Abstract
Customer Lifetime Value (CLV) represents the total worth of a customer to a company over time, aiding businesses in resource allocation and tailored marketing for profitability. This literature review fills a research gap by examining how customer risk factors are integrated into CLV calculations. We conducted a systematic literature review across databases, adhering to strict criteria for relevance and quality. The review analyzed CLV methodologies and outcomes, highlighting the use of mean–variance analysis to optimize customer portfolios, with customer income fluctuations identified as a major risk factor. The study also explores the evolution of CLV research, particularly in the application of Machine Learning (ML) for risk-adjusted CLV. Our findings offer a comprehensive overview, laying the groundwork for future research and helping businesses refine risk management strategies, identify high-risk customers, and enhance customer value through more dynamic, data-driven models.
| Original language | English |
|---|---|
| Article number | 100279 |
| Journal | International Journal of Information Management Data Insights |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Nov 2024 |
Keywords
- UT-Gold-D
- Information systems
- Machine learning
- Risk-Adjusted Revenue
- Systematic literature review
- Customer value
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