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This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.
Using real online retail data from a store, customer value was predicted and segmented using two probability models: BG/NBD and Gamma-Gamma. An example was used to illustrate the implementation process of the customer lifetime value analysis model and segmentation model.
This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.
A retail company wants to create a roadmap for its sales and marketing activities. To plan for the medium to long term, the company needs to predict the potential value that existing customers will bring in the future.
CRM analytics is the process of analyzing customer data to gain insights for improving customer relationships and driving business growth. We use it to make data-driven decisions, optimize customer engagement, personalize marketing, identify opportunities, and enhance customer satisfaction.