Case study: One-to-one, multi-channel marketing optimization

Brock Ferguson
Co-Founder, Principal Data Scientist

How do you personalize promotional efforts in a fast-paced environment filled with unique, demanding clientele?

In a recent project, Strong tackled this question in a collaboration with a leader in casino gaming as we developed a one-to-one marketing engine for their customers. This platform, the most ambitious of its kind, continuously learns from players behaviors both online and in-casino to build and nurture player relationships.

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?

Our Solution

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.