I was asked recently how to prioritize new customers if you do not have demographic or firmographic data available. In other words, what can you do with just the data from the first purchase with which to work?
To make this more concrete, let’s consider the following situation. You are asked to call each and every new customer who has made a purchase. The question is, how do you prioritize the calls? You want to make the first calls to those with the greatest potential to become loyal and valuable customers. The only data available relates to the first purchase: total revenue generated, products purchased, product revenue, etc.
In this case, a linear regression could be used to help you identify the factors that predict lifetime value. (Other types of models can be used depending on the independent and dependent variables available.) Using your existing customer base, build a model that leverages data about the first purchase to predict lifetime spending. You can identify the best and worst new customers using the resulting model equation. Armed with this insight, you can test your model by calling on new customers with the best predicted lifetime revenue and a random selection of new customers regardless of predicted lifetime revenue. In addition, you can test call back timing to determine if there is an optimal call back window.
Even with limited data, analysis can lead to insight. Further, there is always an opportunity to incorporate testing. In this case, testing can validate initial findings and help you learn more about the purchase cycle.