19 of the Best A/B Testing Tools for 2026

A/B testing tools help teams compare different versions of a webpage, product experience, feature, or campaign and measure which version performs better against a defined goal.

In 2026, that covers a much broader range of software than traditional website split testing. Some tools are built for marketers using visual editors, while others handle server-side experiments, feature flags, mobile apps, warehouse-native analysis, personalization, or full-stack product experimentation.

The market has also changed significantly. Google Optimize has been unavailable since September 2023, while products such as Split and Eppo have been absorbed into Harness and Datadog respectively. This guide compares 19 current A/B testing and experimentation platforms, including options for CRO, product, engineering, ecommerce, mobile, and enterprise teams.

TL;DR

• Best for mature product and data teams: ABsmartly
• Best all-in-one CRO and personalization platform: Wingify
• Best for Adobe enterprise environments: Adobe Target
• Best for product experimentation and feature flags: Statsig
• Best open-source option: GrowthBook
• Best for experiments tied to observability: Datadog Experiments
• Best for teams already using Amplitude: Amplitude Experiment
• Best integrated product stack for startups: PostHog Experiments
• Best for landing-page testing: Unbounce
• Best for mobile-app experimentation: Firebase A/B Testing

A/B testing tools compared

Tool	Best for	Experimentation focus	Pricing
ABsmartly	Mature product and data teams	Full-stack experimentation	Custom
Wingify	CRO, marketing, ecommerce, and product teams	Web, product, personalization	Custom
Adobe Target	Adobe enterprise customers	Web, mobile, personalization	Custom
Statsig	Product and engineering teams	Experiments, analytics, feature flags	Free; Pro from $150/month
Harness FME	Engineering and product teams	Feature experimentation and rollouts	Free option; paid plans vary
GrowthBook	Technical and data teams	Open-source and warehouse-native	Free; Pro $40/seat/month
Datadog Experiments	Engineering and product teams	Product experiments + observability	From $450/launched experiment/month annually
Amplitude Experiment	Existing Amplitude users	Web and feature experimentation	Limited free access; paid plans
PostHog Experiments	Startups and product teams	Product experimentation and feature flags	Free allowance + usage pricing
Conductrics	Advanced experimentation teams	Client/server-side and adaptive testing	Custom
Convert Experiences	CRO teams and agencies	Web and full-stack experimentation	From $299/month annually
Dynamic Yield	Enterprise ecommerce teams	Experimentation and personalization	Custom
Webflow Optimize	Webflow marketing teams	Web experimentation and personalization	From $299/month
Kameleoon	Enterprise experimentation teams	Web, product, feature, and AI experimentation	From €495/month
Optimizely	Large experimentation programs	Web and feature experimentation	Free Rollouts option; paid plans custom
Crazy Egg	SMB marketing teams	Website A/B testing + behavioral analytics	A/B testing from $99/month
Unbounce	Paid acquisition teams	Landing-page experimentation	Included in Experiment plan
Firebase A/B Testing	Mobile-app teams	App and Remote Config experimentation	No separate A/B testing fee
DevCycle	Developer-led teams	Feature flags and experimentation	Free; Business $500/month annually

1. ABsmartly

Best for: Product and data teams running high-volume experimentation.

Key features:

• Client-side and server-side experimentation
• Sequential testing and advanced statistical controls
• Deep segmentation and experiment analysis
• Feature flags and experimentation program reporting

Pricing: Annual subscriptions are priced on request. ABsmartly also offers a paid 30-day Proof of Value.

Why we picked it: ABsmartly is built specifically for mature experimentation teams that need more statistical depth and control than a basic visual A/B testing tool.

ABsmartly is an experimentation platform aimed at sophisticated product, engineering, and data teams. The company was founded by people who previously built experimentation infrastructure at Booking.com, and that heritage still shows in its emphasis on experiment design, debugging, analysis, and running tests across the full product stack.

It works across web and apps, client-side and server-side, and provides considerably more control over experimentation than tools designed primarily for marketers editing webpages. ABsmartly has continued adding advanced experimentation features in 2026, including improved multi-variant analysis, group sequential testing, experiment audit histories, and an Impact Report for measuring the cumulative value of an experimentation program.

Another differentiator is the level of experimentation support. ABsmartly combines the software with specialist guidance, making it better suited to companies trying to build an experimentation capability rather than simply launch occasional landing-page tests.

