# Outtest

> Outtest is an AI split tester. It connects read-only to a business's analytics and payment tools, finds where the funnel loses the most money, then designs, launches and monitors split tests on the website, pricing, trials, cancel flow and Meta ads, and keeps the winners. Every test is judged on revenue per visitor from the payment tool (Stripe, Shopify, Polar, Whop, Paddle and others), not clicks or signups.

## Key facts

- Four AI agents: Lead plans the tests, Analyst finds the leaks in the data, Builder writes each new version (it never invents claims, discounts or reviews), Referee calls winners with plain statistics, not an AI opinion.
- A winner must clear three bars, each adjustable: at least 7 days and enough visitors per version to reliably spot the minimum lift, a 90% chance of beating the original, and a lift of at least 10%. Tests that haven't cleared all three by day 42 end as a draw and the original stays. Outtest recommends settings that fit each business's traffic, because checking a test every day without a visitor minimum crowns lucky versions.
- 5% of visitors keep seeing the original after a win, so Outtest can show what each winner actually earned.
- Google SEO and AI SEO (being cited by ChatGPT, Claude and Perplexity) are tested as page-group tests: changed pages against similar unchanged pages.
- Changes to checkout, pricing and cancel flows always wait for the owner's approval.
- Installs with one script (Google Tag Manager, Shopify, Webflow, Wix, WordPress, or one pasted line).
- Pricing: Starter $29/month (3 tests a month), Growth $49/month (10 tests a month), Pro $99/month (30 tests a month), Scale $499/month (unlimited tests). 20% off yearly. Every plan covers one site and includes every feature. No free trial.
- Made by Sancar Media (UK). Website: https://useouttest.com. Contact: outtest@sancarmedia.com.

## Product

- [Home](https://useouttest.com/): what Outtest does and how it works
- [Pricing](https://useouttest.com/pricing): plans and what counts as a test
- [Outtest alternatives](https://useouttest.com/alternatives/outtest): tools we recommend when Outtest isn't the right fit

## Free tools

- [A/B test sample size calculator](https://useouttest.com/tools/ab-test-sample-size-calculator): Work out how many visitors each version of your A/B test needs, from your conversion rate and the smallest lift you care about. Free, no signup.
- [A/B test significance calculator](https://useouttest.com/tools/ab-test-significance-calculator): Paste your visitors and conversions to see the lift, the chance version B beats A, the p-value and a confidence interval. Free, no signup.
- [A/B test duration calculator](https://useouttest.com/tools/ab-test-duration-calculator): Find out how many days your A/B test needs from your daily traffic, conversion rate and the lift you want to detect, rounded to full weeks. Free.
- [Revenue per visitor calculator](https://useouttest.com/tools/revenue-per-visitor-calculator): Compare two versions on revenue per visitor, not clicks. See when more signups or orders actually means less money. Free, no signup.
- [Minimum detectable effect calculator](https://useouttest.com/tools/minimum-detectable-effect-calculator): Find the smallest lift your traffic can reliably detect in a set number of weeks. Tells you whether a test is worth running before you start.
- [Sample ratio mismatch (SRM) checker](https://useouttest.com/tools/srm-checker): Check whether your A/B test's traffic split is broken. Enter visitors per version and the split you expected; get a chi-square test in one click.
- [Split test ROI calculator](https://useouttest.com/tools/split-test-roi-calculator): See what split testing is worth: extra revenue a month and a year from the tests you run, how often they win and how big wins are.
- [A/B test ideas](https://useouttest.com/tools/ab-test-ideas): More than 100 specific A/B test ideas for landing pages, pricing, checkout, onboarding, paywalls, cancel flows, ads and SEO. Filter by page and business.

