A company operating in the retail sector faces significant heterogeneity in the performance of it stores. These differences may be due to various factors, including the type of clientele, geographical location, and local demand and supply dynamics. To maximise the effectiveness of its marketing initiatives, improve resource allocation, and optimise business performance, it is essential to achieve more precise and targeted segmentation of its stores.
To address the client’s needs, at Moxoff we have implemented a clustering solution based on a Machine Learning algorithm capable of analysing socio-economic, geographical, and commercial variables. To segment the stores into homogeneous groups, the algorithm examines factors such as the average income of the local population, demographic density, geographical location, sales performance and the type of product sold. Each cluster makes it possible to identify the specific characteristics and potential of each group, creating a clear and detailed view for targeted marketing actions.
