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The Complete A/B Testing Kit

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Elevate your marketing tactics with our easy-to-use A/B testing kit! Gain insights, track performance, and make informed decisions with our Intro to A/B Testing guide, Significance Calculator and Tracking Template. 

Everything You Need to Start Running Effective A/B Tests

Running A/B tests in your marketing initiatives is a great way to learn how to drive more traffic to your website and generate more leads from the visits you’re getting. Just a few small tweaks to a landing page, email, or call-to-action can significantly affect the number of leads your company attracts.

Too often, though, marketers and web developers don’t understand the theory and process behind effective A/B testing. It may sound easy,  but A/B testing is all about optimizing your variables, finding significant results, and tracking those results to improve your conversion rates over time. HubSpot has brought together some of our most effective resources and created a complete A/B testing kit for marketers. Inside, you’ll find an introductory guide to A/B testing, an easy-to-use significance calculator, and a template to help you track your results over time.

Frequenty Asked Questions (FAQs)

  • If you need to A/B test your landing page (or any other part of your site), the first thing is that you need to know what on-page elements matter the most and get a good understanding of what user behavior tell us. The next step would be define which version will go as “A”, or Control. It’s important at this point too to create for variations before running the Marketing campaign with bad traffic allocation! When launching a Multivariate Experiment in A

  • Test results fall into the "you can't argue with facts" category. Assigning one person in charge of testing means more collaboration, clarity and its easier to track everything and decide what's successful or a wasted effort. By spending a few seconds to log what has been tested, everyone stays on the same page with no misunderstandings.

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  • Running an AB test is a lot of work. The process can be time-consuming and laborious, especially if you don't have the right resources in place to do it well. You also need traffic and an informed hypothesis before you can even start one.

Run better A/B tests

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