Summary

In this chapter, we explored the idea of segmentation and its utility for business. We discussed the key considerations in segmentation, namely, criteria/features and the interpretation of the segments. We first discussed and implemented a traditional approach to customer segmentation. Noting its drawbacks, we then explored and performed unsupervised machine learning for customer segmentation. We established how to think about the similarity in the customer data feature space, and also learned the importance of standardizing data if it is on very different scales. Finally, we learned about k-means clustering – a commonly used, fast, and easily scalable clustering algorithm. We employed these concepts and techniques to help a mall understand its customers better using segmentation. We also helped a bank identify customer segments and how they have responded to previous marketing campaigns.

In this chapter, we used predefined values for the number of groups we asked...