Predicting Customer Lifetime Value in the Hospitality Industry

Introduction

Predicting Customer Lifetime Value (CLV) is crucial for businesses in the hospitality industry to tailor marketing strategies and enhance customer retention. This project proposal outlines a system for predicting CLV using advanced machine learning techniques, drawing inspiration from recent research.

Background

The hospitality industry is highly competitive, and understanding CLV can provide a significant advantage. Recent studies have demonstrated that machine learning models can effectively predict CLV by analyzing customer behavior and transaction data. These models help businesses allocate resources efficiently and personalize customer interactions.

Project Objective

The primary objective of this project is to develop a predictive model for CLV in the hospitality sector. The model aims to improve accuracy over traditional methods by incorporating diverse data sources and leveraging machine learning algorithms.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize publicly available datasets from sources like Kaggle or specific hospitality industry data repositories. These datasets should include customer demographics, transaction history, and interaction data.
• Data Cleaning: Ensure data quality by handling missing values, outliers, and inconsistencies.

2. Model Development

• Feature Engineering: Identify key features influencing CLV such as frequency of visits, average spending, and customer feedback.
• Machine Learning Models: Implement various algorithms including regression models, decision trees, and ensemble methods like Random Forests and Gradient Boosting Machines to predict CLV.

3. Training and Evaluation

• Training: Use historical data to train the models with techniques such as cross-validation to ensure robustness.
• Evaluation Metrics: Assess model performance using metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared.

Expected Outcomes

The proposed model is expected to provide accurate predictions of CLV, enabling businesses to make informed decisions regarding marketing strategies and resource allocation. By utilizing machine learning techniques, the model should adapt to changing customer behaviors over time.

Conclusion

This project aims to advance the prediction of customer lifetime value in the hospitality industry by developing a sophisticated model that integrates various data sources and machine learning algorithms. The anticipated outcome is a tool that significantly enhances strategic decision-making for businesses in this sector.

For further details on related research, please refer to the paper "Predicting Customer Lifetime Value in Hospitality Industry," available at sciencedirect.com/science/article/pii/S2351978920315891.