Customer lifetime value (CLTV) is the expected profit that a single customer brings to a company over the course of their lifetime. CLTV represents a shift in emphasis from quarterly or annual profits to the long-term relationships with customers....
CLTV is an important metric for determining a reasonable customer acquisition cost (CAC). In short, many companies spend too many resources on growth through acquiring new customers and not enough on retaining existing ones.
Subsequently, to ensure continued profitability, companies must keep CLTV larger than CAC. A good CLTV model allows companies to easily monitor these parameters, enlarge the CLTV, and stay profitable.
Calculating CLTV is based on average purchase value, average purchase frequency rate, and average customer lifespan. Further, to improve the CLTV, a good model should also use data from sales history, customer maintenance history, and customer feedback.
Above all else, competitor data is necessary for a good CLTV model. Include, for example, information about new competitors in your market segment, significant changes in your competitors’ statuses, and your competitors’ average customer lifespan and CAC.
Finally, include any data about the customer, online or offline. For example, ethnographic and demographic data can prove very useful when building CLTV models and analyses.
Additional useful data include psychological analyses relating to customer retention and improvements in customer satisfaction.
The main challenges of CLTV are predicting and calculating customer lifespan (especially for young companies) and the analyzing the efficacy of different complex marketing channels. Similarly, accurately calculating gross profit margins is very challenging.
Data-driven CLTV prediction models have been shown to achieve accuracy of about 80-90% while other models have achieved near-perfect accuracy. For more details, click through to this article: Towards Data Science: Measuring users with Customer Lifetime Value (CLTV): an example with complete Python code
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