2. Wingify

Best for: Marketing, CRO, product, and ecommerce teams that want experimentation, personalization, and behavioral analytics in one platform.

Key features:

• Web, mobile, server-side, and feature experimentation
• A/B, multivariate, and split-URL testing
• Behavioral analytics and personalization
• Feature management and AI-assisted optimization

Pricing: Available on request.

Why we picked it: Wingify now brings together two of the best-known names in experimentation: VWO and AB Tasty.

VWO and AB Tasty combined in 2026 under the Wingify brand, bringing their experimentation, personalization, and optimization capabilities into one platform.

Wingify combines experimentation with personalization, customer insights, feature management, behavioral analytics, ecommerce optimization, and customer engagement. For CRO teams, that means traditional web A/B testing, split URL and multivariate testing can sit alongside heatmaps, session recordings, funnels, surveys, and personalization.

The combined platform also makes Wingify relevant to a much wider group than traditional website optimizers. Product teams can work with feature experiments and rollouts, while ecommerce and marketing teams can use the behavioral and personalization layer to identify opportunities before testing them. Existing VWO and AB Tasty customers may still encounter those product names during the transition, but both now sit under the Wingify roadmap.

3. Adobe Target

Best for: Enterprise organizations already using the Adobe Experience Cloud.

Key features:

• A/B/n and multivariate testing
• Client-side and server-side experimentation
• Automated personalization
• Adobe Analytics and Experience Cloud integrations

Pricing: Available on request.

Why we picked it: Adobe Target remains one of the strongest enterprise choices when experimentation needs to work alongside Adobe’s broader analytics and personalization stack.

Adobe Target combines experimentation with targeting and personalization. Its guided workflow lets teams create variants, select audiences, define goals, and launch controlled experiments across websites, mobile apps, single-page applications, and other digital experiences.

Its biggest advantage remains the wider Adobe ecosystem. Segments and behavioral insights from Adobe Analytics and other Experience Cloud products can feed directly into Target, allowing sophisticated organizations to connect experimentation with customer data and personalization.

Target now supports A/B/n testing, multivariate testing, server-side APIs, mobile SDKs, and AI-powered optimization. It is consequently better suited to established enterprise programs than smaller businesses looking for a lightweight standalone testing tool.

4. Statsig

Best for: Product and engineering teams combining experimentation, feature flags, and product analytics.

Key features:

• A/B/n and multivariate experiments
• Bayesian and frequentist statistics
• Feature flags and configuration management
• Warehouse-native and no-code experimentation

Pricing: Free Developer plan; Pro starts at $150/month; Enterprise pricing is custom.

Why we picked it: Statsig remains one of the strongest product experimentation platforms despite a significant ownership change in 2026.

Statsig was built around helping product teams measure the impact of features rather than simply optimize webpages. Teams can combine feature flags, experiments, product analytics, session replay, and configuration management in the same platform.

In May 2026, Amplitude announced that it would take on the Statsig brand and customers. Importantly, Statsig has not simply disappeared: Amplitude says it will continue maintaining and developing the current Statsig cloud and warehouse-native platforms while building a more integrated roadmap.

For existing Statsig customers, the platform remains a viable experimentation option while Amplitude develops a more integrated roadmap across the two products.

5. Harness Feature Management & Experimentation

Best for: Engineering and product teams connecting feature releases directly to experimentation.

Key features:

• Feature flags and progressive rollouts
• Cloud and warehouse-native experimentation
• Sequential and fixed-horizon testing
• Experimentation on features, configs, and AI systems

Pricing: A free starting option is available; paid Feature Management & Experimentation pricing varies by plan and scale.

Why we picked it: Harness FME connects feature delivery, progressive rollouts, and experimentation in one platform, making it a strong fit for engineering-led teams.

Harness incorporated Split’s capabilities into its Feature Management & Experimentation platform. Teams can deploy functionality behind feature flags, expose it to selected users, measure its impact, and expand or reverse the rollout based on the results.

The experimentation layer has become substantially more sophisticated. Teams can turn an existing flag or configuration into an experiment, define guardrail metrics, use sequential or fixed-horizon statistical methods, and attribute changes in product metrics to the specific release a user encountered.

Harness also supports warehouse-native experimentation against Snowflake, Redshift, and BigQuery, making it particularly useful for companies whose authoritative business metrics already live in a data warehouse.

6. GrowthBook

Best for: Technical teams wanting open-source, warehouse-connected experimentation without traffic-based pricing.