## Guides

- [A/B testing vs multivariate testing](https://useouttest.com/guides/ab-testing-vs-multivariate-testing): An A/B test compares the original page with one or more complete alternatives, while a multivariate test changes several elements at once (say two headlines, two images and two buttons, making 8 combinations) to measure each element's effect and how they interact. Use A/B tests for most decisions. Multivariate tests pay off only on high-traffic pages, because picking the best of 8 combinations takes about 4 times the visitors of a simple A/B test.
- [A/B testing with low traffic](https://useouttest.com/guides/ab-testing-low-traffic): Low traffic means your test can only spot big differences, so plan for it. Test bold changes that could move conversion by 20 to 50%, set that as your minimum lift, test further up the funnel where there are more events (or buy traffic with ads to test messages), and use a sequential method that can stop early. At 3,000 visitors a week and a 2% conversion rate, a 10% lift takes about a year to detect, while a 50% lift takes about two weeks.
- [Annual vs monthly pricing, and how to test which to show first](https://useouttest.com/guides/annual-vs-monthly-pricing-test): Test the default billing option by splitting new visitors between an annual-first and a monthly-first pricing page, and judge on revenue per visitor at 30 and 90 days plus projected 12-month revenue. Showing annual first moves cash forward and can cut the number of buyers, so cash in the first month overstates the win. In RevenueCat's app data, a median 28% of annual subscribers were still subscribed after a year, against 11% on monthly plans.
- [Bayesian vs frequentist A/B testing](https://useouttest.com/guides/bayesian-vs-frequentist-ab-testing): Frequentist A/B testing asks how surprising your data would be if there were no difference (the p-value) and controls false wins over many tests with a fixed sample size. Bayesian A/B testing combines the data with a prior belief to give the probability that B beats A and the expected cost of choosing wrong. With a flat prior and decent traffic they usually agree, and neither method makes it safe to stop the moment a result looks good.
- [Checkout A/B testing for e-commerce and SaaS](https://useouttest.com/guides/checkout-ab-testing): Test the things that make people abandon: surprise costs, forced account creation, long forms, missing payment options and unclear totals. Judge each test on revenue per visitor, with average order value and refunds alongside, and check every payment method works before launch. Baymard puts the average documented cart abandonment rate at 70.22%, and in its survey of US shoppers, 40% of those who abandoned for a reason other than browsing said extra costs were too high.
- [Common A/B testing mistakes (and how to avoid them)](https://useouttest.com/guides/ab-testing-mistakes): Most bad A/B test results come from a handful of mistakes: stopping the moment a result looks significant, running tests too small to detect anything, judging on clicks or signups instead of revenue, and missing a broken traffic split. Stopping at the first significant result can push a 5% false win rate above 25%. Fix them by sizing the test first, running whole weeks, judging on revenue per visitor and checking the split.
- [Free trial vs no free trial (and how long a trial should be)](https://useouttest.com/guides/free-trial-vs-no-trial): Neither wins everywhere. A trial helps when people need to use the product to believe it works, and charging up front works when the value is clear before use. Test it by splitting new visitors and judging on revenue per visitor counted after the longest trial has ended plus one billing cycle. On length, RevenueCat's 2026 app data shows a median 42.5% trial-to-paid rate for 17 to 32-day trials against 25.5% for trials of 4 days or less, but longer trials delay revenue and some of that gap is selection.
- [How holdout groups prove what your tests actually earned](https://useouttest.com/guides/holdout-groups): A holdout group is a small random slice of visitors, often 5 to 10%, who keep seeing the original experience after you ship winning tests. Comparing their revenue per visitor with everyone else's shows what the winners earned together. It's usually less than the sum of the individual test lifts, because of lucky readings, overlapping changes and effects that fade.
- [How long should you run an A/B test?](https://useouttest.com/guides/how-long-to-run-an-ab-test): Run an A/B test until it reaches the sample size you calculated before launch, then round up to whole weeks so every weekday is counted equally. Run at least one full week (two if returning visitors might react to novelty) and usually no more than four to six, because cookies expire, seasons shift and traffic mix drifts. Days needed = visitors needed across all versions / visitors entering the test per day.
- [How many visitors do you need for an A/B test?](https://useouttest.com/guides/ab-test-sample-size): It depends on your conversion rate and the smallest lift you want to detect. At a 3% conversion rate, 95% confidence and 80% power, you need about 13,900 visitors per version to detect a 20% relative lift and about 53,200 per version for a 10% lift. Halving the lift you want to detect roughly quadruples the sample, so low-traffic sites should test bold changes.
- [How to A/B test CTA buttons (copy, placement and color)](https://useouttest.com/guides/cta-button-ab-testing): Test what the button says and what happens after the click first, then where it sits, and color last. Color rarely matters on its own once the button clearly stands out from the page, and the famous color wins came from Google and Bing, which test on millions of users. At a 4% conversion rate, detecting a 2% lift needs about 960,000 visitors per version, which most sites never get.
