{"id":16626,"date":"2024-09-16T18:50:52","date_gmt":"2024-09-16T18:50:52","guid":{"rendered":"https:\/\/userpilot.com\/blog\/alternatives-to-ab-testing\/"},"modified":"2026-04-01T12:44:40","modified_gmt":"2026-04-01T12:44:40","slug":"alternatives-to-ab-testing","status":"publish","type":"post","link":"https:\/\/userpilot.com\/blog\/alternatives-to-ab-testing\/","title":{"rendered":"Alternatives to A\/B Testing in SaaS: How to Improve Product and User Experience"},"content":{"rendered":"

A\/B tests are a well-known technique for testing UX elements but is it the only one? Are there any alternatives to A\/B testing for product managers<\/a>?<\/p>\n

Plenty actually, and that\u2019s what we explore in the article!<\/p>\n

What is A\/B testing?<\/h2>\n

A\/B tests<\/a> involve comparing two items or variations to one another to see which one drives better results.<\/p>\n

To carry out the test, you enable the new feature or UI element for half of your users. Next, you collect data on its effectiveness compared to the previous one.<\/p>\n

Split testing vs A\/B testing<\/h2>\n

A\/B testing and split testing are pretty much the same things. In both cases, you show two versions of your product to different groups of users to check which of them gets a better response.<\/p>\n

A\/B tests are normally used to test small variables, like different UI color patterns. A split test can be used to test two very different products, like two different websites.<\/p>\n

However, they both rely on the same principle.<\/p>\n

If there’s any difference, its emphasis is within the name. A\/B stresses the comparison side of the test, ‘split’ focuses on the way traffic is diverted to different products.<\/p>\n

What’s more, split tests aren’t necessarily limited to two versions.<\/p>\n

Multivariate testing vs A\/B testing<\/h2>\n

Multivariate testing is split testing of multiple different versions with multiple different elements.<\/p>\n

In A\/B tests, we make only one change and enable it to the treatment group, while the control group has access to the old one.<\/p>\n

It’s easier to single out the elements that really move the needle in A\/B tests. However, this means potentially running multiple tests, which takes time. Multivariate tests can render quicker results.<\/p>\n

Main use cases for A\/B testing in SaaS<\/h2>\n

A\/B tests are one of the best-known kinds of experiments. Let’s look at situations when they are most suitable.<\/p>\n

Improve conversion rates and attract more users<\/h3>\n

A\/B testing is commonly used to optimize the conversion rates<\/a> of landing pages.<\/p>\n

In SaaS, teams often use A\/B testing to improve the micro and macro conversion<\/a> rates across the user journey<\/a>.<\/p>\n

For example, you could use it to find the best position for the demo button to increase the number of bookings.<\/p>\n

However, in SaaS, the application of A\/B testing goes well beyond the optimization of your website.<\/p>\n

Drive in-app engagement and improve usability<\/h3>\n

A\/B tests are a great tool to improve product usability<\/a> and drive in-app engagement.<\/a><\/p>\n

Just like with websites, you can experiment with changes to different aspects of UI to find the best configuration.<\/p>\n

So apart from button placement, you could use it to identify font styles and sizes, color patterns, or UI copy that provide the best user experience<\/a>.<\/p>\n

Test messaging impact on user behavior<\/h3>\n

A\/B testing is great for measuring the effectiveness of in-app messaging<\/a> for driving user engagement and feature adoption<\/a>.<\/p>\n

For example, you could run a series of tests to see if a tooltip <\/a>results in higher activation<\/a>.<\/a><\/p>\n

\"A\/B<\/picture>
A\/B testing<\/figcaption><\/figure>\n

Message testing is not limited to in-app contexts. For example, you can use them to test ads in your marketing campaign to see which versions will drive more conversions.<\/p>\n

When should you NOT use A\/B testing?<\/h2>\n

Although it\u2019s a useful tool, A\/B testing has limitations that restrict its application.<\/p>\n

Not enough statistical significance due to a lack of data<\/h3>\n

For A\/B tests to work, your website or product needs to have enough users.<\/p>\n

If the sample size is too small, any findings won’t be reliable enough to make informed product decisions.<\/p>\n

In fact, relying on such results is probably worse than not doing any testing because it gives you a false sense of certainty.<\/p>\n

When the cost is not worth it for the desired outcome<\/h3>\n

Not every decision we make needs to be based on data or test results.<\/p>\n

Some things are obvious. You can be pretty sure that a website with no CTA button is going to perform worse than one which has it.<\/p>\n

A\/B testing may sound uncomplicated, and you don’t need to be a data scientist<\/a> to use them effectively. However, they can take a lot of resources and time to set up.<\/p>\n

For starters, some of the A\/B testing tools are pricey. What’s more, the engineers need to build the alternative version, you need to release it, wait for the app store to approve it, and then for users to download it. All of that before you even start collecting data.<\/p>\n

That’s why you need to run tests only when they can have a big impact on generating user behavior insights. Otherwise, look for more cost-effective alternatives.<\/p>\n

