Behavioral Segmentation to Boost ROI

The Problem and Need for a New Approach

Our client, the world’s most diversified casino-entertainment provider, had previously relied heavily on a methodology that predicted their customer’s average daily worth (ADW) based only on previous customer trip spends. This resulted in the inefficient allocation of their annual marketing budget, leading to reduced returns on their investment and mis-targeted reward dole-outs. Given the challenges, there was a clear need for an innovative Segmentation, Targeting and Positioning (STP) marketing model to optimize marketing expenditure and increase ROI.

The Mu Sigma Solution and Impact

Mu Sigma proposed a comprehensive STP marketing model. A new ‘customer trip’ definition was introduced to capture insights from frequent customers visiting various properties in a single trip. A customer segmentation model was also formulated considering daily potential spending and trip frequency. Customers were classified based on spending values (High, Medium, Low) and engagement levels (High, Medium, Low). A predictive model was then developed to calculate customers’ likelihood of fitting into these spending categories. A reclassification algorithm was introduced based on specific zip codes in cases where predictions were inaccurate. The insights from prior customer trips were also used to create a scoring framework to predict future trip spending and guide the client’s targeting strategies.

The new STP marketing model developed a comprehensive customer segmentation framework and a scoring mechanism to predict a customer’s next trip spend. For instance, customers with medium trip frequency showed reduced churn rates, and females exhibited a lower likelihood of churn than males. Recommendations on targeting highlighted the importance of compensating customers during their hotel stays to maintain engagement. Also, refining the target customer list based on the STP model was essential. Positioning insights demonstrated that factors like days spent at the casino, amenities provided, and the type of casino influenced the customer’s spending behavior. Our model also allowed the client to target potential customers with exciting offers to increase their next trip spending.