The project offers RFM segmentation, analyzing Recency, Frequency, and Monetary value. These metrics are vital for understanding customer behavior, influencing both retention and lifetime value.
E-commerce analytics involves collecting, analyzing, and interpreting data from an online store to make informed decisions. It encompasses tracking and measuring various key metrics and customer behaviors, such as: Conversion Rate: The percentage of website visitors who complete a desired action (e.g., making a purchase). Cart Abandonment Rate: The proportion of users who add items to their cart but do not complete the purchase. Customer Acquisition Cost: The expense incurred to acquire a new customer. Customer Retention Rate: The ability to retain existing customers over time. By leveraging these insights, businesses can optimize marketing strategies, enhance customer experiences, and boost overall profitability 123.
The goal of this project is to perform RFM analysis based on historical sales data and other relevant features.
Recency - How recently did the customer purchase?
Frequency - How often do they purchase?
Monetary - How much revenue do they generate?
Rank the customers based on their RFM score and segment them based on the score so as to effectively cater them in the future.
- Importing Necessary Libraries
- Understand data by performing exploratory data analysis
- Calculating the recency, frequency and monetary value for each customer
- Generating a RFM score for each customer
- Segmenting the customers based on their RFM scores