Calculating Customer Lifetime Value through Data Analysis

Abstract

Customer Lifetime Value (CLV) is a crucial metric for businesses to understand the long-term value of their customers. This research paper delves into the significance of CLV, explores various methods for calculating it through data analysis, and discusses the implications of accurate CLV estimation on business strategies. By leveraging data analytics techniques, businesses can gain valuable insights into customer behaviour, preferences, and profitability, ultimately leading to improved decision-making and enhanced customer relationships. Customer Lifetime Value (CLV) is a vital metric for businesses to comprehend the long-term value of their customers. The research paper highlights several key points regarding CLV: Calculation Factors: CLV calculations should consider indirect costs like administrative, marketing, and legal costs, not just direct expenses. Limitations: CLV based solely on spending may not fully represent a customer's value, as factors like brand advocacy, referrals, and feedback also contribute. Historical Data: CLV is often calculated using past customer data, which may not accurately predict future customer behaviours due to changing trends and customer preferences. Pareto's Principle: This principle, known as the 80/20 rule, can be applied to CLV to identify high-value customers and key factors influencing CLV. Overall, the paper emphasizes the importance of understanding CLV beyond mere spending, considering historical data limitations, and utilizing tools like Pareto's Principle to optimize customer value and business strategies.