#3-#4 - Deepstash

#3-#4

Look Beyond Your Core Metrics

You might have hidden data in your losing test results.

E.g. Look at your “losing” ad to see if it drove less traffic but had higher conversions. If you’d only been looking at traffic & revenue, you might not have noticed this ad works better statistically, if not in rough numbers.

Dig into that data to find out why it drove less traffic & use that to improve your next set.

Remove Junk Data

Test results can be skewed by junk data. Remove that to see trends more clearly & drill down to find crucial trends. Remove:

  • bot traffic.
  • your traffic.
  • competitor traffic.

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MORE IDEAS FROM How to Get Useful Data From Losing and Inconclusive A/B Tests

Try Something Really Different

Inconclusive test results could mean your variations are too close. A/B testing can help you see if a small change impacts conversions, but sometimes those tiny tweaks don’t have much impact at all.

Analyze Different Traffic Segments

  • new versus returning customers
  • buyers versus prospects
  • specific pages visited
  • devices used
  • demographic variations
  • locations or languages

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Look for Biases & Get Rid of Them

Look for factors that could have impacted your test.

  • Did you run a promotion?
  • Was it during a traditionally busy/slow season in your industry?
  • Did a competitor’s launch impact your tests?

Try to separate your results from those impacts. If you can’t, try rerunning the test.

Look at how your test was run. Did you randomize who saw which versions? Were both versions mobile-optimized?

Run Your A/B Tests Again

The goal is to continuously improve performance. Once you’ve completed a test & determined a winner (or determined there was none), test again.

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How Do You Know If You Have a Losing or Inconclusive A/B Test?

An inconclusive test might mean the numbers are less than a percent off, or neither variation got any traffic at all. When your tests don’t have enough data or if the numbers are too close, they are considered inconclusive or statistically insignificant.

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RELATED IDEA

A/B Testing: Definition & How it Works

A/B testing is used to find the best marketing strategies. It is be used to test everything from website copy to sales emails. This allows you to find the best-performing version of your campaign before spending your entire budget on one that don’t work. 

While A/B testing is time-consuming, its advantages are enough to offset the time investment. Proper A/B tests make a huge difference in the effectiveness of your campaign. Narrowing down & combining the most effective elements of a campaign creates a higher return on investment, lower risk of failure, & a stronger marketing plan.

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A/B Testing

Over the years, conversion rate optimization (CRO) seems to have become synonymous with A/B testing in the minds of many marketers.

A/B testing is a form of conversion rate optimization. You have a page and you want it to perform better, so you change something and see if it improves your results.

But, A/B testing isn’t the only way to do CRO.

If you’ve got enough traffic, multivariate testing can allow you to produce meaningful results much more quickly.

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What Is Dynamic Keyword Insertion

Dynamic keyword insertion is a paid ad feature that uses machine learning or AI to customize online ads to match users’ search queries. 

KI works by using machine learning or AI to insert terms that match users’ search queries to deliver more relevant ads. This allows businesses to deliver highly relevant ads without spending hours creating different ads for each possible search query.

Dynamic ads can change nearly every aspect of your ads, including the main keyword, images, CTA button, and even the landing page users are sent to. 

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