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How To A/B Test at Scale

With (Statistical) Confidence

Technology   |   Shane Remer   |   May 20, 2022

A/B testing, in theory, is simple. You use it to compare two or more versions of a product or service to determine which one is more effective.

For consumer industries like retail or CPG, you could test marketing promotions to determine which one works best and improve the likelihood of success or deliver a better outcome.

The problems with A/B testing start once you introduce multiple stores, countries, and thousands to millions of data points. Most companies face the same bevy of challenges when they try to implement A/B testing at scale:

  • Multiple data sources
  • Manual, repetitive processes
  • Slow reporting times by external vendors
  • Internal skills gaps

These challenges are a recipe for headaches, delays, and mistakes.

But they’re also an opportunity for growth and results.

Behind each of these potential roadblocks is everything you need to use A/B testing to assess marketing promotions.

  • Multiple data sources give you plenty of data to use
  • Manual, repetitive processes provide you with an easy path to scale
  • Slow reporting times by external vendors offer you an opportunity to bring analysis in house
  • Internal skills gaps present a chance to train employees and advance their careers

How To Scale A/B Testing Efficiently and Effectively

1. Automate processes

Software can automate many tasks associated with A/B testing, including managing test groups, calculating results, and implementing changes.

Analytics automation platforms automate all steps of the A/B testing process, including preparing the data, analyzing it, and outputting it into results. This is especially useful for multivariate tests.

When you integrate analytics automation platforms with RPA and other solutions, you can schedule reports to pull data, run automatically, and trigger actions/decisions when specific conditions are met.  Because everything is automated, you can easily add more data sources, reproduce reports for different products and stores, and scale up as needed.

2. Leverage and train internally

The best people to run an A/B test in your company are the ones that know the data — even if they don’t necessarily know how to analyze it.
Automation platforms that use a drag-and-drop approach to analysis make it easier for anyone to jump in and help. This will save you time and money by reducing your dependency on outside vendors and sources. It will also add to your team’s skillset, increasing your analytic capabilities.

3. Implement changes incrementally

When you train internally and use automation, it’s easy to start with one test, ensure it works, and scale. Taking this approach will help you avoid disruptions. Instead of overhauling everything you have in place, you can implement new processes or technologies in stages, test each one, then scale up implementation once they’re vetted.

An Example of A/B Testing at Scale with Statistical Confidence

Of course, talk is cheap. And what really matters is delivering results from your efforts.
The good news is many companies have taken the approach above before. As you might have noticed from the video clips sprinkled throughout this article, 7-Eleven has taken a similar approach to this.

They reduced reporting times from 100 hours to 1 and increased efficiency by 60 percent.

Because they automated their approach, they also ensured statistical confidence in their A/B testing results regarding their marketing promotions. Cleaner data and vetted processes lead to less probability of chance sneaking into the outcomes. And they were able to do this for 70,000+ locations and 17 countries using millions of data points.


A/B testing is a great way to compare marketing promotions — provided you solve the problems that can get in the way of ensuring accurate results.

Developing the proper testing methods, gathering the right information, and using the right analytics tools are crucial to ensuring the best results.

We’ve created the Analytics Business Use Case Discovery Guide to help you discover the best place to start implementing analytics. Use it to identify the best place to start small and deliver immediate ROI.