Predictive Analytics: the Engine for One-to-One Marketing
•Télécharger en tant que PPTX, PDF•
4 j'aime•1,395 vues
Predictive Analytics: the Engine for One-to-One Marketing. More information on http://www.dmupdate.be
Recommandé
Contenu connexe
Similaire à Predictive Analytics: the Engine for One-to-One Marketing
Similaire à Predictive Analytics: the Engine for One-to-One Marketing (20)
Plus de Abbey Road Creations
Plus de Abbey Road Creations (20)
Dernier
Dernier (20)
Predictive Analytics: the Engine for One-to-One Marketing
- 1. Predictive Analytics: the Engine for One-to-One Marketing February 18, 2011 Ghislaine Duymelings █ Jo De Lange █ Geert Verstraeten
- 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. Overtoom International █ Marketing Channels Yearly Catalogue: Yearly Catalogue: Monthly Leaflet: Office Supplies Warehouse Supplies Promotional Brochure Predictive Analytics - February 18, 2011 █ 3
- 4. Overtoom International █ Marketing Channels Company Website www.overtoom.be Email promotions Predictive Analytics - February 18, 2011 █ 4
- 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. 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. Python Predictions █ Core business: Predictive Analytics █ Based in Brussels █ Since 2006 █ Team █ Customers: Predictive Analytics - February 18, 2011 █ 7
- 8. Predictive Analytics Benefits Respect Efficient for the resource customer deploymt Marketing Marketing Relevance Accountability Predictive Analytics - February 18, 2011 █ 8
- 9. One-to-one marketing What it could be… Minority Report (2002) Predictive Analytics - February 18, 2011 █ 9
- 10. One-to-one marketing The near future? New York, November, 2010 Japan, September, 2010 Predictive Analytics - February 18, 2011 █ 10
- 11. One-to-one marketing Well known example: Amazon Predictive Analytics - February 18, 2011 █ 11
- 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. 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. Reaching the right customer █ Segmentation is exploratory █ Prediction is discriminatory Segmentation Prediction Prediction Predictive Analytics - February 18, 2011 █ 14
- 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. 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. Customized Offers Motivation: the paradox of choice 40% stops 30% purchased 6 jams 60% stops 3% purchased 24 jams Source S. 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. Customized Offers The 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. Customized Offers Motivation: Overtoom facts Percentage of Purchases Number of Customers 6% 40000 5% 35000 30000 4% 25000 3% 20000 2% 15000 10000 1% 5000 0% 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. Customized Offers Solutions Market Basket Response Similarity Analysis Modeling Modeling Predictive Analytics - February 18, 2011 █ 20
- 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 built Customer X A B C Customer X is scored on each of these models Best offer C The product with the highest probability-to-buy/expected return is offered to the customer Predictive Analytics - February 18, 2011 █ 21
- 22. Customized Offers Initial format (April 2009) Predictive Analytics - February 18, 2011 █ 22
- 23. Customized Offers Extended Format Predictive Analytics - February 18, 2011 █ 23
- 24. Customized Offers Similarity Model █ Method Customer X X We compare any customer with all other customers Customers 1 2 3 Company ‘O’ has 3 customers Customers have bought products Products A B C Company ‘O’ has 3 products Best 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. Customized Offers Similarity Model █ Method Advantages Customer X X █ Client-based vs product-based █ 1 model, simple data structure Customers 1 2 3 █ Inclusion of all products, categories █ Development time Products A B C █ Comparison with existing models possible Best offer C Performance Variety Predictive Analytics - February 18, 2011 █ 25
- 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. 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. Results - infield █ Conversion rate based on rank of the offer: Extended format (14 customized offers) Conversion Rate 7% Customized Offers 300 % 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. Implementation █ Stakeholders General Management Inventory Management Marketing Communication Management Partner Purchasing Digital Printing Partner Analytical Partners Predictive Analytics - February 18, 2011 █ 29
- 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. Analytics & the Customer Lifecycle Acquisition Suspect Prospect New Active Customer Inactive Customer Customer At Risk Customer Suspect Prospect Segmenting Segmenting Churn Reactivation Purchase Conversion & Targeting & Targeting Prevention Customized Customized Customized Offers Offers Offers Profit / Long Term Value Loyalty Predictive Analytics - February 18, 2011 █ 31
- 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