10 Best Software for A/B Testing

What A/B Testing Actually Buys You

Most product pages, checkout flows and email subject lines are decided by whoever argued loudest in the last team meeting. A/B testing replaces that argument with a number. You show two versions of something to similar slices of your audience, wait until the sample size is real, and let the winner tell you what to build next.

For a digital product or service business, the stakes are higher than they look. A checkout page that converts half a point better compounds every single month, because you’re not paying for shelf space or shipping. The tool you pick decides how fast you can run that loop, and how much of your traffic you can afford to spend testing instead of selling.

Google Optimize, which used to be the default answer for smaller stores, shut down in September 2023. Anyone still Googling “best A/B testing tool” and landing on tutorials that assume Optimize is free needs a different shortlist. Here’s the one we actually use and recommend, split by where your tests are happening.

A quick note before the list: none of these tools will save a test built on a bad hypothesis. Spend ten minutes writing down why you think a change will help before you open any testing dashboard. “The button should be bigger” isn’t a hypothesis. “Buyers hesitate at the price step because they can’t see what happens after purchase” is one, and it points to a specific, testable fix.

Web Testing vs. Email Testing: Pick the Right Category First

This is where most buying decisions go wrong. A tool built to split-test landing pages and a tool built to split-test email subject lines solve different problems, and the pricing reflects that. Full experimentation platforms like VWO or Optimizely assume you have enough web traffic to reach statistical significance on page variants within a few weeks. If your main revenue lever is actually your email list, a $200-plus-per-month web testing platform is the wrong purchase. Read the list below with your actual bottleneck in mind, not the one that sounds more sophisticated.

10 A/B Testing Tools Worth Your Budget

1. VWO (Visual Website Optimizer)

VWO is the closest thing to a full replacement for what Google Optimize used to offer, and then some. Beyond straightforward A/B and multivariate tests, it bundles heatmaps and session recordings so you can see why a variant won, not just that it did. Plans start around $244 a month, which puts it out of reach for very early-stage stores but firmly in range once you’re running a few thousand checkout sessions a month.

2. Optimizely

Optimizely is built for teams that need to test across web, mobile apps and backend feature flags from one platform. It’s the enterprise pick, quote-based, with an implementation curve to match. Worth evaluating once you have a dedicated growth or engineering hire who’s going to own it, not before.

3. Convert.com

Convert sits in the middle of the market and takes privacy seriously in a way the bigger platforms sometimes treat as an afterthought. No cookie consent workarounds, GDPR-friendly by default, and pricing that starts closer to $99 a month. If you sell into the EU and don’t want your legal team flagging your testing stack, this is worth a look before VWO.

4. AB Tasty

AB Tasty pairs experimentation with on-site personalisation, so a test doesn’t have to end when it wins. You can roll a winning variant out selectively to specific segments instead of an all-or-nothing rollout. Pricing is quote-based, and the sales process reflects a mid-to-enterprise target customer.

5. Moosend: Best for Email A/B Testing

Everything above tests your website. If most of your growth actually happens in the inbox, none of it matters as much as your email platform’s testing capability. Moosend builds subject line, content and send-time testing directly into its automation editor, and reports revenue per email on every test, not just open rate. Plans start at $9 a month with unlimited sends, which makes it realistic to test every campaign instead of saving tests for the “important” ones. You can try Moosend’s free trial here if your funnel’s weak point is nurture emails rather than the landing page itself, and it pairs well with the tactics in our roundup of email automation tools for digital product sellers.

6. Crazy Egg

Crazy Egg started as a heatmap tool and added A/B testing later, which shows in how approachable it is. From about $29 a month, it’s the tool to reach for when you want a visual answer to “why didn’t this page convert” alongside the split test itself, without a multi-week onboarding.

7. Hotjar

Hotjar’s core strength is still session recordings and on-page surveys, and its A/B capability is genuinely secondary to that. Starting around $32 a month, it earns its place on this list less as a testing engine and more as the tool that tells you what to test in the first place.

8. Kameleoon

Kameleoon leans hard into AI-driven personalisation and predictive targeting, automatically routing more traffic toward a winning variant as confidence builds instead of waiting for a fixed end date. Quote-based pricing, and realistically a tool for teams already running dozens of concurrent experiments.

