A marketing firm has been spending furiously on campaigns and various efforts, but it has had trouble determining if its dollars are well spent and channeled in the right direction. The company has been using its historic data to try to identify those customers who are most likely to offer the highest LTV, but so far its results have been mixed. Internal data can only provide a sliver of the information needed to determine how much a customer is likely to spend over their life cycle, and which are worth investing more marketing dollars into.
The company built a repeat customer lifetime value prediction forecasting model, as well as an LTV predictor, by connecting its internal datasets to Explorium, and yielded the following indicators:
Our customer is now able to better predict, following an initial sale, if their buyer will make another purchase, allowing them to place buyers on different tracks for marketing and nurture campaigns. Additionally, for repeat buyers, the marketing team can prioritize and place on more aggressive tracks those who are more likely to continue spending, while taking a longer, more subtle track with those whose LTV is not as high. Overall, the company saw an 18% increase in the number of repeat buyers, while reducing its cost per lead from $102 to $75.