- [How to A/B test email subject lines](https://useouttest.com/guides/email-subject-line-ab-testing): Split a random sample of your list, send each subject line to one group, and pick the winner on click rate or revenue per recipient, not open rate. Apple's Mail Privacy Protection, launched in 2021, loads tracking pixels whether or not someone reads the email, so opens are inflated and can crown the wrong subject line. To detect a 20% lift on a 2.5% click rate you need about 15,600 recipients per version, so smaller lists should split the whole send 50/50 and learn for next time.
- [How to A/B test headlines](https://useouttest.com/guides/headline-ab-testing): Write two to four headlines that make genuinely different promises, split visitors evenly, and judge on revenue per visitor rather than clicks or signups. Run for at least one to two full weeks and until you reach the sample size for the lift you care about. Headlines can matter a lot: a change to how Bing displayed ad headlines raised its revenue 12%, which the researchers put at over $100 million a year in the US.
- [How to A/B test Meta (Facebook and Instagram) ads](https://useouttest.com/guides/meta-ads-ab-testing): Use the A/B test tool in Meta Ads Manager or Experiments, which splits your audience so nobody sees both versions, change one variable, and run it for at least 7 days (Meta allows 1 to 30). Meta picks winners on cost per result, so set the result to purchases, then check the winner against paying customers per dollar in your own payment data, because the ad with the best click-through rate often sells less.
- [How to A/B test on Shopify](https://useouttest.com/guides/ab-testing-shopify): Shopify has built-in A/B tests called experiments, under Markets > Rollouts, on the Grow plan or higher. They split visitors 50/50 by default and can test a theme edit or replacement, a checkout and accounts configuration, catalog prices or discounts. Theme experiments report conversion, add-to-cart and bounce rates but not revenue, so check revenue per session yourself before you keep a winner.
- [How to A/B test your cancel flow](https://useouttest.com/guides/cancel-flow-ab-testing): Randomly assign subscribers who start cancelling to the current flow or a new one, such as a pause offer, a downgrade or a one-time discount, and judge on revenue kept per cancel attempt at 30 and 90 days, not on how many people clicked 'stay'. A pause offer can look worse at 30 days and better at 90, because paused customers pay nothing until they come back. Always keep a clear way to finish cancelling, which California's law requires next to any save offer.
- [How to A/B test your onboarding](https://useouttest.com/guides/onboarding-ab-testing): Assign new signups at random to onboarding versions, track time to first value and activation as early signals, and pick the winner on revenue per signup 30 to 90 days later. Activation can move much more than revenue, so a version that lifts activation by 30% may lift revenue by 4% or not at all. Define activation from your own data as the early action that best predicts paying.
- [How to A/B test your pricing](https://useouttest.com/guides/how-to-ab-test-pricing): Split new visitors at random between two prices or two pricing pages, keep each person on one version, and judge on revenue per visitor over at least 30 days, checking again at 90 days for subscriptions. Testing packaging, plan order, anchors and annual discounts carries less risk than showing different prices for the same product. If you do test the price itself, test on new visitors only, honor the lower price for anyone who asks, and check local rules, since the EU requires traders to disclose prices personalised by automated decision-making.
- [How to measure A/B tests with Stripe revenue](https://useouttest.com/guides/ab-testing-stripe): Give every visitor an ID and a version when they land, then pass both to Stripe with the payment: client_reference_id on Payment Links, Checkout Sessions and pricing tables, or metadata on the Checkout Session, subscription or customer. Sum net revenue per version after refunds and divide by the visitors assigned to that version. That revenue per visitor number is the one to judge the test on.
- [How to run an A/B test, step by step](https://useouttest.com/guides/how-to-run-an-ab-test): Pick one money metric, find the funnel step that leaks most, write a hypothesis with a minimum lift, then calculate the sample size and round the run time up to whole weeks. Split visitors 50/50 by person, check the split after a day or two, and don't stop early. Read the result once at the planned end, ship the winner and keep a small holdout to confirm what it really earned.
- [How to test SEO changes on Google with split testing](https://useouttest.com/guides/seo-split-testing): You can't A/B test Google per visitor, because Googlebot sees one version of each URL and showing it something different from users is cloaking. Instead, split similar pages into two matched groups, change one group, and compare its Google clicks with the unchanged group over the following weeks. This page-group method beats a before-and-after comparison because the control group absorbs seasonality and algorithm updates.
- [How to test whether ChatGPT, Claude and Perplexity recommend you](https://useouttest.com/guides/ai-seo-testing): Write a fixed panel of 30 to 60 prompts your buyers would ask, run each one several times in each assistant every week, and record whether your brand is mentioned and which of your pages are cited. Track the mention rate over time rather than any single answer, because AI recommendation lists rarely repeat. To test a change, edit one group of pages and compare its citation rate with a matched group you left alone.