Key features:

• Open-source feature flags and experimentation
• Bring-your-own-warehouse architecture
• Visual editor and server-side testing
• Advanced statistics including CUPED and sequential testing

Pricing: Free Starter plan; Pro costs $40 per seat/month; Enterprise is custom.

Why we picked it: GrowthBook offers an unusually flexible experimentation stack while letting companies retain control of their own data.

GrowthBook started as an open-source experimentation platform and has matured into a full feature flagging and experimentation system. It can connect directly to existing warehouse data rather than requiring companies to create another isolated analytics dataset.

That architecture makes GrowthBook particularly attractive to data and engineering teams. It can function as a complete experimentation platform, a feature management layer, or an analysis engine connected to an existing stack.

The free tier includes unlimited experiments, feature flags, and traffic for up to three users. The Pro tier adds features such as a visual editor, multi-armed bandits, advanced statistics, safe rollouts, and more sophisticated permissions without moving to traffic-based pricing. Growthbook

7. Datadog Experiments

Best for: Product and engineering teams that want experimentation connected directly to observability and warehouse metrics.

Key features:

• A/B and product experimentation
• Sequential, fixed-sample, and Bayesian analysis
• Warehouse-native business metrics
• Built-in performance and reliability guardrails

Pricing: Starts at $450 per launched experiment/month when billed annually, or $575 on demand.

Why we picked it: Datadog Experiments combines product experimentation with observability and warehouse data, helping teams measure both business outcomes and technical side effects.

Datadog acquired Eppo in 2025 and subsequently rebuilt its experimentation technology inside Datadog. In 2026, Eppo’s experimentation functionality became Datadog Experiments, while its feature-management capabilities became Datadog Feature Flags.

The key differentiator is the ability to evaluate product changes using several classes of data at once. A team can combine behavioral metrics, application-performance signals, and source-of-truth business metrics stored in its data warehouse rather than analyzing each in isolation.

That is particularly useful when experiments could improve a conversion metric while degrading latency, reliability, or another technical guardrail. Datadog can surface both sides of the tradeoff in the same experimentation workflow.

8. Amplitude Experiment

Best for: Teams already using Amplitude that want analytics and experimentation in the same platform.

Key features:

• Feature and web experimentation
• A/B/n and multi-armed bandit tests
• Feature flags and progressive rollouts
• Native targeting using Amplitude cohorts and metrics

Pricing: Limited experimentation is available on the free tier; expanded Feature Experiment and Web Experiment capabilities are available on paid plans.

Why we picked it: Amplitude removes much of the plumbing between product analytics and experimentation.

Amplitude Experiment lets teams run both code-controlled feature experiments and no-code web experiments. Product teams can use feature flags and SDKs to test changes across web, mobile, and backend environments, while marketing or growth teams can use the visual Web Experiment editor.

The major advantage is that experiment data already sits beside Amplitude’s behavioral analytics. Teams can target existing cohorts and evaluate results against funnels, retention, revenue, and other metrics without connecting a separate experimentation product to their analytics stack.

That makes Amplitude particularly compelling for businesses already using it as their main product analytics platform. Its 2026 relationship with Statsig also makes Amplitude an increasingly important player in experimentation infrastructure. Amplitude

9. PostHog Experiments

Best for: Startups and product teams that want experimentation inside an integrated product-engineering stack.

Key features:

• Product experiments
• Feature flags
• Product analytics and session replay
• Usage-based pricing with a generous free tier

Pricing: Free allowance available every month; paid usage is pay-as-you-go. Experiments are billed through feature-flag usage.

Why we picked it: PostHog combines experimentation with most of the other tools a product team needs to understand what happens after a release.

PostHog has expanded well beyond product analytics. Its platform now combines analytics, feature flags, experimentation, session replay, surveys, data warehousing, error tracking, and other product-development tools.

For experimentation, this means teams can release a change behind a feature flag and analyze its impact without sending experiment assignments and behavioral data between several different vendors. This is particularly attractive to engineering-led startups and smaller product teams that want fewer tools in their stack.

The pricing model also distinguishes PostHog from many enterprise experimentation platforms. Its free allowances reset every month, including one million feature-flag requests, with companies moving to pay-as-you-go pricing only when they exceed those limits.

10. Conductrics

Best for: Experienced experimentation teams that need flexible client-side, server-side, and adaptive optimization.

