Predictive Analytics in Fundraising - Success Story DSC
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In this presentation, we explain how DSC, a Belgian direct marketing specialist, developed a predictive model for targeting new potential donors.
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Predictive Analytics in Fundraising - Success Story DSC
- 1. Ludo Longin Direct Social Communications Analytics in Fundraising How knowing donors helps in growing donors Geert Verstraeten Python Predictions
- 2. = communication agency °1985 1 activity: fundraising for humanitarian organisations Team: 12 enthousiastic people Direct Social Communications nv
- 3. Health Food Belgium Belgian Assocation for Burn Injuries Belgian Cystic Fibrosis Assocation Kom op Tegen Kanker (Cancer Assoc.) Food Banks Restaurants of the Heart World Mercy Ships Chain of Hope Damien Foundation Medics Without Vacation
- 4. Children Animals Belgium Collective Research and Expression Youth Village Pelicano Foundation World If Child Help Veterinarians Without Borders
- 5. Handicap People Belgium Blind Care Light & Love Flemish Autism Association World Handicap International Sensorial Handicap Cooperation Pilots Without Borders Mamas for Africa The Voice of the Lebanese Women Friends of Sister Emmanuelle
- 6. ± 400 fundraising campaigns using dm > 8,000,000 letters to private individuals > 1,500,000 inserts in newspapers > 600,000 donations per year > 24,000,000 euros
- 8. Fundraising ≠ Easy Job
- 9. Collecting-box Many ways to raise money for your organisation
- 10. Collecting-Box Activity: pancakes, theatre, car wash,… Many ways to raise money for your organisation
- 11. Collecting-Box Activity: pancakes, theatre, car wash,… Gala-Evening and Diner Many ways to raise money for your organisation
- 12. Collecting-Box Activity: pancakes, theatre, car wash,… Gala-Evening and Diner Art Auction Many ways to raise money for your organisation
- 13. Collecting-Box Activity: pancakes, theatre, car wash,… Gala-Evening and Diner Art Auction Face to face street fundraising Many ways to raise money for your organisation
- 14. Collecting-Box Activity: pancakes, theatre, car wash,… Gala-Evening and Diner Art Auction Face to face street fundraising Telemarketing Many ways to raise money for your organisation
- 15. LABOUR INTENSIVE One common element
- 16. DRTV Many ways to raise money for your organisation
- 17. Mobile giving Many ways to raise money for your organisation
- 18. Mobile giving Many ways to raise money for your organisation Most effective campaign: American Red Cross – Haïti crisis 3 million unique donors sent an sms for the earthquake in Haïti. 32 million dollars
- 19. Internet for fundraising? ◦ On line giving ◦ E-mail ◦ Social Networksites Many ways to raise money for your organisation
- 20. Direct Mail Many ways to raise money for your organisation
- 21. Easy to organise: write – print - post Advantages of Direct Mail
- 22. Easy to organise: write – print – post Easy to measure Advantages of Direct Mail
- 23. Easy to organise: write – print – post Easy to measure Data Analysis/Segmentation improves results Advantages of Direct Mail
- 24. Female 60+ years old religious Fully owned appartement/house Adult children have left the house Typical dm-donor in Belgium
- 25. Response rate in housemailing: ◦ 8 % ◦ 10 % ◦ 12 % ◦ 14 % ◦ More than 15 % Normal results of dm-campaigns
- 26. Belgian Cystic Fibrosis Assocation
- 27. Housemailing: - Posted on 20th april 2012 - 19.328 pieces sent 4.881 donations received = 25,25 % Average donation = 30,90 euros Belgian Cystic Fibrosis Assocation
- 28. Belgian Cystic Fibrosis Assocation
- 29. Response rate in Acquisition campaigns: ◦ 2 % ◦ 2,5 % ◦ 3 % ◦ 4 or More % Normal results of dm-campaigns
- 30. From test to roll-out Test variation 1 Test variation 2 Test variation 3 Test Roll-out + New tests Roll-out (Test variation 2) Test variation 4 Test variation 5 Evaluation
- 31. Who ? Target group & addresses How / Means ? Channels What /Ask? Content How ? Concept & Creation When ? Timing & frequency Gadget ? Incentive Test variables
