Note: This is a retrospective guide to the A/B testing market in 2023. Google Optimize is not included because Google ended the service on September 30, 2023. If your old experimentation strategy depended on it, congratulations: you were suddenly shopping for a replacement.
A/B testing is where bold opinions go to meet real users. Your team may be convinced that a brighter button, a punchier headline, or a brand-new onboarding screen will “definitely” improve conversions. Then the data walks in, clears its throat, and occasionally says, “Actually, no.”
That is the magic of split testing. It replaces guesswork with controlled experiments, helping businesses compare a current version of a page, feature, email, or user flow against one or more alternatives. The goal is not to make every test a winner. The goal is to make better decisions before pouring more budget, development time, and coffee into an idea that users may not even like.
In 2023, the best A/B testing tools were no longer limited to changing button colors on landing pages. Businesses needed tools for website conversion rate optimization, mobile app experiments, server-side testing, feature flags, personalization, analytics, and safer product releases. This guide compares 14 standout platforms from that era and explains which teams they suited best.
What Makes a Great A/B Testing Tool?
The best A/B testing software does more than divide traffic between Version A and Version B. A strong platform should make it easier to build variations, target the right users, track meaningful metrics, calculate trustworthy results, and share what the team learned. Otherwise, you are just conducting an expensive digital séance and hoping the conversion rate speaks.
When evaluating A/B testing tools, focus on these essentials: visual editing for marketers, code-based experimentation for developers, split URL testing, multivariate testing, audience targeting, feature flags, integrations with analytics platforms, statistical reporting, quality assurance controls, privacy support, and experiment history. Your perfect tool depends on whether you are optimizing a product, an e-commerce store, a SaaS signup flow, or a paid-media landing page.
The Top 14 A/B Testing Tools for Growth in 2023
1. Optimizely
Best for: Enterprise experimentation programs.
Optimizely was one of the best-known names in A/B testing for good reason. It supported web experimentation, feature experimentation, multivariate testing, personalization, and product-led testing across multiple digital touchpoints. It was especially attractive to larger teams that wanted marketers, product managers, engineers, and analysts working from a shared experimentation program instead of conducting separate tests in separate corners of the building.
2. VWO
Best for: Growth teams that want testing and behavioral insights together.
VWO was a strong choice for companies that wanted to connect the “what happened?” question with the much more useful “why did that happen?” question. Its testing tools worked alongside heatmaps, session recordings, surveys, form analytics, and funnel analysis. That combination made it practical for conversion rate optimization teams that wanted to discover friction before launching a test rather than randomly redesigning the page every Tuesday.
3. Adobe Target
Best for: Large organizations already invested in Adobe Experience Cloud.
Adobe Target was designed for sophisticated experimentation and personalization. It made the most sense for enterprises with complex customer journeys, major traffic volumes, and an existing Adobe ecosystem. Teams could run A/B tests, multivariate tests, automated targeting activities, and personalized experiences while connecting results with broader analytics and customer data. It was powerful, but it was not exactly a “click around for five minutes and become a CRO wizard” tool.
4. AB Tasty
Best for: Marketing and product teams that need shared experimentation.
AB Tasty gave organizations a balanced mix of web experimentation, feature testing, personalization, audience targeting, and visual editing. It was especially useful when marketing wanted to improve a website while product teams wanted to test experiences inside an app. Rather than buying two platforms and scheduling recurring meetings called “Why Does Our Data Not Match?”, teams could centralize more of their experimentation work.
5. Dynamic Yield
Best for: E-commerce personalization and customer journey optimization.
Dynamic Yield stood out for businesses that wanted to combine A/B testing with personalization. Retailers and subscription brands could test content, promotions, recommendations, product sorting, messaging, and customer journey ideas. This made it particularly valuable for companies with lots of visitors, lots of products, and lots of opportunities to accidentally show the wrong offer to the wrong person at the wrong time.
6. Kameleoon
Best for: Teams that need both web and product experimentation.
