LOYALTY CAN BE PREDICTED!            PROF. DR. DIRK VAN DEN POELSpecial Thanks To:Master of Science in Marketing Analysis ...
•  Brand loyalty is a consumers preference for a particular brand and a commitment to repeatedly purchase that brand in th...
A non-profit organization that commits itself tothe preservation of nature and biodiversity inFlanderswww.natuurpunt.be
•  Business objectives   •  Decrease number of members stopping their membership   •  More efficient and effective marketi...
ExplorationAnalysis and main findingsRecommendations
450 tables211+ thousand contacts12 million observations
•  All information combined in one basetable
•  Did member stop membership in past 11 years?   •  How long did they stay member before they stopped?•  Analyze categori...
•  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect...
Life-Table Survival Curve                                     100%       100%Survival Probability / Hazard rate           ...
•  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect...
Member + donor N = 8.789Member + volunteer N = 698Member + volunteer + donor N = 357Member only = 89.900                  ...
Survival estimate at year 2Based on data after 2001
Survival estimate at year 2Based on data after 2001
•  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect...
Survival estimate at year 2Based on data after 2001
Survival	  Distribu-on	  Func-on	  Es-mate	                                100%	   100.0%	                                ...
•  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect...
•  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect...
Contacts by province           0.58%Wallonia                               29.96%  Others    1.19%               Antwerp  ...
Number of members relative by number of inhabitants per postal code
•  Predict if a member stops membership within next 12 months•  Gives score between 0 (safe) and 1 (at risk) to each membe...
Model Building             INDEPENDENT         GAP    DEPENDENT31DEC01                    31MAR11   01APR11   01APR12   Pu...
Based on test sample with stepwise selection
Lift 10%: 3,76 Based on test sample with stepwise selection
1.         Create analysis basetable      •      Get statistics on members      •      Create new predictive models2.     ...
•  Encourage good behavior   •  Standing orders   •  Subscriptions•  Encourage involvement   •  Get more people on the web...
•  Historical data: Missing dates•  More reliable gender data•  Consistent data input   •  Recruitment type categories•  F...
•  Investigate reasons of stopping    •  Exit questions    •  Why do members stop standing orders?•  Volunteering data   •...
•  Churn-prevention efforts   •  Target top x%   •  Mailings, discount offers, …•  Traffic lights system   •  Color codes ...
We are able to predict customer churn/loyaltyPredictive Analytics makes churnprevention actionable
•  VERHAERT G. & VAN DEN POEL D. (2012),   The role of seed money and threshold size in optimizing fundraising   campaigns...
@DirkVandenPoelDirk.VandenPoel@UGent.be
Presentation charity analytics natuurpunt at bpost media and social day 2013 event jan 29 2013
Presentation charity analytics natuurpunt at bpost media and social day 2013 event jan 29 2013
Presentation charity analytics natuurpunt at bpost media and social day 2013 event jan 29 2013
Presentation charity analytics natuurpunt at bpost media and social day 2013 event jan 29 2013
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Presentation charity analytics natuurpunt at bpost media and social day 2013 event jan 29 2013

  1. 1. LOYALTY CAN BE PREDICTED! PROF. DR. DIRK VAN DEN POELSpecial Thanks To:Master of Science in Marketing Analysis (UGent) students:Ruben Billiet eventSam BeelprezYasir Ekinci Jan. 29, 2013Koen GommersJingbo WeiBrian WeidenbaumAssistants: Jeroen D’Haen & Andrey Volkovwww.mma.UGent.be
  2. 2. •  Brand loyalty is a consumers preference for a particular brand and a commitment to repeatedly purchase that brand in the face of other choices (Dick & Basu, 1994)•  Behavioral loyalty versus Attitudinal loyalty (Jacoby & Chestnut, 1978) •  E.g. monopoly
  3. 3. A non-profit organization that commits itself tothe preservation of nature and biodiversity inFlanderswww.natuurpunt.be
  4. 4. •  Business objectives •  Decrease number of members stopping their membership •  More efficient and effective marketing•  Project goals •  A better understanding of the data •  Create a churn model •  Predict if member will stop in the next year •  Provide insights and recommendations to stop customer attrition
  5. 5. ExplorationAnalysis and main findingsRecommendations
  6. 6. 450 tables211+ thousand contacts12 million observations
  7. 7. •  All information combined in one basetable
  8. 8. •  Did member stop membership in past 11 years? •  How long did they stay member before they stopped?•  Analyze categories that impact relationship•  Long-term perspective
  9. 9. •  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect memberships in the long term?•  Are some recruitment types more effective than others?•  Does the province where you live have an effect on how long you stay a member?
