[brockf]

At our data science company, we're building a marketing automation platform that uses deep reinforcement learning to optimize email marketing campaigns.

Marketers create their messages and define their goals (e.g., purchasing a product, using an app) and it learns what and when to message customers to drive them towards those goals. Basically, it turns marketing drip campaigns into a game and learns how to win it :)

We're seeing some pretty get results so far in our private beta (e.g., more goals reached, fewer emails sent), and excited to launch into public beta later this month.

For more info, check out https://www.optimail.io or read our Strong blog post at http://www.strong.io/blog/optimail-email-marketing-artificia....

[mitbal]

That's very interesting case. In my company, we would also like to optimize email marketing campaign using RL. However, based on my little experience using RL, (please correct me if I'm wrong) wouldn't it take long to iterate and update the V and policy function (or Q function if we use Q-learning), so I'm a bit skeptical if it can be used for real world case where we need to wait days to get the email response as feedback from the environment.

[brockf]

Great points. It's definitely more challenging than learning to play a simple arcade game or something, where feedback is invariant and often instantaneous. To address these challenges, we use a combination of (1) heuristics tailoring our RL algorithms to the problem at hand, (2) many converging sources of feedback. Most importantly, as with any machine learning implementation, it works in practice — our AI-driven campaigns beat randomized, control conditions!

[IndianAstronaut]

I was doing something similar in email marketing. Used decision tree models with a lot of feature engineering to help predict email open rates.