Irrespective of whether the underlying data comes from e-shop customers, your clients, small businesses or both large profit and non-profit organizations, market segmentation analysis always brings valuable insights and helps you to leverage otherwise hidden information in your favor, for example greater sales. Therefore, it is vitally important to utilize an efficient analytical pipeline, which would not only help you understand your customer base, but also further serve you during planning of your tailored offers, advertising, promos or strategy. Let us play with some advanced analytics in order to provide a simple example of efficiency improvement when using segmentation techniques, namely clustering, projection pursuit and t-SNE.
As your goal might be improving your sales through tailored customer contact, you need to discover homogeneous groups of people. The different groups of customers behave and respond differently, therefore it is only natural to treat them in a different way. The idea is to get greater profit in each segment separately, through diverse strategy. Thus, we need to accomplish two fundamental tasks:
- identify homogeneous market segments (i.e. which people are in which group)
- identify important features (i.e. what is decisive for customer behavior)
In this post, I am focusing on the first problem from the technical point of view, using some advanced analytic methods. For the sake of brief demonstration, I will work with simple dataset, describing the annual spending of clients of a wholesale distributor on diverse product categories. Following the figure below, it would be difficult to detect some well separated clusters of clients at the first sight.