This may seem like nitpicking, but I am always worried when I look at a website and the blog hasn't been updated for months. Even a short "Hey guys! We're working on stuff!" would assuage my fears that you've turned into a zombiecorp. =)

How does it work? "the latest algorithms being discovered at leading academic institutions" reads like marketing fluff to me.

Whoops! Thanks for the feedback! That's an old page that was supposed to be replaced on our latest redesign.

The simplest explanation is pattern matching: we analyze customer and transactional data to understand how different customers behave. Using this understanding we can make predictions for how each user will behave in the future.

We use all of that analysis to power actions - take actions on the right user at the right time, optimizing for CLV.

Here's more: https://www.custora.com/home/customer_lifetime_value

That says a little bit more. Do you treat it as a reinforcement learning (RL) problem, or as a classification problem? It seems like a sequential decision making problem, so RL is appropriate but AFAIK there is no RL algorithm that generalises over states while still retaining some error bounds. I suppose you could brute-force a Bayesian solution via MCMC.

There are two big problems that we deal with. First is estimation of customer lifetime value. We use a latent attrition model, which is the 'pattern matching'

The second is figuring out which promotions/emails go to which people. This is a supervised learning problem. We train the model with users past responses to discounts and their past behavioral states (which are the posterior probabilities from the latent attrition model). Then we use this to predict how users in those states will respond to similar promotions in the future.

Are your predictions visible in the web-interface so we can verify if they hold water?

Yes. In addition to the lifecycle marketing product, we have a section of the application devoted to predictive analytics.

Thanks, that's interesting.