Key features:

• A/B and multivariate testing
• Client-side and server-side experimentation
• Machine-learning decisioning
• User intent and survey data

Pricing: Available on request.

Why we picked it: Conductrics remains one of the more flexible platforms for teams that have moved beyond straightforward webpage A/B testing.

Conductrics blends traditional experimentation with machine-learning-driven decisioning. You can use it for standard A/B and multivariate tests, but its real strength is applying experimentation and optimization across more complex digital experiences.

The platform works on both the client and server side and gives technical teams considerable control over how experiments interact with their systems. Conductrics also combines behavioral data with user-intent data, allowing teams to incorporate direct feedback into their optimization programs.

Conductrics makes more sense for organizations with established experimentation expertise than teams looking for a simple visual editor.

11. Convert Experiences

Best for: CRO teams and agencies wanting strong experimentation functionality without top-tier enterprise pricing.

Key features:

• A/B and split-URL testing
• Multivariate and multipage experiments
• Full-stack experiments and feature flags
• Advanced targeting, heatmaps, and session recordings

Pricing: Growth starts at $299/month when billed annually or $399 monthly. Pro starts at $420/month annually.

Why we picked it: Convert continues to provide a strong balance between advanced experimentation functionality and transparent pricing.

Convert Experiences has long been popular with CRO teams and agencies because it offers the features expected from substantially more expensive experimentation platforms without requiring a large enterprise contract.

Teams can run A/B and split-URL tests with the Growth tier, while Pro adds capabilities including multivariate testing, multipage testing, full-stack experimentation, feature flags, multi-armed bandits, and sequential testing.

Convert has also expanded its platform in 2026 with AI-assisted functionality, an MCP server, Shopify price testing, session recordings, and heatmaps. Its privacy and compliance features remain another reason it is frequently considered by organizations operating in regulated or privacy-conscious markets.

12. Dynamic Yield by Mastercard

Best for: Enterprise ecommerce and personalization teams running experiments across complex customer journeys.

Key features:

• Client-side and server-side experimentation
• Personalization and audience targeting
• Product recommendations
• Feature rollouts and experience APIs

Pricing: Available on request.

Why we picked it: Dynamic Yield is strongest when experimentation is part of a wider personalization and ecommerce optimization program.

Dynamic Yield is now firmly part of Mastercard’s personalization portfolio. It combines experimentation with recommendation systems, audience management, personalization, and experience optimization across multiple digital touchpoints.

A/B testing remains an important part of the platform, but Dynamic Yield is most valuable when tests need to work alongside personalization. Teams can compare experiences for specific audience groups, test content and recommendations, and use the results to refine how experiences are targeted.

Its Experience APIs also support server-side tests and controlled feature rollouts, making the platform useful beyond simple webpage changes. That breadth makes it more appropriate for enterprise ecommerce teams than companies that only need occasional A/B tests.

13. Webflow Optimize

Best for: Marketing teams running their websites in Webflow.

Key features:

• Native A/B testing
• Rules-based personalization
• AI Optimize
• Audience targeting and insights

Pricing: Starts at $299/month as a Webflow add-on, based on pageviews, with up to five concurrent optimizations. Enterprise pricing is custom.

Why we picked it: Webflow Optimize removes much of the technical friction of connecting an external testing platform to a Webflow site.

Webflow Optimize is the current form of the technology Webflow acquired with Intellimize. It is now integrated directly into Webflow rather than operating primarily as a separate predictive-personalization platform.

Teams can create A/B tests, define goals, target audiences, and edit page content or layouts without moving into another platform. Personalization can show different experiences according to attributes such as location, device, or referral source.

AI Optimize adds an adaptive layer by changing how traffic is distributed as the system learns which variants perform better. For companies already managing their marketing site in Webflow, that native integration is its biggest advantage over a standalone experimentation tool.

14. Kameleoon

Best for: Enterprise teams combining web experimentation, product experimentation, feature management, and AI.

Key features:

• Prompt-based experiment creation
• Web, server-side, and feature experimentation
• Sequential, Bayesian, and frequentist statistics
• Feature flags, rollouts, and advanced targeting

Pricing: PBX Starter begins at €495/month; Enterprise pricing is custom. A 30-day trial is available.

Why we picked it: Kameleoon has evolved substantially and now connects experimentation directly with AI-assisted experiment creation and feature delivery.

