Customer segmentation
Data-driven segmentation to replace generic campaigns with targeted marketing actions.
Problem
A business wants to better understand its customers to move away from generic campaigns and target marketing actions based on behavior and value. The goal is to identify groups of customers with similar characteristics and turn them into actionable segments.
Data source
Practice project using a public or simulated dataset of customers and transactions. It may include purchase frequency, spend, recency, products purchased, channel, and other variables available in the chosen source.
Analysis
The data will be cleaned and purchase behavior analyzed using recency, frequency, and monetary value (RFM), and — where the dataset allows — clustering techniques to compare profiles and define clearly differentiated segments.
Solution
A customer segmentation will be developed with easy-to-interpret profiles and marketing recommendations for each group — for example retention, reactivation, loyalty, or increasing purchase frequency.
Results & impact
Practice project. The output will be a data-backed segmentation and action proposals for each group. No increases in conversion, sales, or retention will be presented as real results without an implemented and measured campaign.