如何确定您的A/B测试结果是否显着

Sometimes, when looking at a test you’ve conducted on your website or in an email, it can be difficult to tell if the difference in performance actually meant something. But have no fear! By the end of this post, you should have superior knowledge of how to understand yourmarketing A/B test结果以及它们是否重要。

We won’t dive into a full lesson on statistics and details today; there are full college courses completely devoted to imparting those detailed lessons onto marketers. However, what marketers应该know today is that the difference itself is important, so you can quickly understand whether your test results were just "different," or even better, significantly different.

Step 1: Create Your Campaign

When you're creating your campaign, think about which variable you want to test. For example, do you want to try out different text phrases for the call-to-action in your email, different colors, or an entirely different layout? For instance, in the test we're using as an example for the purposes of this article, we're looking at using a regular orange button for aHubSpot免费试用offer versus using some custom art for the free trial button to see if the custom art really makes a difference. Just about anything can have an impact on how many people take the suggested action, so pick one item to vary in your message before beginning your campaign.

一次不要一次选择一个变量。否则，将不可能分辨出哪些变量实际上导致行为变化。坚持更改颜色或布局或文本 - 但并非一次。确保您的软件提供了一种测试成功bob电竞官方下载的方法。例如，提供的软件bob电竞官方下载integrated analytics for A/B testing这可以报告数据。

Step 2: Check Your Data

Once you’ve wrapped up the campaign or want to check your results (maybe 30 days after you launched the landing page or a week after you sent the email), check your data. Record how many people viewed each version of what you did as well as how many reacted. Using our example, if each call-to-action was viewed 200 times, with the top one generating 20 clicks and the bottom one generating 6 clicks, save that information.

步骤3：确定结果的统计显着性

有许多在线计算器可帮助您确定测试结果的差异是否显着。以以下方式作为考虑测试意义的指南：

如果测试之间的差异很小，则可能是您测试的变量不会影响看待它的人。

You can also usePRCOnline's Statistical Significance toolto determine whether your test results are statistically significant. Plug in your numbers to find out if your test yielded significant results. The "sample size" should be the number of people who saw each version of what you did. The "response" should be whatever measured that difference. In our example, the number of clicks on our free trial offer. If the tool says “Significant,” you know you’re on the money. If not, don’t despair. It just means your test didn’t show a significant change in your conversion rate. Not every test will be a winner, and it is important to distinguish which changes are important and which ones are not.

Step 4: Act Accordingly

从本文中讨论的示例实验中，我们得出的结论是，我们的免费试用按钮的定制艺术在人们是否选择单击召唤行动方面产生了很大的影响。当我们使用橙色按钮时，通话行动的流行程度大大降低。因此，我们在整个网站中都实现了该自定义按钮。

One of the benefits ofA/B测试是很容易做到。您可以定期通过许多不同的营销渠道进行A/B测试，并逐案对结果产生极大的影响。您还可以从一项测试中吸取教训，并将其应用于将来的工作。例如，如果您进行了您的电子邮件营销中的A/B测试而且，您一再发现，在电子邮件主题行中使用数字会产生更好的点击率，您可能需要考虑在更多电子邮件中使用该策略。

Do you conduct A/B testing in your inbound marketing? How could you use these kinds of analytics to test your website ormarketing automationmessages?

照片来源：大卫·戈林（David Goehring）