)
Business
Data
Understanding
Understanding
Data
Preparation
Deployment
Modelling
Evaluation

20. Some applications of data mining and
predictive analytical techniques
Segmentation
S t ti
Propensity modelling
Econometrics and forecasting
Anomaly detection

21. Some applications of data mining and
predictive analytical techniques
Segmentation
S t ti
Propensity modelling
Econometrics and forecasting
Anomaly detection

22. Who are your visitors?
Applications of visitor segmentation techniques

24. Creating meaningful segments
• Demographic
• Gender, age etc
g
• Lifestyle
• Behavioural
•BBrowsing
i
• Purchasing
• Response
• Attitudinal
• Brand empathy
• Satisfaction

25. Creating meaningful segments
• Demographic
• Gender, age etc
g
• Lifestyle
• Behavioural
•BBrowsing
i
• Purchasing
• Response
• Attitudinal
• Brand empathy
• Satisfaction

26. Behavioural segmentation
strategies
Deterministic Discovery based
Rules Associations
Hierarchies Patterns
Filters Correlations

27. The framework…
Who visits the Why do they visit What do they do on
site? the site and what the site?
do they think of it?
?
?
?
?

28. Developing the visitor segments
Behavioural segmentation
based on content
b d tt
consumption
Segments profiled using other
behavioural data and also additional
survey and/or customer data

29. Segmentation using cluster
analysis
Behavioural data
Vis123
Vis124
Vis125
Vis126
Vis127
Vis128
Vis129
Vis130
Vis131

30. Building the visitor profile…
Profiling data
Behavioural data Attitudinal data
Vis128
Vis130
Vis124
Vis123
Vis126
Vis127
Vis131
Vis129
Vis125

31. Happy Trackers (6%)
Happy Trackers mainly use the site for Track and
Trace and little else
In terms of profile they tend to have a stronger
business slant and be slightly older than on
average
g
They are not heavy users of the site and their
visits are relatively light and narrow – all they do is
use Track and Trace
However they are happy with what they do, they
rate the site functionality the best out of all the
segments

32. Happy Trackers– 6%, Occasional
information
Top content Top searches Top campaigns
• Track & trace • Redirections • Redelivery
• Redirections • Recorded delivery • XMAS
• Customer services • Redeli er
Redelivery •SSmartstamp
tt
• Delivery services
• 9th highest number of visits Key behaviours
• 4th most buyers; redirections
• Key demographics & attitudes
• Older
• More business than personal
• Satisfaction above par
• Highest site rating
• Stated reasons for visit: Track & Trace

33. Price Finders (10%)
Price Finders are primarily concerned about
finding our information on things like airmail
services and prices as well as other delivery
services and costs
Quite often their visit has something to do with an
online auction activity but they are possibly new to
the game as this segment generally haven t visited
haven’t
the site very often and a large proportion of them
are new to the site

34. Cottage Industrialists (2%)
Cottage Industrialists are frequent users of the site
and they mainly come looking for information on
postal prices, delivery services, parcel information
and the like.
Half of this segment are involved in some type of
online auction related activity and over the course
of their lifetime they tend to look at the broadest
amount of content on the site. Quite often they will
be using the search function to do this
They are reasonably happy with the customer
experience on the site and are more likely than on
average to recommend the site to others

35. Regular Posters (1%)
A small but valuable segment
Regular Posters are frequent visitors to the site
and are mainly buying stamps via online postage.
The vast majority of this group actually bought
something d i th period
thi during the id
This segment has a slightly more older male
profile and is more likely to be coming for business
reasons
As well as visiting frequently, their visits also tend
to be longer and heaviest in terms of content
consumption
ti
However, they are not as satisfied with the site
experience as other groups, possibly due to the
processes i
involved
ld

36. The framework…in action
Who visits the Why do they visit What do they do on
site? the site and what the site?
do they think of it?

37. Segmentation for email targeting
Segment 3: Segment 5:
Average # orders 3.3
33
Average # orders 3.3
Similar ordering patterns Avg # items 6.4
Avg # items 6.0
Avg spend £175
Avg spend £178
Avg order value £54
Avg order value
g £53
Avg items per order 2.0
Avg items per order 1.8
Products: Products:
Different product purchasing
p p g
DIY Domestic appliances
pp
Car maintenance Furnishings
Garden tools and furniture Nursery
Index
I d vs all online
ll li Male
Ml Female
F l Index
I d vs allll Male
Ml Female
F l
Different demographics
shoppers online shoppers
Younger (<35) 87 78 Younger (<35) 83 122
Older (>35) 127 97 Older (>35) 87 106

38. It’s often all about timing…
Tinofrteaapa
im fis m per
g il s
to make a difference
The whole tree is not displayed
here…
Overall the propensity to order twice doubles if an email
is sent within the first 3 days – emailing within 5 days
still generates a significant increase in conversion from
single shopper to repeat shopper

39. Understanding the drivers of conversion over
multiple visits
Propensity to convert…

40. It generally takes more than one
visit to get the conversion
Car Insurance
120%
omers
100%
mulative % of custo
80%
60%
40%
Cum
20%
0%
1 2 3 4 5 6
Number of visits to conversion

41. Tracking visitor behaviour over
multiple visits
First visit Second visit Subsequent Purchase visit
visit
•Source of •Days since •Days since •Days since
first visit first visit first visit first visit
•Campaign •Entry page •Entry page •Source of
visit? visit
•etc •etc
Keywords Campaign
•Keywords •Campaign
used? visit?
•Day/time •Keywords
used?
•Depth of
visit •Tool used?
•Tool used?
Tl d? •Email
E il
landing?
•Entry page
•Exit page

42. Building the event profile…
Visit 1 events Visit 2 events Visit 3 events Purchase visit events
Vis123
Vis124
Vis125
Vis126
Vis127
Vis128
Vis129
Vis130
Vis131

43. Key drivers of First Visit Buyers
All First Time
Buyers
Index = 100
Paid & Natural
Direct Landing Affiliate Other
Search
Index = 131 Index = 46 Index = 77
Index = 100
d
Branded Non‐branded
keyword keyword
Index = 146 Index = 46

44. What are the main factors influencing
purchases over multiple visits?
Conversion amongst
multi‐visit visitors
Index = 100
Used tool Didn’t use tool
on first visit on first visit
Index = 156 Index = 69
2nd visit 4 days
y
2nd visit on same
i it Second visit
S d i it Second visit
S d i it 2nd visit more than
d i it th
or less from first
day as first within 8 days after 8 days 4 days from first
Index = 73
Index = 149 Index = 174 Index = 146 Index = 59

45. Conclusions
• “Web analytics” is a journey not an event
• A volume and complexity i
As l d l it increases new t l such as
tools h
data mining and predictive analytics are needed in the
analysts tool box
• Operationally deployed
• Testing systems, targeting systems
• As an ad-hoc weapon
ad hoc
• DM & PA can help cut through the noise and reveal
relationships and patterns that would be difficult to
determine using t diti
dt i i traditional queering approaches
l i h
• Challenges:
• Data preparation and management
• Selection of appropriate tools and techniques
• Ability to execute!

46. Thank you!
Any questions?
Neil Mason
neil@applied-insights.co.uk