Abstract
This chapter describes the importance and benefits of data mining and gives a detailed overview of the underlying process. The data mining procedure breaks down into five subsections: defining the business objectives, getting the raw data, identifying relevant variables, gaining customer insight, and acting. The discussion of these steps will help the reader understand the overall process of data mining. Furthermore, the process steps are illustrated with the case study of Credite Est (name disguised), a French mid-tier bank. Finally, the case study, “Yapi Kredi—Predictive Model–Based cross-sell Campaign,” shows a comprehensive application of data mining.
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Notes
- 1.
An example would be the expectation maximization algorithm (EM) that takes into account the correlation of the data field for which a nonmissing value is to be generated with other data fields.
- 2.
Sometimes, obstacles such as lacking authorization of the data mining team for accessing the required data might emerge. Data miners frequently work with data which other business departments do not have access to. There is a high level of secrecy and trust involved.
- 3.
Kohonen networks belong to the family of neural network techniques. These are powerful data modeling tools able to capture and represent complex input/output relationships for example in target marketing, financial forecasting, or process control. In particular, the objective of a Kohonen network is to generate, out of complex input patterns of arbitrary dimension, a simplified (discrete) map with very few dimensions, say 1 or 2. Thus, the Kohonen network is an approach to quickly understand complex data as a result of a simplification of the structure. For a good overview of neural networks and Kohonen networks please refer to: Principe, Euliano, and Lefebvre (2000).
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Turner, D., Schroeck, M., & Shockley, R. (2013). Analytics: The real-world use of big data in financial services. IBM Global Business Services. https://www-935.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf. Accessed May 2, 2017.
Acknowledgments
We thank Frank Block, Ph.D., of FinScore Corporation (Switzerland) for his collaboration on this chapter.
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Kumar, V., Reinartz, W. (2018). Data Mining. In: Customer Relationship Management. Springer Texts in Business and Economics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-55381-7_7
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