- [Is split testing worth it? How to work out the ROI](https://useouttest.com/guides/split-test-roi): Split testing is worth it when the revenue flowing through the pages you test is large enough that a few kept lifts outweigh the cost of tools and time. Work out your break-even lift (monthly testing cost divided by monthly revenue through tested pages), then compare it with realistic results: most tests don't win, and winners usually earn less than their test reported. A business with $60,000 a month through its pricing and landing pages and $649 a month in costs breaks even at a 1.1% lift.
- [Minimum detectable effect (MDE), explained](https://useouttest.com/guides/minimum-detectable-effect): The minimum detectable effect (MDE) is the smallest true lift your test has an 80% chance of detecting at 95% confidence, given your traffic and baseline conversion rate. The quick formula is MDE ≈ 4 × √(p(1 − p) ÷ n), with n visitors per version. With 10,000 visitors a week, a 3% conversion rate and a 4-week test, the MDE is about 0.48 percentage points, a 16% relative lift, so smaller real gains will usually go unnoticed.
- [Paywall A/B testing for mobile apps](https://useouttest.com/guides/paywall-ab-testing): Randomly assign new installs to paywall versions and judge on revenue per install over at least 30 to 60 days, after refunds, not on trial starts or paywall conversion. Test the plan lineup, the pre-selected plan, the trial and the timing before copy and design. Apple requires the billed amount to be the most prominent price on the screen, and both Apple and Google require clear trial and renewal terms, so every version you test has to meet those rules.
- [Statistical significance in A/B testing, explained](https://useouttest.com/guides/statistical-significance-explained): An A/B test result is statistically significant when its p-value falls below a bar set in advance, usually 0.05. The p-value is the chance of seeing a gap at least as big as yours if the two versions truly performed the same. It is not the chance that B is better, and a significant result is not 95% certain: when only about 10% of ideas work, roughly 1 in 5 significant winners is a false positive even in a well-run test.
- [What A/B testing with AI can and can't do](https://useouttest.com/guides/ab-testing-with-ai): AI is useful for generating and ranking test ideas, writing and building variations, and reading analytics to find where a funnel loses people. It shouldn't decide which version won, because models overestimate effects and can state false things confidently. Call winners with a rule set before launch and plain statistics on real visitor data, and check every AI-written claim, since fake reviews and invented facts carry legal risk.
- [What is A/B testing?](https://useouttest.com/guides/what-is-ab-testing): A/B testing splits visitors at random between the current version of a page (A) and a changed version (B), then compares one result that matters, such as orders or revenue per visitor. Because chance decides who sees what, a big enough gap can be pinned on the change. At a 3% conversion rate you need about 28,000 visitors in total to reliably spot a 20% lift, so low-traffic sites should test bold changes.
- [What is sample ratio mismatch (SRM) and how do you check for it?](https://useouttest.com/guides/sample-ratio-mismatch): Sample ratio mismatch (SRM) happens when the visitor counts in an A/B test's versions differ from the split you configured by more than chance can explain, for example 10,250 against 9,750 on a 50/50 test. Check it with a chi-square goodness-of-fit test on the visitor counts; a p-value below a strict bar such as 0.01 or 0.001 means the test is broken and its result shouldn't be trusted. It's common: about 6% of experiments at Microsoft had one.
- [What to A/B test first](https://useouttest.com/guides/what-to-ab-test-first): Test first where the most money leaks out of your funnel, usually checkout, pricing or the step with the worst drop-off, not the homepage. Score each idea on impact, confidence and ease (ICE, 1 to 10 each, multiplied), then check the test can finish within about six weeks at your traffic. Steps near the payment often have fewer visitors but higher conversion rates, so they can reach an answer faster than a homepage test.
- [What to A/B test on a landing page, and in what order](https://useouttest.com/guides/landing-page-ab-testing): Test in order of how much money a change can move: the offer and who the page is for first, then the headline and hero, then proof and objections, then the call to action and form, and layout and design last. Most sites can only detect big lifts, so start with big changes. At 3,000 visitors a week and a 2.5% conversion rate, a four-week test can only reliably detect a lift of about 32%.
- [Why stopping an A/B test early misleads you (the peeking problem)](https://useouttest.com/guides/peeking-problem-ab-testing): If you check an A/B test repeatedly and stop the first time it looks significant, the 5% false win rate you think you have becomes several times larger. In a simulation of 20,000 tests where both versions were identical, checking a 0.05 p-value every day for four weeks produced a 'significant' result at some point in 27.8% of tests. Fix it by fixing the sample size and reading the result once, or by using a sequential method built for repeated looks.
- [Why you should judge A/B tests on revenue per visitor](https://useouttest.com/guides/revenue-per-visitor): Judge A/B tests on revenue per visitor: the money a version brought in, after refunds, divided by every visitor who saw it. It catches the common case where a version wins more signups by selling cheaper plans and earns less overall. Revenue is noisier than a conversion rate, so a revenue test can need several times more visitors unless you cap unusually large payments at a limit set before the test starts.

