TY - JOUR
T1 - How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review
AU - Firmansyah, Edo Belva
AU - Machado, Marcos R.
AU - Moreira, João Luiz Rebelo
N1 - Publisher Copyright:
© 2024 The Author(s)
PY - 2024/11
Y1 - 2024/11
N2 - 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.
AB - 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.
KW - UT-Hybrid-D
KW - Information systems
KW - Machine learning
KW - Risk-Adjusted Revenue
KW - Systematic literature review
KW - Customer value
UR - http://www.scopus.com/inward/record.url?scp=85202885839&partnerID=8YFLogxK
U2 - 10.1016/j.jjimei.2024.100279
DO - 10.1016/j.jjimei.2024.100279
M3 - Review article
AN - SCOPUS:85202885839
SN - 2667-0968
VL - 4
JO - International Journal of Information Management Data Insights
JF - International Journal of Information Management Data Insights
IS - 2
M1 - 100279
ER -