Cutting through the NOISE!!
Applications of data mining and predictive analytics
A li ti       fd t    ii      d    di ti ...
The work we do in the online
space

     Analytics strategy
     development and
      implementation
                    ...
The work we do in the online
space

     Analytics strategy
     development and
      implementation
                    ...
The work we do in the online
space

     Analytics strategy
     development and
      implementation
                    ...
The work we do in the online
space

     Analytics strategy
     development and
      implementation
                    ...
The work we do in the online
space

     Analytics strategy
     development and
      implementation
                    ...
The challenge…
                                  Survey
                                   Data
                          ...
Web
            Survey   Panel data   Customer
analytics
            d
            data                  data
            ...
Rabbits in headlights…
Rabbits in headlights…
The response?



  Data integration


  Better query engines

  Data mining and predictive
  analytics
      y
What do we mean by data mining
and predictive analytics?




                              Predictive
 Data mining
       ...
Application of predictive analytics

                                Number of
                                 tracks
   ...
Application of predictive analytics

                                Number of
                                 tracks
   ...
Predictive Analytics - Techniques

• Statistics
   • e.g. Regression
• Artificial intelligence
   • e.g. N
          Neura...
The data mining process
(CRISP-DM)
                 Business
                                        Data
               U...
The data mining process
(CRISP-DM)
                 Business
                                        Data
               U...
Some applications of data mining and
predictive analytical techniques


Segmentation
S     t ti

Propensity modelling

Eco...
Some applications of data mining and
predictive analytical techniques


Segmentation
S     t ti

Propensity modelling

Eco...
Who are your visitors?

Applications of visitor segmentation techniques
Creating meaningful segments

                       • Demographic
                          • Gender, age etc
           ...
Creating meaningful segments

                       • Demographic
                          • Gender, age etc
           ...
Behavioural segmentation
strategies

     Deterministic         Discovery based



              Rules           Associati...
The framework…

 Who visits the    Why do they visit     What do they do on
    site?          the site and what          ...
Developing the visitor segments

              Behavioural segmentation
                 based on content
                ...
Segmentation using cluster
analysis
         Behavioural data

Vis123
Vis124
Vis125
Vis126
Vis127
Vis128
Vis129
Vis130
Vis131
Building the visitor profile…
                            Profiling data
         Behavioural data                    Atti...
Happy Trackers (6%)

Happy Trackers mainly use the site for Track and
Trace and little else

In terms of profile they tend...
Happy Trackers– 6%, Occasional
information

     Top content                      Top searches          Top campaigns


  ...
Price Finders (10%)



Price Finders are primarily concerned about
finding our information on things like airmail
services...
Cottage Industrialists (2%)


Cottage Industrialists are frequent users of the site
and they mainly come looking for infor...
Regular Posters (1%)

A small but valuable segment

Regular Posters are frequent visitors to the site
and are mainly buyin...
The framework…in action

  Who visits the    Why do they visit     What do they do on
     site?          the site and wha...
Segmentation for email targeting

  Segment 3:                                                                 Segment 5:
...
It’s often all about timing…



                                                   Tinofrteaapa
                          ...
Understanding the drivers of conversion over
multiple visits

Propensity to convert…
It generally takes more than one
visit to get the conversion
                                           Car Insurance
    ...
Tracking visitor behaviour over
multiple visits


First visit           Second visit       Subsequent        Purchase visi...
Building the event profile…
         Visit 1 events   Visit 2 events   Visit 3 events   Purchase visit events

Vis123
Vis1...
Key drivers of First Visit Buyers

                                All First Time
                                    Buye...
What are the main factors influencing
purchases over multiple visits?


                                        Conversion...
Conclusions

• “Web analytics” is a journey not an event
• A volume and complexity i
  As l          d       l it increase...
Thank you!
Any questions?
Neil Mason
neil@applied-insights.co.uk
Neil Mason presents on Data Mining and Predictive Analytics at Emetrics San Fransisco 2008
Neil Mason presents on Data Mining and Predictive Analytics at Emetrics San Fransisco 2008
Neil Mason presents on Data Mining and Predictive Analytics at Emetrics San Fransisco 2008
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Neil Mason presents on Data Mining and Predictive Analytics at Emetrics San Fransisco 2008

  1. 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. 2. The work we do in the online space Analytics strategy development and implementation Systems selection & implementation Site and Customer Analytics
  3. 3. The work we do in the online space Analytics strategy development and implementation Systems selection & implementation Site and Customer Analytics
  4. 4. The work we do in the online space Analytics strategy development and implementation Systems selection & implementation Site and Customer Analytics
  5. 5. The work we do in the online space Analytics strategy development and implementation Systems selection & implementation Site and Customer Analytics
  6. 6. The work we do in the online space Analytics strategy development and implementation Systems selection & implementation Site and Customer Analytics
  7. 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. 8. Web Survey Panel data Customer analytics d data data d data
  9. 9. Rabbits in headlights…
  10. 10. Rabbits in headlights…
  11. 11. The response? Data integration Better query engines Data mining and predictive analytics y
  12. 12. 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
  13. 13. 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
  14. 14. Application of predictive analytics Number of tracks Day of Country presentation Length of Time of conference presentation 4 Size of After lunch? conference
  15. 15. 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,
  16. 16. The data mining process (CRISP-DM) Business Data Understanding Understanding Data Preparation Deployment Modelling Evaluation
  17. 17. The data mining process (CRISP-DM) Business Data Understanding Understanding Data Preparation Deployment Modelling Evaluation
  18. 18. Some applications of data mining and predictive analytical techniques Segmentation S t ti Propensity modelling Econometrics and forecasting Anomaly detection
  19. 19. Some applications of data mining and predictive analytical techniques Segmentation S t ti Propensity modelling Econometrics and forecasting Anomaly detection
  20. 20. Who are your visitors? Applications of visitor segmentation techniques
  21. 21. Creating meaningful segments • Demographic • Gender, age etc g • Lifestyle • Behavioural •BBrowsing i • Purchasing • Response • Attitudinal • Brand empathy • Satisfaction
  22. 22. Creating meaningful segments • Demographic • Gender, age etc g • Lifestyle • Behavioural •BBrowsing i • Purchasing • Response • Attitudinal • Brand empathy • Satisfaction
  23. 23. Behavioural segmentation strategies Deterministic Discovery based Rules Associations Hierarchies Patterns Filters Correlations
  24. 24. 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? ? ? ? ?
  25. 25. 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
  26. 26. Segmentation using cluster analysis Behavioural data Vis123 Vis124 Vis125 Vis126 Vis127 Vis128 Vis129 Vis130 Vis131
  27. 27. Building the visitor profile… Profiling data Behavioural data Attitudinal data Vis128 Vis130 Vis124 Vis123 Vis126 Vis127 Vis131 Vis129 Vis125
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. 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?
  34. 34. 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
  35. 35. 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
  36. 36. Understanding the drivers of conversion over multiple visits Propensity to convert…
  37. 37. 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
  38. 38. 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
  39. 39. 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
  40. 40. 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
  41. 41. 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
  42. 42. 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!
  43. 43. Thank you! Any questions? Neil Mason neil@applied-insights.co.uk

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