A/B testing is a powerful tool that digital marketers, product managers, and web developers use to optimize websites, apps, and campaigns. By comparing two or more variations of a webpage or an element on a page, one can glean insights into what works best for the target audience.
However, the most pressing question is, “How much traffic do you actually need for a successful split test?”
It’s all about statistical significance -it indicates how sure you can be about the results of your test. Without a sufficient number of conversions, any changes in conversion rates between your variations could be due to random chance rather than the changes you made.
Here’s a breakdown of why this range is critical:
Precision and Confidence: With a minimum of 50 conversions, you start to gain some confidence in your results. It’s an initial threshold that ensures random fluctuations don’t overly influence the outcome. As you approach 100 conversions, this confidence becomes even stronger, providing a more reliable basis for decision-making.
Reduced Margin of Error: The more data you have, the smaller the margin of error. By targeting between 50 and 100 conversions per variation, you significantly reduce the chances of making a decision based on flawed or insufficient data.
Now, knowing that we need between 50 to 100 conversions per variation, how do we calculate the traffic required?
Here’s a simplified formula:
Required Traffic = (Number of Conversions Needed) / (Baseline Conversion Rate)
For example, if your current (baseline) conversion rate is 2%, and you aim for 100 conversions for a particular variation:
Required Traffic = 100 / 0.02 = 5,000
In this case, you’d need 5,000 visitors for that specific variation.
Time plays an essential role in the efficacy of your A/B test. While it’s tempting to run a test for an extended period, aiming to conclude your test within a few months is ideal due to the following reasons:
Seasonality: Business cycles, holidays, and other seasonal factors can influence user behavior. By limiting your test to a shorter timeframe, you can control for these external variations more effectively.
Changing User Behavior: Digital landscapes evolve rapidly. User preferences and behaviors might change over prolonged periods, making older data less relevant.
Operational Efficiency: If you find a winning variation early, implementing changes sooner can lead to better results for your business.
If your website or specific webpage doesn’t generate 50-100 conversions per variation within a reasonable timeframe, don’t fret! You can still obtain valuable insights from A/B testing by adjusting your approach. Here are some strategies to consider:
Redefine Your Goal: Rather than focusing on ‘hard’ conversions, like completed purchases or form submissions, consider looking at ‘soft’ conversions or micro-conversions. These are smaller actions that users might take before the primary conversion, such as:
Clicking on a specific link or button
Viewing a video
Signing up for a newsletter
Optimize for Engagement Metrics: Shift your testing goal towards engagement metrics. For example:
Time Active: Instead of looking at outright conversions, see how long users spend on your page or how much they interact with your content. A significant increase in active time can hint at higher user interest and engagement.
Page Scroll Depth: This metric measures how far a user scrolls down a page. If users scroll further on one variation than another, it may indicate that they find that version more captivating or relevant.
AB Split Test can help you implement both of these test types.
Increase Traffic: While this might not be an immediate solution for everyone, consider methods to increase your traffic temporarily. This could be achieved through paid advertising, SEO improvements, or even leveraging social media.
Run Longer Tests: If you’re on the verge of obtaining enough conversions, consider extending your test period a little longer. However, be cautious of extending it too much due to reasons like seasonality, as mentioned earlier.
Refine Your Audience Segmentation: Instead of testing on your entire audience, consider narrowing down to specific, more engaged segments. For instance, target returning visitors or users who’ve interacted with your content before. These segments might be more responsive, leading to faster conversion results.
A lack of conversions doesn’t mean you can’t benefit from A/B testing. By adjusting your goals, focusing on engagement metrics, and utilizing other strategies, you can still glean valuable insights to enhance your user experience and conversion rates. Remember, it’s all about adapting your approach to the data you have and the goals you’re aiming to achieve.
