Predict Player Lifetime Value in Gambling & Casinos

Business Problem

Regardless of the market or the customer, being able to gauge a customer’s potential value to the business is crucial. In the legal gambling industry, some 3 of the core customer acquisition strategies are customer referral bonuses, new customer incentives, and customer loyalty rewards programs. All of these are tied to monetary value, so understanding the ROI of these strategies in terms of customer LTV is especially important. According to a 2003 study of casino CRM initiatives, casinos that increased retention by 5% were able to increase profitability by as much as 50-75%, which suggests that retention has a disproportionately large impact on LTV. This is true in many markets, but gambling and casinos are a prime example due to the tremendous impact of good player retention and loyalty programs on performance. Additionally, outside of select customer loyalty programs, there are relatively low switching costs between casinos and/or sportsbooks. This is part of why retention strategies are so important, but good customer acquisition and retention strategies are not sufficient on their own. Holistically understanding and isolating the core drivers of LTV is very challenging, but the same 2003 study found that the top 20% of customers accounted for over 80% of revenues, so it is more than likely worth the challenge.

Intelligent Solution

AI will empower your casino (or online casino platform) to understand your customers like never before. Projecting the LTV of an individual customer will allow your client service leaders to granularly prioritize individual customers and/or customer segments. Take your customer retention strategy to the next level by identifying high-potential customers and establish competitive advantage by finding these players and locking them in before it is obvious to competitors. AI enables you to synthesize your historical player data, known attributes of those players, and any other industry or game-specific information your organization finds valuable to identify patterns associated with additional value and/or risk. After creating a dataset with your desired list of variables, you can use those attributes against past player LTV / revenue outcomes using machine learning to identify behavioral patterns of different players, seasonal effects, the impact of different promotional offers, etc.