UX Challenges in Implementing an Interactive B2B Customer Segmentation Tool

TL;DR

This paper explores UX challenges in developing an interactive customer segmentation tool using unsupervised machine learning, highlighting user effort and interaction design issues, based on a real-world case study with domain experts.

Contribution

It presents a case study of implementing an IML prototype for customer segmentation, offering insights into UX challenges and effective design strategies in a real-world industrial context.

Findings

User effort in interpreting clusters is significant.

Effective interactions improve understanding of clustering output.

Feedback from domain experts guides UX improvements.

Abstract

In our effort to implement an interactive customer segmentation tool for a global manufacturing company, we identified user experience (UX) challenges with technical implications. The main challenge relates to domain users' effort, in our case sales experts, to interpret the clusters produced by an unsupervised Machine Learning (ML) algorithm, for creating a customer segmentation. An additional challenge is what sort of interactions should such a tool support to enable meaningful interpretations of the output of clustering models. In this case study, we describe what we learned from implementing an Interactive Machine Learning (IML) prototype to address such UX challenges. We leverage a multi-year real-world dataset and domain experts' feedback from a global manufacturing company to evaluate our tool. We report what we found to be effective and wish to inform designers of IML systems in the context of customer segmentation and other related unsupervised ML tools.