## By industry

- [A/B testing for AI startups](https://useouttest.com/for/ai-startups): How AI founders should A/B test credit and usage pricing, free credits versus trials and pack sizes, and why heavy users make revenue tests need more traffic.
- [A/B testing for B2B software](https://useouttest.com/for/b2b-software): How B2B marketers should A/B test with low traffic and long sales cycles: what to measure instead of closed revenue, which tests to run first, and when to skip.
- [A/B testing for course creators and info products](https://useouttest.com/for/course-creators): How course creators should A/B test sales pages, prices, payment plans and tiers for courses, templates and digital products, and how much traffic it takes.
- [A/B testing for e-commerce brands](https://useouttest.com/for/ecommerce): How DTC brands should A/B test: ad landing pages, bundles, subscribe and save and first-order offers, the traffic each test needs, and why revenue decides.
- [A/B testing for indie hackers and solo founders](https://useouttest.com/for/indie-hackers): How solo founders with small traffic should A/B test: what a few thousand visitors can detect, the big-swing tests worth running, and when to skip testing.
- [A/B testing for mobile apps](https://useouttest.com/for/mobile-apps): How app developers should A/B test the money side of an app: website, web checkout, pricing and ads, plus where in-app paywall tests fit and what to measure.
- [A/B testing for paid communities and memberships](https://useouttest.com/for/memberships-and-communities): How paid communities and memberships should A/B test join pages, prices, trials and cancel flows, and why a membership test must look past the first payment.
- [A/B testing for SaaS](https://useouttest.com/for/saas): How SaaS teams should A/B test: the pricing, trial and cancel flow tests to run first, how much traffic each needs, and why to judge them on revenue.
- [A/B testing for Shopify stores](https://useouttest.com/for/shopify-stores): How Shopify store owners should A/B test: the product page, cart and shipping tests to run first, the traffic each needs, and why revenue per visitor decides.
- [Split testing for marketing agencies](https://useouttest.com/for/agencies): How marketing agencies should run split tests for clients: which clients have the traffic, which tests to run first, and how to report wins on revenue.