  10. 10. Life-Table Survival Curve 100% 100%Survival Probability / Hazard rate 90% 80% 74% 67% 62% 58% 60% 55% 52% 49% 46% 43% 40% 19.95% 20% 10.33% 9.28% 7.60% 7.07% 5.99% 5.68% 5.61% 5.61% 6.85% 0% 0 1 2 3 4 5 6 7 8 9 10 Length of Membership Relationship (in years) Hazard Function Estimate at Midpoint Survival Distribution Function Estimate Total # of members: 99.744 Based on data after 2001
  11. 11. •  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect memberships in the long term?•  Are some recruitment types more effective than others?•  Does the province where you live have an effect on how long you stay a member?
  12. 12. Member + donor N = 8.789Member + volunteer N = 698Member + volunteer + donor N = 357Member only = 89.900 Based on data after 2001
  13. 13. Survival estimate at year 2Based on data after 2001
  14. 14. Survival estimate at year 2Based on data after 2001
  15. 15. •  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect memberships in the long term?•  Are some recruitment types more effective than others?•  Does the province where you live have an effect on how long you stay a member?
  16. 16. Survival estimate at year 2Based on data after 2001
  17. 17. Survival  Distribu-on  Func-on  Es-mate   100%   100.0%   Average duration after stopped order 80%   1 year 6 months 11 daysSurvival  Probability   60%   42.8%   40%   31.9%   24.9%   20.0%   20%   11.5%   9.2%   7.6%   6.9%   5.7%   5.7%   0%   0   1   2   3   4   5   6   7   8   9   10   Length  of  Membership  RelaEonship  aFer  stopped  standing  order  (in  years)   Based on data after 2001
  18. 18. •  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect memberships in the long term?•  Are some recruitment types more effective than others?•  Does the province where you live have an effect on how long you stay a member?
  19. 19. •  Does involvement have an effect on how long a membership lasts?•  How does having or not having a standing order affect memberships in the long term?•  Are some recruitment types more effective than others?•  Does the province where you live have an effect on how long you stay a member?
  20. 20. Contacts by province 0.58%Wallonia 29.96% Others 1.19% Antwerp 1.36%Brussels 9.32% Limburg 22.56% East- 16.73% Flanders West- Flanders Flemish Brabant 18.29% Total # of contacts: 211.731
  21. 21. Number of members relative by number of inhabitants per postal code
  22. 22. •  Predict if a member stops membership within next 12 months•  Gives score between 0 (safe) and 1 (at risk) to each member •  Based on historical data •  Indicates likelihood of stopping membership•  A search engine for members who are in danger of stopping
  23. 23. Model Building INDEPENDENT GAP DEPENDENT31DEC01 31MAR11 01APR11 01APR12 Purpose Model INDEPENDENT GAP DEPENDENT31DEC01 31MAR12 01APR12 01APR13
  24. 24. Based on test sample with stepwise selection
  25. 25. Lift 10%: 3,76 Based on test sample with stepwise selection
  26. 26. 1.  Create analysis basetable •  Get statistics on members •  Create new predictive models2.  Give “Churn Score” to every active member •  Churn probability between 0 (safe) and 1 (at risk) •  Find members in danger of stopping membership
  27. 27. •  Encourage good behavior •  Standing orders •  Subscriptions•  Encourage involvement •  Get more people on the website •  Promote donating and volunteering•  Focus on successful campaigns •  “Member get member” actions
  28. 28. •  Historical data: Missing dates•  More reliable gender data•  Consistent data input •  Recruitment type categories•  Fix the missing link with website users •  Automatic account creation
  29. 29. •  Investigate reasons of stopping •  Exit questions •  Why do members stop standing orders?•  Volunteering data •  Start & end dates •  Reason of volunteering•  Gather birthdates (online form)•  Membership card + scanners •  Member visits to parks •  Natuurpunt shop purchases•  Qualitative Market Research on churn impact factors
  30. 30. •  Churn-prevention efforts •  Target top x% •  Mailings, discount offers, …•  Traffic lights system •  Color codes for probability of stopping •  More actions for members with red light •  Evolution of probability
  31. 31. We are able to predict customer churn/loyaltyPredictive Analytics makes churnprevention actionable
  32. 32. •  VERHAERT G. & VAN DEN POEL D. (2012), The role of seed money and threshold size in optimizing fundraising campaigns: Past behavior matters!, Expert Systems with Application, 39 (18), 13075-13084.•  VERHAERT G. & VAN DEN POEL D. (2011), Empathy as Added Value in Predicting Donation Behavior, Journal of Business Research, 64 (12), 1288-1295.•  VERHAERT G. & VAN DEN POEL D. (2011), Improving campaign success rate by tailoring donation requests along the donor lifecycle, Journal of Interactive Marketing, 25 (1), 51-63.•  JONKER J.J., PIERSMA N. & VAN DEN POEL D. (2004), Joint Optimization of Customer Segmentation and Marketing Policy to Maximize Long-Term Profitability, Expert Systems with Applications, 27 (2), 159-168.•  A 30 video featuring Dr. Griet A. Verhaert is available on this page.
  33. 33. @DirkVandenPoelDirk.VandenPoel@UGent.be

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