Author Archives: Matt Gershoff

Conductrics Announces Updated Release of its SaaS Experimentation Platform

 SEPTEMBER 24, 2020 – AUSTIN, TX – Digital experimentation and Artificial Intelligence (AI) SaaS company Conductrics, today announced its latest major release of its experience optimization platform built expressly for marketers, developers, and IT professionals. The updated platform, which shares the company’s name, is a cloud-based A/B testing and decision optimization engine. This latest release […]

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Headline Optimization at Scale: Conductrics Macros

Conductrics has a history of providing innovative approaches to experimentation, testing and optimization.  In 2010, we introduced one of the industry’s first REST API for experimentation which also supported multi-arm bandits and predictive targeting.  Continuing our goal to provide innovative solutions, we’ve developed Express Macros and Templates to safely and easily build tests at scale. […]

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Getting Past Statistical Significance: Foundations of AB Testing and Experimentation

How often is AB Testing reduced to the following question:  ‘what sample size do I need to reach statistical significance for my AB Test?’  On the face of it, this question sounds reasonable. However, unless you know why you want to run a test at particular significance level, or what the relationship is between sample […]

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What is the value of my AB Testing Program?

Occasionally we are asked by companies how they should best assess the value of running their AB testing programs. I thought it might be useful to put down in writing some of the points to consider if you find yourselves asked this question. With respect to hypothesis tests, there are two main sources of value: […]

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Do No Harm or AB Testing without P-Values

A few weeks ago I was talking with Kelly Wortham during her excellent AB Testing webinar series.  During the conversation, one of the attendees asked if they just wanted to pick between A and B, did they really need to run standard significance tests at a 90% or 95% confidence levels? The simple answer is […]

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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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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 […]

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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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