## Comparisons

- [Outtest vs AB Tasty](https://useouttest.com/compare/ab-tasty): AB Tasty is a Paris-born experimentation and personalization platform for websites and apps, now part of Wingify after its 2026 merger with VWO.
- [Outtest vs Amplitude Experiment](https://useouttest.com/compare/amplitude-experiment): Amplitude Experiment is Amplitude's A/B testing product, split into Feature Experimentation for code-based tests and Web Experimentation with a visual editor, both built on Amplitude analytics.
- [Outtest vs Coframe](https://useouttest.com/compare/coframe): Coframe is an AI platform that writes new versions of your website's copy, layout and images, tests them, and keeps shifting traffic toward the versions that convert best.
- [Outtest vs Convert](https://useouttest.com/compare/convert): Convert Experiences is a privacy-focused A/B testing platform with published prices, a visual editor, frequentist and Bayesian stats, and full-stack testing on its Pro plan.
- [Outtest vs Crazy Egg](https://useouttest.com/compare/crazy-egg): Crazy Egg is a website behavior analytics tool with heatmaps, session recordings and surveys, plus built-in A/B testing on its Plus plan and above.
- [Outtest vs Eppo](https://useouttest.com/compare/eppo): Eppo is a warehouse-native experimentation and feature flag platform that Datadog bought in 2025 and now sells as Datadog Experiments.
- [Outtest vs Google Optimize](https://useouttest.com/compare/google-optimize): Google Optimize was Google's free A/B testing and personalization tool for websites, built on Google Analytics, with a paid Optimize 360 version for enterprises.
- [Outtest vs GrowthBook](https://useouttest.com/compare/growthbook): GrowthBook is an open-source, warehouse-native platform for feature flags, A/B tests and product analytics that you can use in the cloud or host yourself.
- [Outtest vs Intelligems](https://useouttest.com/compare/intelligems): Intelligems is a Shopify app for testing and personalizing prices, shipping, content, offers and checkout, with results scored on profit per visitor.
- [Outtest vs Kameleoon](https://useouttest.com/compare/kameleoon): Kameleoon is an experimentation platform for web, feature and mobile app tests, built around Prompt-Based Experimentation (PBX), where you describe a change and AI builds the variant.
- [Outtest vs LaunchDarkly](https://useouttest.com/compare/launchdarkly): LaunchDarkly is a feature management platform for engineering teams, with experimentation, observability and AI config tools built on its feature flags.
- [Outtest vs Mutiny](https://useouttest.com/compare/mutiny): Mutiny was a B2B website personalization platform. Since its 2026 relaunch it's an AI agent that creates customer-facing sales and marketing assets such as deal rooms, business cases and ABM campaign pages.
- [Outtest vs Optimizely](https://useouttest.com/compare/optimizely): Optimizely is an enterprise experimentation platform with a visual editor for website tests, SDKs for server-side and feature tests, and Opal AI agents that help plan and build experiments.
- [Outtest vs PostHog Experiments](https://useouttest.com/compare/posthog): PostHog Experiments is the A/B testing product inside PostHog, an open-source product platform that also covers analytics, session replay, feature flags and a data warehouse.
- [Outtest vs RevenueCat](https://useouttest.com/compare/revenuecat): RevenueCat is a subscription and in-app purchase backend for iOS, Android and web apps, and its Experiments feature A/B tests prices, trials and paywalls and reports the results on revenue and lifetime value.
- [Outtest vs Shoplift](https://useouttest.com/compare/shoplift): Shoplift is a Shopify app for A/B testing themes, templates, URLs and, on higher plans, prices, with results reported on revenue per visitor from Shopify orders.
- [Outtest vs Statsig](https://useouttest.com/compare/statsig): Statsig is a feature flag and experimentation platform with built-in product analytics and session replay, owned by Amplitude since May 2026.
- [Outtest vs Superwall](https://useouttest.com/compare/superwall): Superwall lets subscription apps design, target and A/B test paywalls on iOS, Android and web without shipping an app update, with a free subscription backend underneath.
- [Outtest vs Unbounce](https://useouttest.com/compare/unbounce): Unbounce is a drag-and-drop landing page builder with built-in A/B testing and an AI feature, Smart Traffic, that routes each visitor to the variant most likely to convert them.
- [Outtest vs VWO](https://useouttest.com/compare/vwo): VWO is a website A/B testing and conversion optimization suite from Wingify, now sold under the Wingify name after its 2026 merger with AB Tasty.
- [Outtest vs Webflow Optimize](https://useouttest.com/compare/webflow-optimize): Webflow Optimize is Webflow's paid add-on for A/B testing, rules-based personalization and AI-driven variant delivery, built into the Webflow editor.

