Test, Learn, Optimize
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At Conductrics we believe that intentional use of machine learning and contextual multi-armed bandits (MAB) often requires human interpretability. Why?1) Compliance – in many environments it is important to insure that policies are followed about how and when different people…
At Conductrics, two of our guiding principles for experimentation are:Principle 1: Be mindful about what the problem is, preferring the simplest approach to solving it. Principle 2: Remember that how data is collected is often much more important than how…
Multi-armed bandits, Bayesian statistics, machine learning, AI, predictive targeting blah blah blah. So many technical terms, morphing into buzz words, that it gets confusing to understand what is going on when using these methods for digital optimization. Hopefully this post…
In this post, we are going to introduce an optimization approach from artificial intelligence: Reinforcement Learning (RL). Hopefully, we will convince you that it is both a powerful conceptual framework to organize how to think about digital optimization, as well…
The key idea behind Conductrics is that marketing optimization is really a reinforcement learning problem, a class of machine learning problem, rather than an AB testing problem. Framing optimization as a reinforcement learning problem allowed us to provide, from the very beginning, not just AB and…
As some of you may have noticed, there are often little skirmishes that occasionally break out in digital testing and optimization. There are the AB test vs multi-armed bandits debate (both are good, depending on task), standard vs multivariate testing…
While AB Testing is currently the default method for optimizing online conversions, there are frameworks other than AB testing for selecting between competing optimization options. One of the main alternative approaches is known as the Multi-Armed Bandits problem. Solutions to…