Kameleoon remains one of the few platforms covering web experimentation, server-side product experimentation, feature management, personalization, and mobile experimentation within one system.

Its biggest recent change is Prompt-Based Experimentation (PBX). Instead of constructing every test manually through a visual editor, teams can describe a proposed change in natural language and use AI to generate the variation. Human approval is still required before the experiment launches.

For advanced programs, Kameleoon adds features such as multi-armed and contextual bandits, CUPED, holdout groups, multiple-testing correction, warehouse integrations, and progressive feature rollouts. This breadth makes it particularly relevant to organizations trying to bring marketing, product, engineering, and data teams onto the same experimentation infrastructure.

15. Optimizely

Best for: Large organizations running mature web and product experimentation programs.

Key features:

• Web and full-stack experimentation
• Feature flags and progressive rollouts
• Visual and server-side testing
• AI-assisted experimentation workflows

Pricing: Optimizely Rollouts is free and allows one experiment at a time. Full Web Experimentation and Feature Experimentation pricing is available on request.

Why we picked it: Optimizely remains one of the most established enterprise experimentation platforms and continues to support both marketer-led and engineering-led testing.

Optimizely has expanded beyond the web-testing platform many CRO teams originally knew. Its current experimentation offering covers fast visual website experiments as well as server-side and feature experimentation across digital products.

Web Experimentation is better suited to marketing and growth teams that want to create experiences quickly, while Feature Experimentation lets engineering and product teams validate product changes and control releases through feature flags.

Optimizely has also added AI deeper into the experimentation workflow, while still retaining the enterprise controls required for teams running large numbers of concurrent tests. Smaller teams can begin with the free Rollouts product before moving into the full paid platform.

16. Crazy Egg

Best for: Small and mid-sized marketing teams that want user-behavior research and simple website A/B testing in one tool.

Key features:

• Website A/B testing
• Heatmaps
• Session recordings
• Funnels, surveys, and conversion tracking

Pricing: Starter is $29/month, but A/B testing begins with the $99/month Plus plan. All listed plans are billed annually and include a 30-day trial.

Why we picked it: Crazy Egg remains one of the easiest ways for smaller teams to connect qualitative behavior data with straightforward website experiments.

Crazy Egg is better known for heatmaps and session recordings than advanced experimentation infrastructure. That is also its main advantage for the right user.

A marketer can identify where visitors click, where they stop scrolling, or where a funnel leaks, create a hypothesis, and then run an A/B test without moving the entire workflow into another product.

It will not replace platforms such as Optimizely, ABsmartly, or Statsig for complex experimentation programs, but it does not try to.

17. Unbounce

Best for: Paid acquisition and marketing teams testing landing pages without relying heavily on developers.

Key features:

• Unlimited landing-page A/B testing on the Experiment plan
• Drag-and-drop landing-page builder
• Conversion reporting
• Popups and sticky bars

Pricing: A/B testing is included in the Experiment plan; current pricing depends on plan and traffic allowance.

Why we picked it: Unbounce is still a strong specialist option when the thing you need to test is the landing page itself.

Unbounce differs from broader experimentation platforms because landing-page creation is the core product. Marketers can build a page, duplicate it into a variant, change the offer, copy, layout, imagery, or form, and split incoming traffic between versions.

That is particularly useful for paid search and paid social campaigns where teams need to produce and test dedicated acquisition pages quickly without waiting for engineering support.

Unbounce is not the right choice for full-stack product experimentation or sophisticated feature flags. For landing-page experimentation, however, its narrower focus can make it considerably simpler than deploying a large enterprise testing platform.

18. Firebase A/B Testing

Best for: Mobile-app teams already building with Firebase.

Key features:

• App UI and feature experiments
• Remote Config experimentation
• Notification and engagement experiments
• Native retention, revenue, and engagement metrics

Why we picked it: Firebase A/B Testing is a strong option for mobile teams that want to run app experiments using Remote Config and native Firebase metrics.

Why we picked it: Firebase fills the mobile-app experimentation gap left by removing Apptimize from the list.

Firebase A/B Testing is designed specifically for product and marketing experiments inside apps. Teams can test UI changes, product features, AI-powered functionality, Remote Config values, and engagement campaigns before exposing them to the full user base.

Firebase also measures common app outcomes such as retention, engagement, and revenue out of the box. When combined with Google Analytics, teams can evaluate additional user actions for each experiment group.