## Optional

- [AB Tasty alternatives](https://useouttest.com/alternatives/ab-tasty): options for teams moving off AB Tasty
- [Amplitude Experiment alternatives](https://useouttest.com/alternatives/amplitude-experiment): options for teams moving off Amplitude Experiment
- [Coframe alternatives](https://useouttest.com/alternatives/coframe): options for teams moving off Coframe
- [Convert alternatives](https://useouttest.com/alternatives/convert): options for teams moving off Convert
- [Crazy Egg alternatives](https://useouttest.com/alternatives/crazy-egg): options for teams moving off Crazy Egg
- [Eppo alternatives](https://useouttest.com/alternatives/eppo): options for teams moving off Eppo
- [Google Optimize alternatives](https://useouttest.com/alternatives/google-optimize): options for teams moving off Google Optimize
- [GrowthBook alternatives](https://useouttest.com/alternatives/growthbook): options for teams moving off GrowthBook
- [Intelligems alternatives](https://useouttest.com/alternatives/intelligems): options for teams moving off Intelligems
- [Kameleoon alternatives](https://useouttest.com/alternatives/kameleoon): options for teams moving off Kameleoon
- [LaunchDarkly alternatives](https://useouttest.com/alternatives/launchdarkly): options for teams moving off LaunchDarkly
- [Mutiny alternatives](https://useouttest.com/alternatives/mutiny): options for teams moving off Mutiny
- [Optimizely alternatives](https://useouttest.com/alternatives/optimizely): options for teams moving off Optimizely
- [PostHog Experiments alternatives](https://useouttest.com/alternatives/posthog): options for teams moving off PostHog Experiments
- [RevenueCat alternatives](https://useouttest.com/alternatives/revenuecat): options for teams moving off RevenueCat
- [Shoplift alternatives](https://useouttest.com/alternatives/shoplift): options for teams moving off Shoplift
- [Statsig alternatives](https://useouttest.com/alternatives/statsig): options for teams moving off Statsig
- [Superwall alternatives](https://useouttest.com/alternatives/superwall): options for teams moving off Superwall
- [Unbounce alternatives](https://useouttest.com/alternatives/unbounce): options for teams moving off Unbounce
- [VWO alternatives](https://useouttest.com/alternatives/vwo): options for teams moving off VWO
- [Webflow Optimize alternatives](https://useouttest.com/alternatives/webflow-optimize): options for teams moving off Webflow Optimize