How Are RevTrax A.I. Offers Unique?

Artificial Intelligence and Machine Learning have been popular buzzwords for quite some time now.  At RevTrax, it’s much more than a buzzword.  We’re applying differentiated A.I. and Machine Learning techniques that can help brands convert consumers while maximizing the ROI on moving products off the shelf.

But how is this different from the other A.I. solutions out there? Many other solutions rely on A.I. to enhance retargeting and segmentation techniques via lookalike modeling. It’s based mainly on perceived behavior and projected behavior of other consumers. RevTrax has a different approach and it can be summed up with one word.


Everything we do is based on causality and not just behavior and lookalike models (though that is part of the equation). While behavior is important, we’re determining the factors that caused a certain action which, in our case, is someone engaging with an offer and ultimately making a purchase.

Many lookalike models are based purely on behavior and that behavior is classified. Consumers that visit websites that other consumers visit or view the same content are often lumped into the same segments for targeting or retargeting purposes. There’s nothing wrong with this approach but we feel we can do better.



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By expanding on the traditional use of machine learning to classify yet-to-be observed consumers we use causal analysis to determine the features of promotions that are generating the behavior.  Using this strategy, we can determine what type of offer, if any, is going to motivate the appropriate action for this individual consumer and tailor the experience as such. And all of this happens in real-time.

So, what does this mean? It means we’re not just segmenting users into groups based on behavior or based on the behavior of consumers that “look like them.” Instead, we’re classifying the cause of that behavior. With RevTrax A.I. we know, with a high degree of confidence, what caused a certain action to be taken, or not taken. Based on that knowledge, we can expand the number of variables in the equation to provide a richer dataset and solution to our clients. 

While the expansion of the dataset is more expensive, we feel the expense is outweighed by the performance and ROI attained by using such a model.  It took a while to crack the code on the math, but we feel this approach will provide far more benefits and ultimately empower all brands to deliver optimized offers to all of their consumers in real-time.


Greg Hansen is the CTO & Co-founder of RevTrax

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