Revenue Optimization

At the request of a client, we brought in a second variable to be used in conjunction with an existing metric. The result was a new metric that was 0.9% more likely to identify a paying customer. What would an extra 0.9% of revenue do for your business?

This project involved dividing previous attempts to acquire customers into deciles for each variable. A color-coded Excel sheet was made to explain an identified pattern to the client. After a pattern was identified, a model was made with a parameter, and a Xi square fit was used to determine the best value of the parameter. The business then included this new metric as a calculated field in their existing database.