Customer Segmentation: Taking a Page out of the Computer Vision Book
Why Customer Segmentation?

Customer segmentation refers to the process of dividing the customer base of a company into groups, which share common demographical or behavioural characteristics. Understanding the different types of customers is important for formulating a coherent set of targeted strategies, such as brand positioning, one-to-one marketing and targeted individual recommendations. On some occasions, it has even been used to identify missing segments in the customer portfolio, and launch an aggressive acquisition campaign (Groysberg, 2018).

An example of a persona profile by Salminen et al. (2018)

Customer segmentation can also be used as a foundation for customer persona generation. Defined as “a fictitious person representing an underlying customer or user group”, customer persona crystallises a specific segment into an archetype often with concrete visualisation, background stories and transactions with the retailer. The main benefit of such an approach is to provide a realistic, shared mental model of the different types of customers for key decision makers. However, the generation of personas is not without its criticisms, which are usually about its lack of verifiability and actionability. See Salminen et al. (2018) for a more detailed account.

We are at present witnessing the advent of mass collection of rich datasets such as application engagement statistics and shopping behaviours, coupled with the increasing availability of online analytics data such as social media sources. Automated, data-driven customer segmentation is fast becoming readily available for many retailers and tech platforms enabled by a wealth of rich datasets. It is often the low hanging fruit and the first step towards understanding their customers better.
['Zero One Group']
['Machine Learning', 'Data Science', 'Customer Segmentation', 'Technology', 'Algorithms']