Optimizing offers

In addition to predicting the future worth of patrons, it is important to know which marketing campaigns are the most effective for driving response, revenue, and profit. In general, certain offers are better than others, and specifically certain offers will be better for certain patrons.

While knowing the probable future worth of a patron is critical for determining the reinvestment level for which a patron is eligible, patrons’ behaviors and interests can be used to identify the offer(s) that will be most appealing to each patron as well as the ones generating the most profitable response. By analyzing the likelihood that a patron will respond to a certain offer or offers, sports book analysts can optimize the offer that each patron is given in order to maximize the amount of revenue and profit driven by the marketing campaigns as a whole.

A/B testing is one of the best ways to identify which offers work best. A/B testing involves testing two different offers against one another in order to identify the offer that drives the highest response and the most revenue/profit. More advanced statistical methods can be used to generate likelihood of response scores and classification scores. Some of the more common statistical approaches are logistic regression, decision trees, and discriminant analysis.

Essentially, these statistical methods use historical data to find the factors that are related as to why a patron responds. Those factors can then be used to assess the likelihood of response based on the similarity of a patron profile to that of responders.

These methods have historically been used in direct marketing analysis to identify the best types of offers and the most likely responders.  In order to build accurate and predictive response models, historical data about response is required. The likelihood of response might be a broad measure of response that refers to the likelihood a patron will respond to any offer, or it might be specific to the likelihood of response to a specific type of offer.

It’s a good idea to select test segments of customers for the purpose of continually testing new offers. Doing so will help to ensure that there is a large amount of response data that can be used to build models and continually improve the efficacy of marketing. Effective response models will help identify which patrons are most likely to respond to an offer, and in turn to which offer patrons are most likely to respond.