An eCommerce business wants to (1) identify customers before they become inactive and create targeted marketing campaign to keep customers active with incentives & (2) wants to keep customers active in purchasing by personalizing promotions based on their frequency/spending. The marketing outreach can be prioritized for each group of customers based on the recency of purchases and customer promotions can be personalized based on how often customer purchases and the average amount spent.
In this project, would be leveraging on customer segmentation techniques such as RFM, K-Means to target specific groups of customers and personalize promotions for each group.
Please access jupyter notebooks in the repository.
Python: Pandas, Scikit-learn, Matplotlib, Seaborn.
Online Retail Data, UCI Machine Learning Repository. Available: https://archive.ics.uci.edu/ml/datasets/online+retail.