What are A/B Testing Tools?

A/B testing tools measure how customers interact with a brand’s digital creative content. Through conducting tests on customer interaction, companies can extract real data and metrics to optimize products. A/B testing tools allow marketers, advertisers, and web developers to test different versions of digital content to find the customer-preferred digital experience and personalization for various personas. Website visitors subconsciously react to every element on a web page—such as the color and aggressiveness of a live chat pop-up, the phrasing of a call-to-action (CTA) button, or the positioning of the search bar. A/B testing solutions give web content creators the tools to deploy the tests, determine the target audience, and analyze the experiment results. These tools are used to improve conversion rate and bounce rates, as well as the overall success of a website. The main way that digital creativity is tested is through a rotating A/B test. This is done by using two iterations of a design and seeing which one gets the highest click-through rate. A company can conduct a test with version A for 500 users from the target audience and then another test on version B for 500 users with a completely different variant. The company can then see which one gets the highest click-through rate and, ultimately, determine which variant should be used. A/B testing can offer the following use cases to improve a company’s performance: Create the most successful version of the e-commerce site, especially the home page and calls-to-action (CTA) Increase website traffic and reduce bounce rates Gain insight into user behavior Analyze any and all test segments to discover key opportunities Increase conversion rates and e-commerce profits Tailor e-commerce content and goals through continuous testing Determine the consumer impact of differing designs and formats on website optimization What Types of A/B Testing Tools Exist? Proprietary A/B testing Proprietary solutions will require time and resources but will adapt to very specific case studies and customizations. Proprietary products also provide customer support and streamline the process of setting up, tracking, and analyzing tests. Open-source A/B testing Open-source solutions come with a very small price tag (if any at all). While open-source software doesn’t offer the same kinds of reports and finetuned features as proprietary solutions, it gives access to an entire community of programmers and developers with bountiful testing experience.

What are the Common Features of A/B Testing Tools?

A/B/n testing: This type of split testing takes A/B testing to the next level by analyzing multiple versions of a creative product, one variable at a time. This allows a company to determine the best variation across a multitude of tests that measure KPIs on several iterations. The analytics tools used for a simple A/B test will also measure the same KPIs for the multiple variations. Multivariate testing: Multivariate testing uses the same methods as A/B testing, but instead of testing only two variables, it tests a higher number. This is essentially like having two tests combined into one. This is a way to conduct test combinations. For example, one multivariate test can test whether the combination of a blue header and white text works better than a red header with grey text. Then, it can test what happens when the combination is flipped—whether a blue header would work better with grey text or a red header would work better with white text. A successfully performed multivariate test would then show which combination had the highest click-through rate. The major benefit of multivariate testing is saving time and ensuring that the best combination with the highest conversion rate is being presented on the website. Multipage funnel testing: Multipage funnel testing is a way to test variations of several consecutive website pages. This is a great way to see if customers are finding what they’re looking for in the quickest way possible. The use case of multipage funnel testing can be easily applicable to retail, e-commerce, and any other website where the company is selling a product. This can test how quickly a user went through the buyer funnel from initial interest all the way to the final goal of purchasing the product. A multipage funnel test can help test the most effective way to convert customer interest into a customer purchase. Audience targeting: Audience targeting provides the ability to choose where the test will run and for whom. A/B test can be conducted to see which visitor conditions to show the experiment to and which specific URLs the experiment should run on the site. To increase engagement from mobile users who are using Google Chrome, audience targeting can conduct the A/B test on these specific users. This helps increase engagement with a particular audience. Funnel analysis: Funnel analysis allows companies to analyze the data in the buyer's journey and make necessary changes to facilitate conversion rate optimization. A/B test with funnel analysis helps understand if things such as registration pages or website subscription options are driving more people to convert or if they’re turning customers away. This allows the tester to see if they need to make adjustments to certain stages to increase engagement. Heat maps: Heat maps are a very effective tool for visualizing which links users click on when they visit a website. Heat maps applied to an A/B test will show certain links on a webpage in either blue, yellow, or red to indicate how often those links are clicked. This way, testers can analyze how to effectively set up a page and ensure that the most important links are in spots that are being clicked on the most. Surveys: A valuable feature in A/B testing software is surveys. This enables companies to directly ask users which type of variant they prefer through a variety of different questions. The data from the survey can then be translated into graphs or charts, which makes it easier for the tester to visualize the results. This can help companies target which specific variants are working well and which variants are failing. It also may give companies more detail on where to head in order to improve their designs by asking questions that make users explain in detail rather than asking simple yes or no questions. Statistical relevance analysis: Statistical relevance analysis helps confirm if an A/B test has a large enough sample size. Users can track their data in real time and see if the test needs more time or if there’s enough traffic to confirm that one variant performed better than another. This helps users stop the test and not use more time on it than needed. Achieving the right sample size ensures the statistical significance of test results is achieved during the testing process. Test scheduling: A/B testing platforms allow users to schedule tests in advance to ensure that they are conducted during a time when the website is expected to get a lot of traffic.

