Predictive Analytics: the Engine for One-to-One Marketing

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Predictive Analytics: the Engine for One-to-One Marketing

  1. 1. Predictive Analytics: the Engine for One-to-One MarketingFebruary 18, 2011 Ghislaine Duymelings █ Jo De Lange █ Geert Verstraeten
  2. 2. Overtoom International█ Business-to-Business distance selling company (Market leader) • Penetration rate in Belgium: 7% ( 850.000 companies) • Database customers: 85.000 companies / 240.000 contacts • Database products : 40.000 references Predictive Analytics - February 18, 2011 █ 2
  3. 3. Overtoom International █ Marketing ChannelsYearly Catalogue: Yearly Catalogue: Monthly Leaflet: Office Supplies Warehouse Supplies Promotional Brochure Predictive Analytics - February 18, 2011 █ 3
  4. 4. Overtoom International █ Marketing ChannelsCompany Website www.overtoom.be Email promotions Predictive Analytics - February 18, 2011 █ 4
  5. 5. Overtoom International █ Challenges By offering the right Product(s)Reaching the right Customer Through the most appropriate Marketing Channel Predictive Analytics - February 18, 2011 █ 5
  6. 6. Python Predictions█ Core business: Predictive Analytics …in order to Using all …we predict manage available future customer one-to-one customer behavior… relationships. information… Predictive Analytics - February 18, 2011 █ 6
  7. 7. Python Predictions█ Core business: Predictive Analytics█ Based in Brussels█ Since 2006█ Team█ Customers: Predictive Analytics - February 18, 2011 █ 7
  8. 8. Predictive Analytics Benefits Respect Efficient for the resource customer deploymtMarketing MarketingRelevance Accountability Predictive Analytics - February 18, 2011 █ 8
  9. 9. One-to-one marketingWhat it could be…Minority Report (2002) Predictive Analytics - February 18, 2011 █ 9
  10. 10. One-to-one marketing The near future?New York, November, 2010 Japan, September, 2010 Predictive Analytics - February 18, 2011 █ 10
  11. 11. One-to-one marketingWell known example: Amazon Predictive Analytics - February 18, 2011 █ 11
  12. 12. One-to-one marketing at Overtoom █ How it all started… By offering the right Product(s)Reaching the right Customer Through the most appropriate Marketing Channel Predictive Analytics - February 18, 2011 █ 12
  13. 13. Reaching the right customer █ Increase targeting efficiency of current marketing actions to existing clients Yearly catalogues Monthly leaflets █ Increase response and turnover █ Segmentation Predictive Analytics Predictive Analytics - February 18, 2011 █ 13
  14. 14. Reaching the right customer █ Segmentation is exploratory █ Prediction is discriminatory Segmentation Prediction Prediction Predictive Analytics - February 18, 2011 █ 14
  15. 15. Reaching the right customer █ Turnover during field test Short term Reduction target size: -10% Turnover: +28% Long term Reduction target size: -10% Turnover : +10% (average) Predictive Analytics - February 18, 2011 █ 15
  16. 16. Personalized Targeting █ The plot thickens… By offering the right Product(s)Reaching the right Customer Through the most appropriate Marketing Channel Predictive Analytics - February 18, 2011 █ 16
  17. 17. Customized OffersMotivation: the paradox of choice 40% stops 30% purchased 6 jams 60% stops 3% purchased 24 jamsSourceS. Iyengar & M. Lepper, When Choice is Demotivating: Can One Desire Too Much of a Good Thing?Journal of Personality and Social Psychology, 2000, Vol. 79, No. 6, 995-1006 Predictive Analytics - February 18, 2011 █ 17
  18. 18. Customized OffersThe Paradox of Choice (Barry Schwartz) █ Too much choice and too much information • is paralyzing • leads to bad decisions • leads to dissatisfaction with good decisions █ Modern technology has helped create this problem, but it can also help create the solution, by tailoring options and information in ways that are relevant to individual consumers Predictive Analytics - February 18, 2011 █ 18
