This project is a part of the Tasks issued to me by ParallelScore.
The objective of this study is to optimally cluster the customer data to get a clear view of various customers, develop customized marketing campaigns, choose specific product features for deployment and Prioritize new product development efforts.
After proper cleaning of our data to remove 543 null rows and 2 duplicate rows we arrived at a dataset of 10 features and 2452 records with 3 categorical and 7 numerical columns
- Pandas for data manipulation
- Numpy for mathematical calculation and analysis
- Seaborn and Matplotlib for visualization and insights
- Sklearn for Machine Learning and preprocessing
- Python 3.7 Environment
- Jupyter and Microsoft Excel as tools
- Inferential Statistics
- Machine Learning
- Data Visualization
- Predictive Modeling
- etc.
- Business Analytics
- data exploration/descriptive statistics
- data processing/cleaning
- statistical modeling
- writeup/reporting
- etc. (be as specific as possible)
- Data Cleaning.
- Exploratory data analysis
- Clustering
- Improved clustering using RMF analysis
- Conclusion and visualization
RFM and K-means clustering technique is a proven marketing model that helps e-commerce and fintech businesses maximize profit margins through better understanding of various customer segment. After much data manipulation I arrived at this well-defined segment in the various users of our product below.
- Core/Loyal - Your Best Customers Who They Are: Highly engaged customers who have bought the most recent, the most often, and generated the most revenue
- Slipping - Once Loyal, Now Gone Who They Are: Great past customers who haven't bought in a while
- Faithful - The customers having common behaviour across these metrics. Who They Are: Customer who have average metrics across each RFM scores
- Lost - The customers with low recency, frequency and monetary value
The above-generated RFM customer segments can be easily used to identify high ROI segments and engage them with personalized offers.
