NEEDS-BASED SEGMENTATION CREATED FROM DATA WAREHOUSE DATA

If you used the decision tree presented in Exhibit 4.1, you have been guided to this section because you want to improve your acquisition campaigns and have no historical campaigning data. You have, however, indicated that you have access to data warehouse information or other kinds of transactional customer data. This transactional data could come from billing, sales, or customer relationship management (CRM) systems. Since we do not have campaign-specific data, we will rely on this data to guide us in a direction that gives us an improved understanding of the customer needs.

From an analytical perspective, we source customer data that describes customers’ consumption patterns, we make a cluster analysis on this data, and interpret—supported by background variables—what needs have driven this customer behavior. Afterward it is up to the commercial decision makers to determine whether they can act on this segmentation or whether another segmentation model or approach has to be pursued. Exhibit 4.6 gives an overview of the process steps from an analytical perspective.

Exhibit 4.6 Methodological Flow of This Section

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Cluster Analysis Based on Transactional Data

A car dealer once asked for a segmentation model for his local garage. He had a list of the last 350 cars he had sold in the previous year including what additional ...

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