Kameleoon was built for organizations that did not want to choose between marketing-led optimization and developer-led experimentation. It supported testing across websites, mobile applications, and product features, with audience targeting and feature management capabilities. This was a useful option for companies moving beyond simple landing-page tests and toward a broader experimentation culture across digital products.
7. Convert Experiences
Best for: Privacy-conscious CRO teams and agencies.
Convert Experiences was popular with optimization specialists who wanted advanced testing features without the heavyweight feel of some enterprise platforms. It supported A/B testing, split URL experiments, personalization, targeting, and integrations with common analytics tools. Agencies and mid-market companies often appreciated its focus on flexible testing workflows, practical support, and a more approachable path to serious conversion rate optimization.
8. Split
Best for: Product teams using feature flags to test releases.
Split was a strong option for engineering and product organizations that wanted to connect feature delivery with experimentation. Instead of fully launching a new feature and crossing every available finger, teams could use feature flags to expose different versions to controlled user groups. They could then measure adoption, engagement, retention, or revenue impact before deciding whether to roll the feature out more broadly.
9. LaunchDarkly
Best for: Safer software releases and controlled product experiments.
LaunchDarkly became a favorite among software teams because it treated experimentation as part of responsible delivery. Its feature flags helped teams release functionality gradually, target specific user groups, test variations, and quickly turn off a problematic feature when reality became less charming than the roadmap. For engineering-led companies, this reduced deployment risk while making experimentation a normal part of building software.
10. Statsig
Best for: Product-led businesses that care deeply about metrics.
Statsig appealed to companies that wanted experimentation, feature management, and product analytics in one connected environment. It was designed for teams that needed trustworthy statistical analysis and detailed measurement of product changes. For a SaaS business testing onboarding, pricing prompts, recommendations, or feature adoption, Statsig could help turn product decisions into measurable learning instead of a battle of the loudest opinion in Slack.
11. GrowthBook
Best for: Data teams seeking open-source and warehouse-native experimentation.
GrowthBook was a compelling choice for technical teams that wanted transparency, control, and a close relationship with their own data warehouse. It supported feature flags and experimentation while allowing companies to use existing metrics and data infrastructure. That made it appealing for teams wary of exporting every valuable event into yet another black-box platform with a mysterious dashboard and a suspiciously cheerful upsell button.
12. Unbounce
Best for: Paid-media landing pages and fast marketing tests.
Unbounce was not trying to be a complete enterprise experimentation operating system, and that was part of its charm. It focused on helping marketers build, launch, and A/B test landing pages without waiting for designers or developers. For campaigns involving paid search, social ads, lead generation, webinars, or product launches, Unbounce made it easier to test messaging and page structure at a speed that matched the advertising calendar.
13. HubSpot
Best for: Inbound marketing teams already using HubSpot.
HubSpot’s A/B testing capabilities made sense for organizations that wanted to optimize pages, CTAs, and marketing emails within the same inbound marketing platform. It was particularly useful for teams that valued convenience and connected reporting over highly technical experimentation features. If your lead generation, content marketing, email nurturing, and CRM workflows already lived in HubSpot, keeping basic tests there could prevent unnecessary tool sprawl.
14. Zoho PageSense
Best for: Budget-conscious website optimization teams.
Zoho PageSense offered a practical collection of A/B testing, split URL testing, heatmaps, funnel analysis, session recording, and website personalization features. It was a sensible option for smaller businesses that wanted a broader optimization toolkit without immediately stepping into enterprise pricing territory. For many teams, it offered enough power to run meaningful experiments before they needed a platform with more specialized product experimentation infrastructure.
How to Choose the Right A/B Testing Platform
Start with the job you need the tool to do. A content marketer optimizing PPC landing pages has different needs from a product manager testing a new recommendation algorithm. A retailer personalizing product pages has different needs from an engineer rolling out a feature to five percent of users.
Choose a visual, no-code platform when speed matters and the experiments mostly involve web content, copy, layouts, forms, and calls to action. Choose feature experimentation software when tests involve application logic, mobile experiences, pricing rules, recommendation systems, or backend changes. Choose an enterprise platform when you need advanced audience targeting, personalization, governance, security, and collaboration across many teams.
