Machine Learning That Drives Real Optimization

The Problem with A/B Testing

A/B testing was online marketing’s original metrics milestone.

If a business had two marketing campaigns and wanted to see which performed best, it would run both, giving 50% of customers campaign A, and 50% campaign B. After 100,000 visits, a winner was chosen, which was good news.

The bad news was during the test 50,000 visitors saw a loser, and were therefore less likely to convert.

An Untapped Repository of Data

Not only that, but before, during, and after the test, customers were providing even more data about how to target and improve the campaigns. The devices they used, the links they clicked, their locations, and other data points were all vital but were neither collected nor acted upon in a standard A/B test scenario.

But with Cordial Experiments powered by machine learning, your campaigns can listen, learn and adjust to all the data customers provide—optimizing in real time, and automatically.

Enter Cordial Experiments

With Cordial Experiments, your campaigns will improve on-the-fly as you test variables like offers, landing pages, price, and scheduling. Experiments learns from customer responses to those variables, and adapts to the new learnings.

Unlike standard A/B testing—where a business must wait until the conclusion of a test to take action and, unfortunately, expose half its test base to the eventual loser of the test—Cordial Experiments enables you to move a critical mass of customers to the winning variation the moment a clear winner is established.

Serving Customers Better, Faster

No casualties are lost to time-consuming testing run over long periods of time. As your options are tested and winners identified, more customers are served the winning option, driving the response that you want.

Cordial Experiments can mean less lost revenue, more relevant communications, and higher customer lifetime value.

See the difference Cordial can make.

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