It is not a general website CRO platform, but that is precisely why it belongs here: teams building Android, iOS, C++, or Unity products need different experimentation infrastructure from marketers testing a homepage headline.

19. DevCycle

Best for: Developer-led teams wanting lightweight feature management and experimentation built around open standards.

Key features:

• Feature flags and A/B experimentation
• Percentage and targeted rollouts
• OpenFeature support
• Unlimited seats and flags

Pricing: Free plan includes A/B testing and up to 1,000 client-side MAUs. Business costs $500/month when billed annually; Enterprise pricing is custom.

Why we picked it: DevCycle is a strong modern alternative for engineering teams that want experimentation without separating it from feature delivery.

DevCycle combines feature flags, targeting, rollouts, and A/B testing in a developer-focused platform. Teams can gradually expose new functionality, create controlled experiment groups, and use custom events to evaluate the resulting impact.

A major differentiator is its commitment to OpenFeature, an open standard for feature flagging. That can reduce dependence on proprietary SDKs and make feature-management infrastructure easier to move or integrate with an existing engineering stack.

DevCycle is now part of Dynatrace, but the DevCycle product and pricing remain active. Its free plan includes experimentation, unlimited seats, and unlimited feature flags, making it a particularly practical option for smaller engineering teams before they need enterprise governance

FAQs about A/B testing tools

What is an A/B testing tool?

An A/B testing tool splits users between two or more versions of an experience and measures whether the change affects a defined outcome, such as conversion rate, revenue, activation, retention, or engagement.

Modern experimentation platforms may also include feature flags, server-side testing, personalization, product analytics, and automated rollouts.

What is the best A/B testing tool?

The right A/B testing tool depends on what you need to test. Wingify, Convert, and Optimizely are strong options for web experimentation, while ABsmartly, Statsig, GrowthBook, Amplitude, and PostHog are better suited to product experimentation.

For specialist use cases, Unbounce focuses on landing pages and Firebase A/B Testing is designed for mobile apps.

What replaced Google Optimize?

There is no single direct replacement for Google Optimize. Google discontinued Optimize and Optimize 360 on September 30, 2023 and shifted toward integrations with third-party experimentation platforms.

Teams looking for website experimentation can consider tools such as Wingify, Convert, Optimizely, Webflow Optimize, or Crazy Egg, depending on their budget and technical requirements.

What is the difference between client-side and server-side A/B testing?

Client-side testing changes an experience after it reaches the user’s browser, making it useful for relatively quick changes to copy, layouts, CTAs, and other front-end elements.

Server-side testing assigns variants before the experience reaches the browser. It gives product and engineering teams more control over features, algorithms, pricing logic, backend systems, and other changes that cannot be reliably tested through a visual editor.

Do you need feature flags for A/B testing?

No. Feature flags are most useful when experimentation is tied to software releases or product features.

Platforms such as Statsig, Harness, GrowthBook, Amplitude, PostHog, and DevCycle combine feature flags with experimentation so teams can expose a new feature to part of their audience, measure its impact, and then roll it out or disable it without another deployment. Harness, for example, can turn an existing feature flag directly into an experiment.

How much traffic do you need for A/B testing?

There is no universal minimum traffic requirement. The sample size you need depends on your baseline conversion rate, expected effect size, statistical power, significance threshold, and how evenly traffic is divided between variants.

Low-traffic sites may struggle to detect small improvements reliably, which is why sample-size planning should happen before launching a test rather than deciding when to stop based on early results.

Are free A/B testing tools good enough?

They can be. GrowthBook, Statsig, PostHog, DevCycle, and several other platforms provide free experimentation capabilities that can be sufficient for smaller teams.

Paid plans become more important when you need higher usage limits, enterprise governance, advanced statistics, personalization, multiple concurrent experiments, dedicated support, or complex server-side experimentation.

Build stronger A/B testing and experimentation skills

The tool only handles the infrastructure. Successful experimentation still depends on choosing worthwhile hypotheses, designing valid tests, interpreting results correctly, and turning what you learn into better decisions.

CXL has three relevant courses for building those skills:

• A/B Testing Foundations: Learn test prioritization, statistical validity, and the fundamentals of running meaningful experiments.
• A/B Testing Mastery: Go deeper into hypotheses, KPIs, test setup, analysis, QA, and client-side versus server-side experimentation.
• Advanced Experimentation Masterclass: Learn how to build roadmaps, scale testing programs, improve test velocity, and develop a wider experimentation culture.