Customer Segmentation using K-Means Clustering
As part of the requirements of completing the For The Women (FTW) Foundation Data Science Program, scholars were divided into groups with a data science practitioner as a mentor for the capstone project. Since real-world data from the sponsor companies were used, real-time communication and collaboration were required. Our team was assigned to analyze and create a customer segmentation for a health maintenance organization to be able to help them in their marketing efforts.
We were given the company's sales and membership data from 2019 to 2022 and these were analyzed through Excel, Python, and Tableau. Using K-Means Clustering, we were able to identify three personas based on their age, gender, civil status, location, and transaction history.
Team Members: Emily Latoja, Pamela Grace Toledo, Pamela Mildred Rosales, Naomi Grace Casuga
For more information regarding this project, you may send me an email or a message through LinkedIn.
