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1. Introduction
2. Agenda
3. Objectives
4. Response analysis
5. Steps in building a targeting model
6. Modelling methodology
7. CRISP-DM
8. CRISP-DM processes (1)
9. CRISP-DM processes (2)
10. Phase 4: modelling
11. Modelling techniques
12. Recency, frequency, monetary value (RFM)
13. Basic idea of RFM
14. RFM process
15. RFM results
16. Benefits of RFM
17. Problems with RFM
18. Classification trees (1)
19. Classification trees (2)
20. CHAID
21. CHAID tree example
22. Benefits of CHAID
23. Algorithms and vendors
24. Regression analysis (1)
25. Regression analysis (2)
26. Linear regression
27. Logistic regression
28. Response modelling
29. Loan response example (1)
30. Loan response example (2)
31. Loan response example (3)
32. Benefits of logistic regression
33. CHAID and regression
34. Logistic regression: vendors
35. New approach to response modelling
36. Structured risk minimisation
37. Benefits of SRM
38. SRM: vendors
39. Model testing and evaluation
40. Validation
41. Gains curve (1)
42. Gains curve (2)
43. The random model curve
44. Gains curve and random curve together
45. Decile analysis (1)
46. Decile analysis (2)
47. Decile analysis (3)
48. Which technique?
49. Main points of RFM
50. Trees or regression? (1)
51. Trees or regression? (2)
52. Case study
53. Background
54. Data
55. Initial regression model
56. Reduced variable model
57. Cade study: decile analysis
58. Gains chart
59. Conclusion

Topics Covered

• What is response analysis?
• The steps in building a targeting model (CRISP)
• How analytical techniques such as RFM, classification trees and regression analysis can be applied in order to identify the best targets for a direct marketing campaign
• Choice of software and modelling technique
• Model testing and evaluation
• Case study example

Links

Series:

• Statistics of Direct Marketing

Categories:

• Marketing & Sales

Talk Citation

Publication History

• Published on May 22, 2008