- 32. # sent units # gifts Total Gifts € Mean gift € % Gift €/ sent unit Cost €/ sent unit Income €/ sent unit 8000 152 6438 42,36 1,90% 0,80 1,01 -0,21 8000 230 9501 41,31 2,88% 1,19 1,08 +0,11 Roll-out of 50.000 sent units: 5.500 euros profit in stead of 10.500 euros loss
- 33. Recruitment campaigns Where do we get our donors from? D.S.C. ± 220,000 addresses CONSUDATA + criteria 6 million addresses Other databases
- 34. Prospection Approaches Address List Purchase Use of analytics: none
- 35. Prospection Approaches Intuitive Segment Purchase / Hire Use of analytics: precooked segmentation ‘young singles’
- 36. Informed Segment Purchase / Hire Use of analytics: precooked segmentation Prospection Approaches ‘young families’
- 37. Prospection Approaches Customer/Donor Cloning Use of analytics: advanced (predictive models, similarity models)
- 38. Python Predictions Using all available data… …we predict future events … …to improve decision making Core business: Predictive Analytics in marketing, risk and operations
- 39. Core business: Predictive Analytics Since 2006 SAS Partner since 2006 References: Python Predictions
- 40. Roadmap to Cloning 1 Define interesting donors = donors ‘to be cloned’ 2 Calculate Targets = historic conversion rate per area 3 Calculate Predictors = objective socio-demographic criteria per area 4 Predict future conversion = Select & combine best predictors 5 Validate predictions = Compare predicted vs real response, and profile
- 41. Project Approach Define which donors are used ‘to be cloned’ OPTION 1: Previous Blind Care Light & Love donors OPTION 2: Previous DSC donors BUT: only active donors selected = at least minimal gift during last 2 years 1
- 42. Project Approach 2 Per geographical area we know: # of donors # of households Historic conversion (# donors/ # households) = ‘the target’
- 43. Project Approach 3 We calculate socio-demographical information, containing: • Residents (age, education, activity,…) • Housing (inhabitants, comfort,…) • Neighbourhood (facilities, transport,…) Around 700 variables known for each of the 20.000 geographical areas in Belgium
- 44. Project Approach 4 We predict historic conversion: Number of Households Age 55- 60y 270 10% 100 30% 65 70% … Cloning Model Predicted Conversion 0.01% 0.02% 0.15% … Area Number Historic Conversion 1 0% 2 0.05% 3 0.20% … … 2 3 4
- 45. 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 86-89 90-94 >95 Age category Profile: Example Households we would not target Households we would target 5
- 46. Profile: Example 8% 26% 0 1 2 or more Number of bathrooms 9% 21% N/A < 35 m2 35 till 54 m2 55 till 84 m2 85 till 104 m2 105 till 124 m2 > 125 m2 House surface Households we would not target Households we would target 5
- 47. Estimated Quality 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 0 50000 100000 150000 200000 250000 300000 Cloning Geo Segmentation Age Comparison of historic response rate per target size Number of Households Targeted Response
- 48. Estimated Quality 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 0 50000 100000 150000 200000 250000 300000 Cloning Geo Segmentation Age Comparison of historic response rate per target size Number of Households Targeted Response
- 49. Estimated Quality 0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 0 50000 100000 150000 200000 250000 300000 Cloning Geo Segmentation Age Comparison of historic response rate per target size Number of Households Targeted Response
- 50. Heatmap (predicted conversion) Example: Region of Bruges Bruges
- 51. 1.98% 2.29% 2.71% Age > 55y Cloning Top 100.000 Cloning Top 10.000 Response Percentage 21.6 € 25.5 € 28.6 € Age > 55y Cloning Top 100.000 Cloning Top 10.000 Average Donation Amount improvement of 37% in response rate improvement of 32% in donation amount Real-life Prospection Campaign
- 52. 0.43 € 0.58 € 0.78 € Age > 55y Cloning Top 100.000 Cloning Top 10.000 Break (revenue per letter sent) improvement of 82% in revenue per letter sent Current and Future Usage Real-life Prospection Campaign
- 53. QUESTIONS? Or later? Ludo Longin ludo.longin@dsc.be Tel +32 2 280 00 74 Direct Social Communications www.dsc.be Geert Verstraeten geert.verstraeten@pythonpredictions.com Tel +32 2 762 69 00 Python Predictions www.pythonpredictions.com