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Predicting Customer Lifetime Value in E-commerce

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    Project Mart
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Introduction

Predicting Customer Lifetime Value (CLV) is a crucial aspect of e-commerce that helps businesses understand the long-term value of their customers. By accurately predicting CLV, companies can tailor their marketing strategies, optimize resource allocation, and improve customer retention. This project proposal presents a machine learning-based approach to predict CLV in e-commerce, drawing inspiration from recent advancements in the field.

Background

Recent research has demonstrated that machine learning models can significantly enhance the accuracy of CLV predictions. These models analyze customer behavior data, such as purchase history, browsing patterns, and demographic information, to forecast future spending. Techniques such as regression analysis, decision trees, and neural networks have been effectively employed to capture complex relationships within the data.

Project Objective

The primary objective of this project is to develop a predictive model that accurately estimates the CLV of e-commerce customers. This model aims to improve upon existing methods by incorporating advanced feature engineering and leveraging large-scale customer datasets.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize publicly available datasets such as the UCI Machine Learning Repository's Online Retail dataset for training and evaluation.
  • Feature Engineering: Extract relevant features including purchase frequency, average order value, recency of purchases, and customer demographics.

2. Model Architecture

  • Machine Learning Models: Implement various models including linear regression, random forests, and gradient boosting machines to predict CLV.
  • Neural Networks: Explore deep learning models for capturing non-linear relationships in the data.

3. Training and Evaluation

  • Training: Use appropriate loss functions such as mean squared error for training the models.
  • Evaluation Metrics: Measure performance using metrics like mean absolute error (MAE), root mean squared error (RMSE), and R-squared.

Expected Outcomes

The proposed system is expected to provide accurate CLV predictions that can help e-commerce businesses enhance their customer relationship management strategies. By utilizing machine learning techniques, the system should effectively handle diverse customer behaviors and provide actionable insights.

Conclusion

This project aims to advance the field of customer analytics by developing a robust system capable of predicting CLV in e-commerce settings. The integration of various machine learning models is anticipated to yield significant improvements in prediction accuracy.

For further details on related research, please refer to the paper "Predicting Customer Lifetime Value in E-commerce," available at https://ieeexplore.ieee.org/document/8776942.

Dataset link: Online Retail Dataset

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