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Optimizing offers

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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. The common components of marketing involve offers for rooms, restaurants, retail, and gaming (i.e., free play). 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 and generate the most profitable response.

A/B Testing

The most basic way to identify the best offer is through A/B testing. 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 to whether 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. Additionally, it’s a good idea to select test segments of patrons 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. There are at least three main uses of response modeling that can improve marketing results:

  1. Identify the likelihood of patrons to respond to the offer
  2. Identify the offer(s) to which patrons are most likely to respond
  3. Predict when a patron is likely to return