Also, inspect the reporting model before you fall in love with a slick dashboard. Ask how the platform handles sample size, statistical significance, false positives, attribution, experiment overlap, and guardrail metrics. A tool that announces “Winner!” after three conversions is not helping you grow. It is helping you create a very confident mistake.
A Simple A/B Testing Workflow That Actually Helps Growth
Begin with a real business question. For example: “Will showing delivery timing earlier in checkout increase completed purchases?” That is more useful than: “Should we make the button green because someone saw a competitor do it?”
Next, define a primary metric and at least one guardrail metric. The primary metric might be completed purchases, demo requests, or activated users. Guardrails could include refund rate, page speed, customer support tickets, account cancellations, or average order value. A test that boosts signups while creating a flood of unqualified leads is not necessarily a win. It may simply be a more efficient way to annoy your sales team.
Launch one meaningful change at a time when possible, set the test duration before starting, ensure traffic is randomized correctly, and document the hypothesis. When the test ends, share the result whether it wins, loses, or lands in the awkward middle called “inconclusive.” Every outcome can improve future decisions when the experiment was designed properly.
Field Notes: of A/B Testing Experience
The most useful lesson from real-world A/B testing is that users are wonderfully unpredictable. Teams often assume that more information creates more trust, more options create more freedom, and more urgency creates more conversions. Sometimes that is true. Other times, the page becomes a digital garage sale where every badge, timer, popup, and discount code is screaming for attention.
One recurring experience is that the strongest tests usually begin with customer evidence, not design inspiration. Heatmaps can show that visitors are ignoring an important section. Session recordings can reveal that users repeatedly click an element that is not clickable. Sales calls can expose confusion about pricing. Support tickets can show that buyers do not understand a policy. These observations create far better test hypotheses than “Let’s try a new gradient because gradients are having a moment.”
Another lesson is that a conversion increase is only valuable when it improves the right outcome. Imagine testing a checkout page and discovering that a louder “Buy Now” button increases orders by five percent. Great news, perhaps. But then refund requests rise, customer service calls increase, and repeat purchases drop. The button may have increased impulsive orders while lowering long-term customer quality. This is why experienced growth teams use guardrail metrics. Revenue matters, but durable revenue matters more.
Product teams learn a similar lesson during feature experiments. A new onboarding flow may increase the percentage of users who complete setup, but that does not automatically mean those users become active customers. The more meaningful metric could be whether they complete a first valuable action, return during the following week, invite teammates, or upgrade to a paid plan. The best A/B testing programs connect immediate behavior to the business result that actually matters.
Patience is also a competitive advantage. A test can look exciting after two days, especially when a variation is ahead by a few conversions. It is tempting to announce a winner, update the slide deck, and celebrate with pastries. Resist. Short-term results can be noisy, especially with low traffic, uneven audience segments, weekday-versus-weekend behavior, promotions, or sudden shifts in traffic sources. A disciplined test plan is less glamorous than a dramatic dashboard screenshot, but it is far more likely to produce dependable learning.
Finally, the healthiest experimentation culture does not worship winning tests. It values clear hypotheses, reliable measurement, rapid learning, and honest documentation. A losing test can save months of development work. An inconclusive test can reveal that the expected effect is too small to justify implementation. A winning test can become a new control and inspire the next question. Growth is not one giant breakthrough. More often, it is a long series of thoughtful decisions that compound while everyone else is still arguing about button colors.
Final Thoughts
The best A/B testing tool in 2023 was not automatically the platform with the longest feature list. It was the one that matched your team’s skills, traffic volume, testing maturity, data stack, and growth goals. Optimizely, VWO, Adobe Target, AB Tasty, and Dynamic Yield were strong options for larger optimization programs. Statsig, LaunchDarkly, Split, and GrowthBook were compelling for product experimentation. Unbounce, HubSpot, and Zoho PageSense made testing more accessible for marketing teams.
Pick a platform that helps you form better hypotheses, run cleaner experiments, and make decisions with confidence. Then remember the most important rule in experimentation: test assumptions, not just decorations.