When data analysis can give you enough relevant insights<\/h3>\n

Sometimes user segmentation<\/a> combined with customer behavior analytics<\/a> is enough to spot trends and understand what’s working.<\/p>\n

For example, you can see that your power users<\/a> use features in a certain sequence. That’s how they get the best results. You just need to make the other user segments with the same JTBDs emulate their behavior and see what works for them.<\/p>\n

If yes, that’s sorted. If not, then of course you can try other ways of testing your hypothesis.<\/p>\n

Alternatives to A\/B testing<\/h2>\n

So what other tools do product managers<\/a> and marketers have at their disposal to validate their assumptions and ideas<\/a>?<\/p>\n

User behavior tracking<\/h3>\n

As mentioned, tracking user behavior<\/a> can give insights that are sufficient to improve your product<\/a> and the UX.<\/p>\n

You can easily track how users interact with the product and make others follow the same path. Once they do it, you collect more data to verify if it works for this user cohort as well.<\/p>\n

\"User<\/picture>
User behavior tracking<\/a> is an A\/B test alternative.<\/figcaption><\/figure>\n

Session recordings<\/h3>\n

In session recordings, you can see literally every single cursor move. Again, you can use it with user segmentation<\/a> to see how the most and least successful users engage with the product.<\/p>\n

\"Session<\/picture>
Session recordings as an alternative to A\/B testing.<\/figcaption><\/figure>\n

Usability testing<\/h3>\n

Usability testing <\/a>is another A\/B testing alternative used for UX research.<\/p>\n

Techniques like guerilla testing, screen sharing, eye-tracking, 5-second tests, or first-click tests can reveal a lot about easy to navigate the product is.<\/p>\n

Also, session recording can be used for usability testing as long as it has enough focus, like a specific task for users to complete.<\/p>\n

User feedback surveys<\/h3>\n

User surveys <\/a>are an easy way to collect user feedback <\/a>on new functionality or UI features.<\/p>\n

You can trigger them contextually as soon as the user engages with the feature and ask them how they like it.<\/p>\n

You can then segment users according to their answers and then cross-reference the results with product usage data<\/a> to look for patterns.<\/p>\n

\"In-app<\/picture>
In-app surveys are an A\/B testing alternative.<\/figcaption><\/figure>\n

To get an even deeper understanding, you can include qualitative questions<\/a> in your surveys.<\/p>\n

User interviews<\/h3>\n

User interviews are an even better way to collect qualitative data about product usability and user experience<\/a>. That\u2019s because you can adjust your questions to the user and the context as you proceed.<\/p>\n

Having said that, each set of interviews needs to have a clear purpose and so requires preparation.<\/p>\n

\"User<\/picture>
User Interviews are another alternative to A\/B testing.<\/figcaption><\/figure>\n

Beta testing<\/h3>\n

Beta testing<\/a> allows you to collect feedback from real users before you release the product to the wider public.<\/p>\n

By giving access to the product to a sample of target users, we can reliably test its usability and identify any issues that need to be rectified before the launch<\/a>.<\/p>\n

This means that when the product or feature is finally out, there’s a good chance it will drive engagement and conversions.<\/p>\n

\"Use<\/picture>
Use beta testing instead of A\/B testing.<\/figcaption><\/figure>\n

Feature flagging<\/h3>\n

Feature flagging involves showing and hiding features.<\/p>\n

As you make the changes, you watch the impact they have on different users and how they engage with the product.<\/p>\n

For example, removing a shortcut button on the screen can increase friction and increase the time to value<\/a>.<\/p>\n

However, removing the majority of buttons or taps can actually remove friction and speed up activation<\/a> because users won’t get distracted by irrelevant features.<\/p>\n","protected":false},"excerpt":{"rendered":"

A\/B testing is a powerful tool for optimizing UI and UX, but it has its limitations. When you don’t have enough users or the potential improvements will bring little value, they may prove too time-consuming and costly. A\/B testing alternatives like user behavior tracking or feature flagging can also help you assess the impact of the changes you’re planning to implement more quickly and without the expense associated with buying dedicated tools. If you want to see how Userpilot can help you test your ideas and collect user data, book the demo!<\/p>\n","protected":false},"author":51,"featured_media":16628,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[214],"tags":[254,979,60,942,850,253,216,185,215,232,572,838,707,936,429],"class_list":["post-16626","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-product-management","tag-a-b-testing","tag-ab-testing","tag-conversion-rate","tag-feedback-collection","tag-happy-path","tag-product-experiments","tag-product-management","tag-product-management-tools","tag-product-manager","tag-product-managers","tag-product-usage","tag-saas-product-managers","tag-saas-user-feedback","tag-usability-testing","tag-user-behavior"],"acf":[],"yoast_head":"\nAlternatives to A\/B Testing in SaaS: How to Improve Product and UX<\/title>\n<meta name=\"description\" content=\"A\/B tests are a well-known technique for testing UX elements. 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