9 product experimentation tools to run smarterA/B tests

Product experimentation tools help you test changes to your product, measure their impact, and learn what drives user behavior. They fall into two categories: A/B testing platforms that run experiments, and behavioral analytics tools that reveal why those experiments succeed or fail.

The challenge? A/B testing tools show you which variation wins, but not why. To design better experiments and make confident product decisions, you need to combine quantitative test data with qualitative insights from behavioral tools.

This article covers nine tools across both categories to help you build a complete experimentation stack and a healthy optimization ecosystem.

Key takeaways

• Product experimentation tools fall into two categories: A/B testing platforms (to run tests) and behavioral analytics tools (to understand why tests win or lose)

• The most effective experimentation programs combine quantitative test data with qualitative insights from heatmaps, session replays, and surveys

• When choosing tools, prioritize integration capabilities, statistical rigor, and ease of use for your team's technical level

• Leading A/B testing tools like Optimizely, VWO, and AB Tasty integrate directly with Contentsquare for deeper experiment analysis

Product experimentation tools comparison

5 A/B testing and experimentation platforms

Here are five A/B testing tools that let you create, measure, and report on experiments through a centralized dashboard.

1. Optimizely

Optimizely is a digital experience platform with multiple tools, including web, server-side, email, and B2B commerce experimentation. With Optimizely's popular Web Experimentation tool, you can run A/B and multivariate tests on landing pages and personalization campaigns to improve conversion rates, retention, and the user experience.

Key features:

• Web and server-side experimentation

• Multivariate testing

• Personalization campaigns

• Feature flags and rollouts

• Statistical significance calculations

Best for: enterprise teams running complex, multi-channel experiments.

Pricing: enterprise pricing (contact for quote)

Pro tip: Contentsquare integrates with Optimizely. That means if you're running an A/B test in Optimizely, you can use Contentsquare to dig into why winning variations are such a hit with users (or why losing variations are missing the mark). You can:

• Record session replays to see how users behave on each page variation

• Launch surveys on the pages you're A/B testing, asking users' opinions directly

• Collect heatmaps that show where users click, scroll and hover on your test pages

• Use Journey Analysis—a sunburst-shaped visualization of your customer journey data—to perform deep funnel analysis and view how the changes you're A/B testing affect your key user flows

Use Contentsquare's AI, Sense, to ask key questions of your customer journeys.

These tools can add invaluable context to your experiment results, allowing you to iterate on unsuccessful experiments, and identify ways to build on your successful ones.

Contentsquare's Optimizely integration is bi-directional: real-time data flows both to and from each platform

2. VWO

Visual Website Optimizer (VWO) is a conversion optimization platform for creating A/B, multivariate, and split tests. VWO is primarily intended for marketing and conversion rate optimization (CRO) teams to create test variations without coding using a visual editor.

Key features:

• Visual editor for no-code test creation

• A/B, multivariate, and split testing

• Server-side testing (VWO FullStack)

• Feature flags and progressive rollouts

• Behavioral targeting and segmentation

Best for: marketing and CRO teams who want to run tests without developer involvement.

Pricing: starts at $314/month for Growth plan; enterprise pricing available.

They also offer an enterprise-grade tool, VWO FullStack, for product teams at large companies to run server-side A/B tests without impacting performance. VWO FullStack also has a feature rollout tool, allowing you to release new features to small groups and measure user impact before launching it to all customers.

3. AB Tasty

AB Tasty is another leading platform for running A/B, split, and multivariate tests across all digital surfaces. It offers a what-you-see-is-what-you-get (WYSIWYG) editor for trialing product changes without involving developers.

Key features:

• Visual editor for test creation

• A/B, split, and multivariate testing

• Feature flags and progressive rollouts

• AI-powered emotion targeting

• Server-side experimentation

Best for: teams wanting a balance of ease-of-use and advanced capabilities like emotion-based segmentation.

Pricing: custom pricing based on traffic volume.

