Special Thanks To: Master of Science in Marketing Analysis (UGent) students: Ruben Billiet Sam Beelprez Yasir Ekinci Koen Gommers Jingbo Wei Brian Weidenbaum Assistants: Jeroen D’Haen & Andrey Volkov www.mma.UGent.be

LOYALTY CAN BE PREDICTED! PROF. DR. DIRK VAN DEN POEL

event Jan. 29, 2013

• Brand loyalty is a consumer's 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

A non-profit organization that commits itself to the preservation of nature and biodiversity in Flanders

www.natuurpunt.be

• 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

Exploration

Analysis and main findings

Recommendations

450 tables 211+ thousand contacts

12 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 categories that impact relationship

•  Long-term perspective

• 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?

10.33%

19.95%

9.28% 7.60% 7.07% 5.99% 5.68% 5.61% 5.61% 6.85%

100% 90%

74% 67%

62% 58% 55% 52% 49% 46%

43%

0%

20%

40%

60%

80%

100%

0 1 2 3 4 5 6 7 8 9 10

Surv

ival

Pro

babi

lity

/ Haz

ard

rate

Length of Membership Relationship (in years)

Life-Table Survival Curve

Hazard Function Estimate at Midpoint Survival Distribution Function Estimate

Total # of members: 99.744 Based 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 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?

Based on data after 2001

Member + donor N = 8.789 Member + volunteer N = 698 Member + volunteer + donor N = 357 Member only = 89.900

Survival estimate at year 2 Based on data after 2001

Survival estimate at year 2 Based 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 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?

Survival estimate at year 2 Based on data after 2001

Based on data after 2001

100.0%  

42.8%  

31.9%  24.9%  

20.0%  

11.5%   9.2%   7.6%   6.9%   5.7%   5.7%  

0%  

20%  

40%  

60%  

80%  

100%  

0   1   2   3   4   5   6   7   8   9   10  

Survival  Probability  

Length  of  Membership  RelaEonship  aFer  stopped  standing  order  (in  years)  

Survival  Distribu-on  Func-on  Es-mate  

Average duration after stopped order 1 year 6 months 11 days

• 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?

• 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?

Total # of contacts: 211.731

29.96%

22.56%

18.29%

16.73%

9.32%

1.36% 1.19%

0.58%

Contacts by province

Antwerp

East-Flanders

Flemish Brabant

West-Flanders

Limburg

Wallonia

Others

Brussels

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 member •  Based on historical data •  Indicates likelihood of stopping membership

• A search engine for members who are in danger of stopping

INDEPENDENT GAP DEPENDENT

31DEC01 31MAR11 01APR11 01APR12

Model Building

INDEPENDENT GAP DEPENDENT

31DEC01 31MAR12 01APR12 01APR13

Purpose Model

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 models

2.  Give “Churn Score” to every active member •  Churn probability between 0 (safe) and 1 (at risk) •  Find members in danger of stopping membership

• 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

• Historical data: Missing dates

• More reliable gender data

• Consistent data input •  Recruitment type categories

•  Fix the missing link with website users •  Automatic account creation

•  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

• 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

We are able to predict customer churn/loyalty Predictive Analytics makes churn prevention actionable

•  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.

Dirk.VandenPoel@UGent.be @DirkVandenPoel