What are the Benefits of A/B Testing Tools?

A/B testing software is an essential tool for optimization and growth. The best way to improve a product's performance is by continuously conducting new trials to see what works and what doesn't. The following are reasons why vendors benefit from using these tools. Higher conversion rates: Companies can conduct a test to see what will produce the best conversion rate. Rotating A/B tests can give content creators an accurate representation of how quickly users can find what they’re looking for on a website. This can lead to customers purchasing more quickly or subscribing to newsletters more frequently, leading to greater conversions and higher revenue for the company. Real-time testing: A/B testing can save companies plenty of time by testing variations in real time. Instead of having to pull people aside to conduct trials, rotating tests are conducted on users who are currently visiting the website. Genuine testing: Another major benefit of A/B testing is genuine testing. Tests are conducted on actual visitors who are coming to the website, which means that the results are not skewed by incentives or preconceived knowledge within a trial. Since the testing is done on completely random visitors, the company will get the most accurate picture of how customers are behaving in real time. Reduced bounce rate: One of the main goals of A/B testing tools is to test ways to keep people on a website for as long as possible. This can be done by incorporating different page layouts, links that lead back to the website, and CTA buttons. High bounce rates are the main reason for low conversion rates, so testing ways to reduce the bounce rate of a landing page is a critical way to keep customers engaged.

Page 4 | Best A/B Testing Tools: User Reviews from October 2026

Monetate

Monetate helps organizations make digital experiences personal and profitable. We empower merchandising, marketing, and technical teams by delivering category-defining customer experiences that drive over $230 billion in annual revenue. More of the top 1,000 retailers than any other vendor use Monetate’s AI-powered search, merchandising, testing, and personalization capabilities to increase customer engagement, boost conversion rates, and maximize revenue through interactions that matter to customers. Global companies deliver customer-centric content, product recommendations, and experiences powered by Monetate’s unique blend of machine learning and natural language processing that analyzes real-time and historical data, intent, and meaning.

Average Rating: 4.0/5.0

Total Reviews: 128

How Do G2 Users Rate Monetate?

Has the product been a good partner in doing business?: 8.8/10 (Category avg: 9.0/10)
Reporting and Analytics: 7.0/10 (Category avg: 8.5/10)
Multivariate testing capacities: 7.9/10 (Category avg: 8.6/10)
AI/Machine Learning: 6.7/10 (Category avg: 7.5/10)

Who Is the Company Behind Monetate?

Seller: Monetate
Company Website:
Year Founded: 2008
HQ Location: New York
Twitter: @monetate
5,819 Twitter followers
LinkedIn® Page: www.linkedin.com
114 employees on LinkedIn®

Who Uses This Product?

Top Industries: Retail, Apparel & Fashion
Company Size: 41% Large, 38% Medium

What Do G2 Reviewers Say About Monetate?

AI-generated summary from verified user reviews

Pros

Users highlight the ease of use in Monetate, appreciating its straightforward implementation and active support.
Users value the flexibility and ease of use offered by Monetate for creating effective A/B testing experiences.
Users value the helpful support team and the ease of use that Monetate offers for all levels.
Users value the automation capabilities of Monetate, allowing seamless testing and scheduling of campaigns effortlessly.
Users praise Monetate for its effective content personalization, enabling tailored experiences for diverse consumer audiences.

Cons

Users are frustrated by Monetate's missing fundamental features that hinder effective A/B testing and manual integration.
Users find the feature deficiency in Monetate limits usability and complicates data handling and diagnostic experiences.
Users find the poor interface design of Monetate outdated and confusing, especially for new or less technical users.
Users find data inaccuracy problematic, hindering their ability to interpret analytics and optimize their performance effectively.
Users find the dated and confusing UI of Monetate hinders usability and complicates their overall experience.

What Are Recent G2 Reviews of Monetate?

"cost and ease of use, as well as having a csm that is active"

Rating: 4.0/5.0 stars

— ronald w.

Best A/B Testing Tools - Page 4

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