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White Paper

Six A/B Testing Strategies for Low Traffic Websites

When it comes to testing, there are several potential bottlenecks to watch out for: perhaps your development or creative teams don’t have enough time for testing. Perhaps your testing team is struggling to come up with new test ideas. Or, perhaps your site simply doesn’t receive enough traffic to reach statistical significance.

There are two factors that contribute most directly to achieving a statistically significant result: sample size and effect size. 

Sample size is important because larger sample sizes tend to reduce the amount of variance in the observations. Less variance leads to more precision, which makes it easier to assess whether the result is consistently different from the status quo. Effect size is also important as it measures the magnitude of a phenomenon. It shows how different the current observation is from the norm or status quo.

This white paper offers several strategies for testing with limited traffic, but keep in mind that even these strategies are not enough in all cases. Check out the last few suggestions for help when traffic is really small.

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