In this project, I was given extensive data on the bank's customers over a 6 month period. In this data provided, it includes the customer's transaction frequency, amount, tenure of being a customer with the bank, and many more. The data is provided in the repository. The data can also be found on kaggle (https://www.kaggle.com/arjunbhasin2013/ccdata)
The task is to use the data provided to help launch a targeted marketing ad campaign that is designed for specific customer profiles. Part of this marketing program involves designing ads for 4 specific groups of customers (i.e. Transactors, Revolvers, New Customers, VIP/Prime). This targeted marketing should maximize the marketing campaign conversion rate.
To achieve this objective, I designed an unsupervised learning model using Principal Component Analysis and K-Means to train the data, and obtained the optimal number of clusters using the elbow method to identify the clusters and prominent features associated with each cluster to better understand each customer segment.
The result was 4 customer types, each with distinct features that the marketing team can use to design targetted ads.