Author Archives: Matt Gershoff
Thompson Sampling or how I learned to love Roulette
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 will give you a basic idea of how adaptive learning works, at least here at […]
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Going from AB Testing to AI: Optimization as Reinforcement Learning
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 as a set of useful computational tools to help us solve online optimization problems. Video […]
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1 Comment
Machine Learning and Human Interpretability
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 multivariate testing tools, but also multi-armed bandits, predictive targeting, and a type of multi-touch decision attribution […]
Conductrics 3.0 Release
Today I am happy to announce Conductrics 3.0, our third major release of our universal optimization platform. Conductrics 3.0 represents the next generation of personalized optimization technology, blending experimentation with machine learning to help deliver the best customer experiences across every Marketing channel. Conductrics 3.0 highlights include: Conductrics Express – You asked and we listened. While many […]
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Segmentation and Shrinkage
In our last post, we introduced the idea of shrinkage. In this post we are going to extend that idea to improve our results when we segment our data by customer. Often what we really want is to discover what digital experience is working best for each customer. A major problem is that as we segment […]
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Prediction, Pooling, and Shrinkage
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 (same, both good), and the Frequentist vs. Bayesian testing argument (also, both good). In the […]
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Easy Introduction to AB Testing and P-Values
UPDATED: 5/20/2021 A version of this post was originally published over at Conversion XL For all of the talk about how awesome (and big, don’t forget big) Big data is, one of the favorite tools in the conversion optimization toolkit, AB Testing, is decidedly small data. Optimization, winners and losers, Lean this that or the other […]
Posted in Analytics, Testing and Data Science, Uncategorized
2 Comments
Predictive Targeting: Managing Complexity
Personalization, one to one, predictive targeting, whatever you call it. Serving the optimal digital experience for each customer is often touted as the pinnacle of digital marketing efficacy. But if predictive targeting is so great, why isn’t everyone doing it right now? The reason is that while targeting can be incredibility valuable, many in […]
Posted in Analytics, Testing and Data Science, Uncategorized
3 Comments
Big Data is Really About the Very Small
Awhile back I put together a fun list of the top 7 data scientists before there was Data Science. I got some great feedback on others that should be on the list (Tukey, Hopper, and even Florence Nightingale). In hindsight I probably should have also included Edgar Codd. While at IBM, Codd developed the relational […]
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AB Testing: When Tests Collide
Normally, when we talk about AB Tests (standard or Bandit style), we tend to focus on things like the different test options, the reporting, the significance levels, etc. However, once we start implementing tests, especially at scale, it becomes clear that we need a way to manage how we assign users to each test. There […]
Posted in Analytics, Reporting, Testing and Data Science
Leave a comment
Thompson Sampling or how I learned to love Roulette
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 will give you a basic idea of how adaptive learning works, at least here at […]
Posted in Uncategorized
Leave a comment
Going from AB Testing to AI: Optimization as Reinforcement Learning
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 as a set of useful computational tools to help us solve online optimization problems. Video […]
Posted in Uncategorized
1 Comment
Machine Learning and Human Interpretability
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 multivariate testing tools, but also multi-armed bandits, predictive targeting, and a type of multi-touch decision attribution […]
Conductrics 3.0 Release
Today I am happy to announce Conductrics 3.0, our third major release of our universal optimization platform. Conductrics 3.0 represents the next generation of personalized optimization technology, blending experimentation with machine learning to help deliver the best customer experiences across every Marketing channel. Conductrics 3.0 highlights include: Conductrics Express – You asked and we listened. While many […]
Posted in Uncategorized
Leave a comment
Segmentation and Shrinkage
In our last post, we introduced the idea of shrinkage. In this post we are going to extend that idea to improve our results when we segment our data by customer. Often what we really want is to discover what digital experience is working best for each customer. A major problem is that as we segment […]
Posted in Uncategorized
Leave a comment
Prediction, Pooling, and Shrinkage
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 (same, both good), and the Frequentist vs. Bayesian testing argument (also, both good). In the […]
Posted in Uncategorized
Leave a comment
Easy Introduction to AB Testing and P-Values
UPDATED: 5/20/2021 A version of this post was originally published over at Conversion XL For all of the talk about how awesome (and big, don’t forget big) Big data is, one of the favorite tools in the conversion optimization toolkit, AB Testing, is decidedly small data. Optimization, winners and losers, Lean this that or the other […]
Posted in Analytics, Testing and Data Science, Uncategorized
2 Comments
Predictive Targeting: Managing Complexity
Personalization, one to one, predictive targeting, whatever you call it. Serving the optimal digital experience for each customer is often touted as the pinnacle of digital marketing efficacy. But if predictive targeting is so great, why isn’t everyone doing it right now? The reason is that while targeting can be incredibility valuable, many in […]
Posted in Analytics, Testing and Data Science, Uncategorized
3 Comments
Big Data is Really About the Very Small
Awhile back I put together a fun list of the top 7 data scientists before there was Data Science. I got some great feedback on others that should be on the list (Tukey, Hopper, and even Florence Nightingale). In hindsight I probably should have also included Edgar Codd. While at IBM, Codd developed the relational […]
Posted in Uncategorized
Leave a comment
AB Testing: When Tests Collide
Normally, when we talk about AB Tests (standard or Bandit style), we tend to focus on things like the different test options, the reporting, the significance levels, etc. However, once we start implementing tests, especially at scale, it becomes clear that we need a way to manage how we assign users to each test. There […]
Posted in Analytics, Reporting, Testing and Data Science
Leave a comment
