I recently read an article on Sociomantic, “Customer Lifetime Value in Three Dimensions,” about looking at lifetime value (LTV) tied to the customer journey and it adds another dimension to calculating lifetime value that could greatly improve its predictive value for you as well as pointing to areas for improvement. The article breaks LTV into three dimensions: recency, frequency and profitability (Note: The authors refer to the third dimension as “monetization.” Based on my previous posts on monetization, I felt this term would confuse my readers, as our definitions differ).
The article points out that a key determinant of recency is when the customer last made a purchase (or in a game, last monetized). When examining the recency dimension of your customers, you should analyze which cohorts purchased which items. With this information, you can then predict subsequent purchases, including what items each cohort (actually each customer) is likely to purchase. This analysis provides an LTV for each cohort (or even customer) and can power a machine learning recommendation engine or post-purchase retargeting engine that would increase LTV. Continue reading “Lifetime Value Part 21: 3D LTV”