Project title: Credit Card Customer Segmentation and Behavior Analysis

Context: Analyzed 7,000+ credit card transactions from a course dataset, which I cleaned and preprocessed, to understand customer value and spending behavior and to design differentiated marketing and promotion strategies.

My role: Individual project. I built a structured dataset from the raw data, ranked customers with an RFM model, and designed two custom indicators for activity (CAI) and stability (CRI) to track behavior changes over time.

Methods and tools: R, SPSS, Excel. RFM, K-means clustering, ANOVA, chi-square tests, t-tests.

Outcome: A single demographic variable such as age or gender is a limited basis for targeting. Adding behavioral indicators made differences between customer groups visible over time and supported more precise resource allocation. For example, within the "about to churn" segment, customers aged 21-30 showed a declining CAI and a significantly stronger churn trend than other age groups. I recommended targeted campaigns for this group, such as partnering with apparel brands they prefer and offering rewards to raise card usage.