Customer Lifetime Value Prediction added

[AMAN1011011]

#628 resolved

Project Overview

The objective of this project is to predict the Customer Lifetime Value (CLTV) using historical transaction data. By accurately predicting CLTV, businesses can better understand customer value, identify high-value customers, and optimize marketing strategies to enhance profitability.

Objectives

Time Frame Selection: Define an appropriate time frame for CLTV calculation (6 months in this case). Feature Identification: Identify and create features for predicting CLTV, including RFM scores. CLTV Calculation: Calculate the historical CLTV for training the machine learning model. Model Building: Train and evaluate a machine learning model to predict CLTV. Model Evaluation: Assess the model's usefulness and accuracy in predicting CLTV.

Dataset Description

CustomerID: Unique identifier for each customer. InvoiceDate: Date of the transaction. InvoiceNo: Unique identifier for each transaction. Quantity: Quantity of items purchased. UnitPrice: Price per unit of the items. TotalCost: Total cost of the transaction. GrossRevenue: Revenue generated from the transaction.

Use Cases

Customer Segmentation Targeted Marketing Campaigns Churn Prediction Budget Allocation for Customer Acquisition Revenue Forecasting

Benefits

Enhanced Customer Understanding Optimized Marketing Strategies Increased Customer Retention Improved Resource Allocation Higher Profitability

By systematically exploring, preprocessing, and modeling the data, this project aims to develop a robust predictive model for CLTV and provide valuable insights into customer value, enabling better business decisions and strategies.