  19. 19. Customized Offers Motivation: Overtoom facts Percentage of Purchases Number of Customers6% 400005% 35000 300004% 250003% 200002% 15000 100001% 50000% 0 A B CD E F GH I J K L MNOPQR S T 1 2 3 4 5 6 7 8 9 10 Product Categories Number of Different Categories Purchased Most customers purchase All categories are purchased to in a limited number a certain degree of categories Predictive Analytics - February 18, 2011 █ 19
  20. 20. Customized Offers SolutionsMarket Basket Response Similarity Analysis Modeling Modeling Predictive Analytics - February 18, 2011 █ 20
  21. 21. Customized Offers Response Models █ Method Product A B C Company ‘O’ has 3 products Model A B C 3 propensity-to-buy models are builtCustomer X A B C Customer X is scored on each of these modelsBest offer C The product with the highest probability-to-buy/expected return is offered to the customer Predictive Analytics - February 18, 2011 █ 21
  22. 22. Customized OffersInitial format (April 2009) Predictive Analytics - February 18, 2011 █ 22
  23. 23. Customized OffersExtended Format Predictive Analytics - February 18, 2011 █ 23
  24. 24. Customized Offers Similarity Model █ MethodCustomer X X We compare any customer with all other customersCustomers 1 2 3 Company ‘O’ has 3 customers Customers have bought products Products A B C Company ‘O’ has 3 productsBest offer C Based on the purchases of the most similar customers, we offer the best possible suggestion to each customer Predictive Analytics - February 18, 2011 █ 24
  25. 25. Customized Offers Similarity Model █ Method AdvantagesCustomer X X █ Client-based vs product-based █ 1 model, simple data structureCustomers 1 2 3 █ Inclusion of all products, categories █ Development time Products A B C █ Comparison with existing models possibleBest offer C  Performance  Variety Predictive Analytics - February 18, 2011 █ 25
  26. 26. Results█ Evaluation:  Conversion rate Percentage of buyers who purchased the specific offer  Success Rate Percentage of buyers who purchased at least 1 of the offers  Variety index Indicator of the global variety of the offers across all customers Predictive Analytics - February 18, 2011 █ 26
  27. 27. Results - development█ Summary Success Rate 0.7 +14.6% +8.6% 0.6 0.5 0.4 0.3 0.2 0.1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Customized Offers Benchmark LogReg Benchmark Similarity Modeling Response Modeling Most Popular Most Popular Product Product Predictive Analytics - February 18, 2011 █ 27
  28. 28. Results - infield █ Conversion rate based on rank of the offer:  Extended format (14 customized offers) Conversion Rate 7% Customized Offers300 % 6% 5% Folder more Baseline 4%relevant 3% 2% 1% 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Number of Recommendations Predictive Analytics - February 18, 2011 █ 28
  29. 29. Implementation█ Stakeholders General Management Inventory Management Marketing Communication Management Partner Purchasing Digital Printing Partner Analytical Partners Predictive Analytics - February 18, 2011 █ 29
  30. 30. Personalized Targeting █ The future… By offering the right Product(s)Reaching the right Customer Through the most appropriate Marketing Channel Predictive Analytics - February 18, 2011 █ 30
  31. 31. Analytics & the Customer Lifecycle AcquisitionSuspect Prospect New Active Customer Inactive Customer Customer At Risk CustomerSuspect Prospect Segmenting Segmenting Churn ReactivationPurchase Conversion & Targeting & Targeting Prevention Customized Customized Customized Offers Offers Offers Profit / Long Term Value Loyalty Predictive Analytics - February 18, 2011 █ 31
  32. 32. █ SAS Success Story█ Visit our websites: www.overtoom.be www.pythonpredictions.com█ Contact information: ghislaine.duymelings@overtoom.be jo.delange@overtoom.be geert.verstraeten@pythonpredictions.com Predictive Analytics - February 18, 2011 █ 32

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