Casino gaming is a fast-paced industry, often pursuing state-of-the-art technology to improve the player experience. However, when it comes to their loyalty marketing programs, most casino gaming providers have stuck to traditional tier-based direct marketing approaches.
Specifically, following the traditional approach, patron data are analyzed on a regular (typically, monthly) cadence to determine each player's value based on how much patrons tend to spend when they are in the casino. Players are then "tiered" into low- to high-value buckets based on those values. Finally, promotional offers such as free-play and restaurant and hotel discounts are allocated to tiers, with players receiving notifications about their new offers via direct mail or email.
There are several disadvantages associated with this traditional approach. First, the process is very slow, as promotions are only being sent out about once per month. Second, the process is often very manual, requiring a lot of effort on the part of those involved in the promotional distribution process. Third, the process is rather static in the sense that it is not responsive to recent changes in player behavior. If promotional messaging is only sent out monthly, it is likely that the player information is outdated by the time the monthly mailer is finished being prepped. Finally, the process lacks personalization since users are broken down into discrete buckets rather than individual players getting customized offers.
In a recent project, Strong worked with a leading casino gaming provider to build a one-to-one marketing engine for their players. This platform, the most ambitious of its kind, continuously and automatically learns from players behaviors both online and in-casino to build and nurture player relationships through providing personalized, highly-responsive offers.
Separating Signal from the Noise to Optimize Actions
In a world driven by the excitement of random chance, separating the signal from the noise is no easy task. Thus, to build an engine capable of learning how to engage players at the individual, one-to-one level, we need to consider the many facets of player engagement.
Moreover, while many projects are interested in merely predicting what a player will do in the future, we needed to go on mere observation to optimize action. Given everything we know in general and about this particular player, how should we incentivize and communicate to them in order to best nurture the relationship?
We built a custom solution using our end-to-end platform for distributed reinforcement learning at scale, Strong RL. This multi-channel "next best action" platform streams in data from multiple sources, builds a quantified snapshot of each player in real-time, and streams out optimal actions that maximize player engagement — including incentives and personalized communication across email, direct mail, online, in-app, and SMS channels.
The results have been impressive. Not only did the system enhance players' experience through a highly-personalized offers that responded to quick changes in players' play, experimental testing revealed that it improved player profitability for both new players and existing players migrated from the traditional approach.