Personalization Glossary

A/B testing? Recommendation models? The personalization engine space uses several acronyms that may seem daunting, so we're here to help. You can find definitions to the most common e-commerce customer experience optimization terms here!

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A/B Testing

A/B or split testing is a process of showing two variants of the same web page to website visitors to identify which version performs better. A/B testing can determine which variant is better optimized for improving ROI, performance, and conversion rate.

During testing, the two pages are required to be similar in every aspect other than the values to be tested.  For example, an eCommerce retailer might try testing a version of the site where the headline or call-to-action is different from the current site. if there is a significant uplift in the number of visitors clicking through to the newer version. and a proportionate increase in sales metrics we can assume that implementing the newer version will produce better results

It’s important to allow adequate time for each test to be completed and weigh all factors that may impact results. Effective optimization demands persistence, and scrutiny of short-term successes. 

There isn’t a single journey that can be ideal for all segments of customers coming to the ecommerce site. Testing multiple journeys helps the user gain key insights into customer behavior and identify the best journey for that section of the audience. The ideal approach to A/B testing is to continuously experiment with a combination of experiences and present the most relevant content across different audience clusters with a view toward maximizing a business goal. 

As a part of’s personalization engine, retailers can run A/B tests with a frequentist approach allowing them complete control in executing/monitoring and concluding A/B tests to identify winning buyer journeys.

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