9. PostHog

PostHog is the open-source option, and it’s a genuinely different animal from everything else on this list. Feature flags, experimentation and product analytics live in one self-hostable platform, free if you run it yourself, with a usage-based cloud tier if you’d rather not. Engineering-led teams that already think in terms of feature flags tend to prefer it over a marketing-first tool like VWO.

10. Zoho PageSense

PageSense is the option nobody mentions first, and that’s a mistake if you’re already inside the Zoho ecosystem for CRM or email. It covers A/B testing, funnel analysis and heatmaps at a price point closer to Crazy Egg than VWO, and the native integration with Zoho CRM means test results can trigger workflows without a Zapier step in between.

Running Your First Test Without Wasting a Month

Buying the tool is the easy part. Most first attempts at A/B testing fail quietly, not because the software was wrong, but because the test itself was set up to never produce a usable answer. A few rules keep that from happening.

Test one variable at a time. Swapping the headline, the button color and the price display in the same variant tells you which combination won, not which change mattered. If you’re tight on traffic, that’s expensive information you’ll never be able to act on individually.

Decide your sample size before you launch, not while you’re watching the results come in. Peeking at a test after two days and calling a winner because it’s “up 20%” is how most bad decisions get made. Early results swing wildly. A test that looks decisive on day two frequently reverses by day twelve once the sample stabilizes.

Run tests for full weeks, not arbitrary day counts. A Tuesday-to-Thursday test misses your weekend traffic entirely, and weekend buyers often behave differently than weekday ones, especially for anything priced as an impulse purchase. Seven, fourteen or twenty-eight days, never something that cuts across a partial week.

Pick a metric that actually matters before you start. Click-through rate on a call-to-action button is easy to move and easy to lie to yourself with. Revenue per visitor, trial-to-paid conversion, or completed checkout is harder to move and much more honest about whether the change helped.

Mistakes That Quietly Wreck Your Test Data

Running tests during a launch, a sale, or a big traffic spike from a newsletter blast is one of the most common ways teams poison their own data. That traffic doesn’t behave like your normal audience, and a variant that wins during a 50%-off weekend will not necessarily win in a normal week.

Stopping a test the moment it hits statistical significance in your favor, then not stopping the one that isn’t going your way, is a bias almost everyone has fallen into at least once. Set the end date in advance. Respect it either way.

Testing on a page that gets forty visits a week is another quiet trap. Some pages simply don’t get enough traffic to ever reach significance, no matter how long you run the experiment. For low-traffic pages, skip formal A/B testing and rely on qualitative tools instead, session recordings, on-page surveys, direct customer feedback. Hotjar earns its spot on this list for exactly that reason.

How to Actually Choose

Ignore the feature comparison charts for a second and ask where your funnel actually leaks. If it’s the landing page or checkout, start with Convert.com or VWO depending on budget. If it’s email open and click rates, Moosend will get you testing faster than any web platform will. If you’re an engineering-heavy team already shipping behind feature flags, PostHog probably fits your workflow better than a marketing tool ever will.

One more thing worth saying plainly: a testing tool doesn’t fix a page that has no traffic. Solve the visibility problem first, then check our separate breakdown of conversion rate optimization software if you want tools built specifically around lifting the number a test is measuring, not just running the test itself.

What’s Actually Worth Testing on a Digital Product Site

Generic conversion advice tends to assume you’re selling a physical product with shipping costs and product photography to worry about. Digital sellers have a different set of high-value tests, and most of them live closer to the price and the delivery promise than to the page design.

Pricing page structure is the single highest-leverage test most EDD and WooCommerce sellers never run. Three tiers versus two, a highlighted “most popular” badge versus none, monthly-first versus annual-first framing, these decisions get made once during setup and never revisited. A four-week test on tier framing alone regularly outperforms months of blog traffic growth in terms of actual revenue impact.

Checkout field count is the second. Every additional required field on a checkout form is a small tax on completion rate. Testing a version that asks for company name and phone number against a version that only asks for what’s needed to fulfill the order will usually surprise you with how much friction those “just in case” fields were adding.