Like VWO, AB Tasty offers a feature rollout tool, which helps you de-risk launches by showing them to small groups before they go live to your entire customer base. This helps you catch any important bugs early.

AB Tasty also offers an AI tool to automate the process of segmenting users by the emotions they're currently experiencing on your site, so you could, for example, run an A/B test only on satisfied users.

How ASICS uses Contentsquare for A/B testing

AB Tasty and Contentsquare are key to the experimentation program at the footwear brand, ASICS. They chose Contentsquare as their tool to get customer insights and complement hard test data, partly because the setup was so simple. Here's how to connect AB Tasty to Contentsquare.

4. Dynamic Yield

Dynamic Yield is a tool for personalizing your users' experiences of your site with a very granular list of characteristics, triggers, and behaviors. These advanced personalization tools make Dynamic Yield the A/B testing tool of choice for many enterprise customers.

Key features:

• Advanced personalization engine

• A/B and multivariate testing

• Predictive targeting

• Audience segmentation

• Omnichannel experimentation

Best for: enterprise ecommerce teams focused on personalization at scale.

Pricing: enterprise pricing (contact for quote)

If your A/B test disproves your hypothesis, Dynamic Yield might help you find a silver lining using its predictive targeting feature. This alerts you if a particular audience segment seemed to respond better to one of your losing variations than the overall winner. You could then serve the winning variation to most of your traffic and the losing one to the group that preferred it.

5. Omniconvert

Product and ecommerce teams use Omniconvert to create A/B tests, personalization experiments, pop-ups and overlays, and trigger on-site surveys.

Key features:

• A/B testing with advanced segmentation

• 40+ targeting parameters

• On-site surveys

• Pop-ups and overlays

• Ecommerce-specific features

Best for: ecommerce teams who need granular audience segmentation.

Pricing: starts at $273/month

Omniconvert's main A/B testing tool is called 'Explore' and offers advanced segmentation, often pulling from your CRM like HubSpot, to create experiments for specific user cohorts. You can segment by over 40 parameters, including geolocation, on-site behavior, and traffic source, to run experiments that only target a specific audience.

If you want to go beyond knowing which variation won, connect Omniconvert to Contentsquare. The integration lets you collect qualitative behavioral data alongside your A/B test results—giving you the experience intelligence to understand what actually drove the outcome, and what to optimize next.

4 behavioral analytics tools to understand why experiments succeed

A/B testing starts with solid hypotheses founded on good insight. These four Contentsquare capabilities help you collect foundational insights so you can understand why winning tests succeed, double down on what's working, and create a virtuous cycle of data-backed optimization.

1. Heatmaps

Heatmaps give teams an at-a-glance overview of what users are doing in a product, making them a great starting point for developing testing hypotheses.

Once you've run an experiment, heatmaps give you a deeper insight into why one variation outperformed another. Monitor heatmaps for both test variations and compare results to get a clear, visual overview of how click and scroll activity differed. They're also great to show team members and stakeholders—it's hard to argue with a heatmap!

Heatmaps in action: when the team at fashion retailer New Look decided to A/B test user-generated content (UGC) on their product pages, they used Contentsquare's Heatmaps to dig into their results. Heatmaps displayed the improved click rate of pages containing UGC in a visual way the whole team could understand.

Contentsquare's Heatmaps let you see how users click, move, scroll, and engage with

2. Session replay

Session replays are video-style renderings of the real actions an individual visitor takes as they use your product or website, from entry to exit. Since they measure all visitor actions, replays are great for finding where people hesitate or get stuck, giving you ideas for fixes and improvements to test.

Replays also help you understand why some experiments don't result in a winner, and give you qualitative insight from low-traffic tests that may not have a statistically significant sample size.

Contentsquare lets you filter session replays from any A/B test variant to see what users did throughout their session, and how their collective behavior affected your test results.

Replays in action: the team at home appliances company Electrolux used Contentsquare's Session Replay capability to watch back recordings when they noticed a recurring issue: users were struggling to find the products they were looking for with the website's search bar.

