Customer lifetime value (CLV) – A component of CRM

Customer Lifetime Value (CLV) is one of the four components of Customer Relationship Management along with key account management, customer portfolio analysis and the relationship lifecycle. CLV is defined as the difference between what it costs to acquire, service, and retain a customer and the revenue generated from that customer over the customer lifecycle.

The main concept of CLV is the total customer portfolio, which reflects the total customer value generated for the firm in the form of enhanced profitability. Within this context, managers think in terms of long-term relationships instead of isolated transactions, while keeping in mind that customers are likely to alter their behavior due to competitive factors and/or change in their needs and wants.

CLV is increasingly used as a key metric in customer-centric organizations for several reasons. Primarily it has a straightforward concept which is easy to understand. Undoubtedly, it makes good business sense to identify the most and least profitable customers and aim marketing efforts at the most valuable ones. By applying customer classification into high, medium, and low-value customers, CLV allows for product/service differentiation according to expected customer value. In that way, the Customer Relationship Management experts gain substantial insight into marketing decision-making and they are able to allocate scarce resources among selling efforts and service levels. Finally, the calculation of CLV is straightforward (net present value of the future cash flows of a profitable customer).

Despite above mentioned advantages of CLV, few companies succeed to accurately calculate it due to shortcomings and limitations. Primarily, CLV requires the firm to grasp the long-term potential value of the customer, which insinuates projecting effectively the revenues and costs into the future. However, the statistical techniques required for the forecasting and modeling of future customer behavior in terms of spending rate & frequency are subject to statistical error and they are not so straightforward in their use. Moreover, useful CLV analysis requires five to ten years of transaction data. Hence, CLV analysis becomes inappropriate for new firms and new products where historical data do not exist and variations in customer behavior cannot be identified. Generally, CLV system requires highly trained personnel capable to model customer response behavior as CLV figure is biased on a variety of assumptions made by the manager as well as a wide choice of various parameters, which eventually make CLV an uncertain estimate.