Analytical CRM pp 135-170 | Cite as

Support Vector Machines for Predicting Customer Activity and Future Best Customers in Non-Contractual Settings


The Pareto/NBD and the BG/NBD models owe their names to their underlying distributional assumptions, which emphasizes the strong theoretical foundation of the models. Yet, the last chapter showed that they do not outperform simple management heuristics. In fact, even back in the late 1960s, Tukey (1969) has already postulated that putting too much emphasis on the mathematical theories of statistics did not help in solving the real world problems. It was his mantra that statistical work is detective work and that one should let the data speak for itself. The branch of exploratory data analysis emerged, but was dismissed by mathematical statisticians for a long period of time. Many of them proclaimed that proper statistical analysis must be based on hypothesis and distributional assumptions. Their argument was that looking at data before formulating a scientific hypothesis would bias the hypothesis towards what the data might show. The term data mining typically was used in a derogatory connotation. The argument culminated in the reproach of improper scientific use, the reproach of torturing the data until it confesses everything.


Support Vector Machine Unsupervised Learning Cost Parameter Simple Heuristic Empirical Risk 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Cui, Dapeng and David Curry (2005), “Prediction in Marketing Using the Support Vector Machine,” Marketing Science, 24(4), 595–615.CrossRefGoogle Scholar
  2. Courant, Robins and David Hilbert (1953), Methods of Mathematical Physics, New York: Interscience.Google Scholar
  3. Vapnik Vladimir, (1995), The Nature of Statistical Learning Theory, New York: Springer.Google Scholar

Copyright information

© Gabler | GWV Fachverlage GmbH, Wiesbaden 2008

Personalised recommendations