From observing user sessions, Electrolux learned their site's search functionality was unintuitive and what a more intuitive solution might look like, based on how people tried to use the feature. This insight inspired a successful A/B test.

These changes increased purchase path conversion rates by +28.6% while lead conversion rates grew by 70% from Q1 to Q4 of the same year.

Contentsquare's Session Replay lets you see how customers move through your site.

3. Surveys

Surveys are a quick and easy way for teams to collect feedback directly from users. Knowing what real users actually think about and need from your product helps you build experiments around user experience and reveals issues and opportunities that might come as a surprise to your team.

If you're using Contentsquare's AI-powered Surveys capability, you can set up events targeting to trigger surveys within A/B test variations and collect valuable qualitative data during experiments.

Using A/B test surveys alongside quantitative data gives you a fuller picture of not just what users did (Ex: where they clicked), but what they thought while doing it (Ex: do they understand what a new feature does? Do they like the new color scheme? Is there something they're missing?).

Surveys in action: package holiday provider easyJet Holidays often decides which A/B tests to launch based on Contentsquare insights. Recently, they noticed mobile users weren't using a particular feature: a tool to add your favorite holidays to a list.

They were unsure whether this was due to a UX problem or if users simply weren't interested in this functionality. So, easyJet launched a mobile survey and found users did like the feature so they decided to invest resources into testing and developing it.

Contentsquare's Surveys helps brands make targeted optimizations based on real customer feedback.

4. Journey analysis

Customer journey maps help you understand the steps your users take, revealing which pages make or break a user's decision to convert or act on their goals and therefore, which pages are ripe for A/B testing.

Contentsquare's Journey Analysis capability lets you turn your customer journey data into a visualization the whole team can understand. If there's a page where your users typically drop out of processes, Journey Analysis will show where so you can run A/B tests on it.

For example, perhaps you know from testing that adding shipping information to your product page increases conversions in your mobile app—but you didn't know exactly why until you spotted, from Journey Analysis, that users were no longer exiting your conversion flows to visit the help center and look for shipping information.

Journey Analysis in action: retail bank NatWest used Journey Analysis to examine user behavior on the sign-up journey for their youth savings account. They spotted a high drop-off rate on this account's product page.

The team A/B tested a new design that removed the hero image and added more key benefits and information instead. It resonated more with users, and drop-offs decreased.

See where users drop off in their customer journey with Contentsquare's Journey Analysis.

How to choose the right product experimentation tool

With so many options available, selecting the right experimentation platform depends on your team's specific needs. Here are the key criteria to evaluate:

• Integration capabilities: does the tool connect with your existing analytics stack? The most effective experimentation programs combine A/B testing platforms with behavioral analytics and product analytics tools like Mixpanel. Look for tools that integrate with each other, for example, Optimizely, VWO, and AB Tasty all integrate with Contentsquare, letting you see not just which variant won, but why.

• Statistical rigor: how does the tool calculate statistical significance? Enterprise tools like Optimizely and Dynamic Yield offer advanced statistical methods, while simpler tools may require larger sample sizes to reach confident conclusions.

• Team technical level: does your team have developers available, or do you need no-code solutions? Tools like VWO and AB Tasty offer visual editors for marketers, while server-side options like VWO FullStack require engineering resources.

• Experimentation maturity: are you just starting out or running dozens of tests monthly? Teams new to experimentation may want simpler tools with guided workflows, while mature programs need advanced features like granular segmentation and feature flag capabilities.

• Budget and traffic volume: most tools price based on traffic or monthly tracked users. Calculate your expected volume and compare pricing tiers—some tools offer free tiers for startups and low-traffic sites.

• Qualitative insight needs: A/B tests tell you what happened, but not why. If understanding user motivation matters (and it should), prioritize tools that integrate with session replay, heatmaps, and survey capabilities.

FAQs about product experimentation tools