Trial length and trial type matter more than almost anyone tests for. A 14-day free trial and a 7-day trial with a money-back guarantee attract different buyers and convert at different rates, and the difference isn’t intuitive until you’ve run the actual numbers on your own audience.

Refund policy visibility is the one nobody wants to test because it feels like admitting weakness. It’s the opposite. A visible, specific refund policy on a checkout page for a digital download reduces pre-purchase anxiety more than almost any design change, and it’s a two-minute test to set up in any of the tools above.

Where Testing Fits Into a Bigger Conversion Strategy

Testing tools answer “which version wins.” They don’t answer “what should I be testing.” That second question comes from your analytics, not your testing platform. If you’re selling through Easy Digital Downloads, pull your actual conversion data before you set up a single experiment, our guide on using EDD analytics and conversion tracking to make data-driven growth decisions walks through exactly which reports matter and which ones are noise.

Checkout abandonment is usually the highest-leverage place to point a test. Before you spend a month split-testing button colors, check whether people are dropping off at a specific checkout step, our piece on EDD store optimization for speed, security and conversions covers the fixes that tend to move the needle before you even open a testing dashboard.

Small teams get this backwards constantly. They buy the $244-a-month platform before they’ve confirmed they have enough monthly traffic to reach significance on anything. Run the math first. If you can’t get 1,000 sessions to a page in two weeks, your energy is better spent on traffic and email than on statistical experiments that will sit at “not yet significant” for a quarter.

Start with the cheapest tool that answers your actual bottleneck, not the one with the longest feature list.

Matching Tools to Budget Stage

Under $50 a month, your realistic options are Moosend for email tests, Crazy Egg for basic web tests paired with heatmaps, or PostHog self-hosted if you have someone comfortable running infrastructure. This tier is about proving the habit of testing before you fund a bigger platform.

Between $50 and $250 a month, Convert.com becomes realistic, and it’s the strongest privacy-conscious pick for anyone selling into Europe. Hotjar sits in this range too, more useful for diagnosis than for running the tests themselves.

Above $250 a month, VWO, Optimizely, AB Tasty and Kameleoon all become options, and the differentiator stops being price and starts being which one fits your existing stack. A Salesforce-heavy sales team leans toward Optimizely. A team that already lives in personalisation workflows leans toward Kameleoon or AB Tasty.

Skip the temptation to buy at the top of your budget range on day one. Prove the process works with a cheaper tool first. Upgrade once you’re running out of the current tool’s features, not before.

Questions Worth Answering Before You Buy

Do I need a testing tool if I’m under 1,000 monthly visitors?

Probably not yet, and that’s a fine place to be. Below roughly 1,000 monthly sessions to the page you want to test, most experiments will sit at “not statistically significant” for months. Spend that budget on traffic and on a heatmap or recording tool like Hotjar or Crazy Egg instead, and revisit formal A/B testing once volume catches up.

Can I run both email and web tests on one platform?

Not well. Web testing platforms treat email as an afterthought, and email platforms don’t touch your checkout page. Most serious sellers end up running two tools side by side, a web platform like Convert.com or VWO, and an email platform like Moosend, rather than compromising on one that does both poorly.

How long should a test run before I trust the result?

Long enough to cover at least two full weekly cycles and to reach the sample size your tool’s built-in significance calculator says you need, whichever is longer. Ending early because a variant looks good is the single most common way testing programs produce false confidence.

What if every test I run comes back “no significant difference”?

That’s a real answer, not a failed test. It usually means the variable you changed wasn’t the thing actually holding people back. Go back to session recordings or direct customer feedback, find the real friction point, and test that instead of a smaller version of the same idea.

Does A/B testing replace SEO or paid traffic work?

No, and treating it that way is a common early mistake. Testing improves what happens once someone already lands on your page. It does nothing for how many people arrive in the first place. Both matter, and running one without the other leaves real revenue on the table either way.

None of this works without traffic to test against, and it stops mattering the moment your checkout itself is broken or confusing. Fix the fundamentals first. Then let the data pick the winner instead of your gut.