The Complete Guide to Using Convert.com: How to A/B Test and Optimize Every Stage of Your Customer Journey

Introduction

The promise of A/B testing is simple: stop guessing, start knowing. But the reality practitioners face in 2025 is anything but simple. The customer journey has fractured across dozens of touchpoints — from the first ad impression to the post-purchase email sequence — and optimizing that journey requires more than swapping a headline color and checking a dashboard three days later.

If you've spent any time in the conversion rate optimization (CRO) space recently, you know the landscape has shifted dramatically. Google Optimize, the free tool that democratized A/B testing for small teams and solo founders, shut down in September 2023. That left a vacuum. Practitioners scrambled to find replacements, and the market responded with a flood of options — some enterprise-grade, some bootstrapped, and some purpose-built for very specific niches. The challenge isn't finding a tool anymore; it's finding the right tool and then actually using it well enough to move the metrics that matter.

This guide is about Convert.com specifically — not as a sales pitch, but as a practitioner-level walkthrough. Convert occupies an interesting position in the market: it's built for teams that need enterprise-grade experimentation capabilities (server-side testing, full-stack SDKs, advanced statistical controls) but don't necessarily have enterprise budgets or the patience for enterprise onboarding timelines. It's particularly strong for agencies managing multiple client accounts and for privacy-conscious organizations that need GDPR compliance baked in rather than bolted on.

What follows is a comprehensive, stage-by-stage guide to using Convert.com to optimize your entire customer journey — from awareness through acquisition, activation, retention, and revenue. We'll cover the practical setup, the strategic thinking that separates "button-pushers" from experimentation leads, and the real tradeoffs you'll encounter along the way. Whether you're a founder running your first split test or a seasoned CRO professional managing a portfolio of client experiments, this guide will give you a structured framework for getting real value out of the platform.

Let's start with where Convert fits in the broader landscape, because context matters before configuration.

Overview

The A/B Testing Landscape: Where Convert.com Fits

Before diving into the how-to, it's worth understanding why Convert.com exists in its current form and who it's actually built for. The A/B testing market in 2025 is not a monolith — it's a spectrum ranging from free, lightweight tools to six-figure enterprise contracts.

The pain point that drives most practitioners to evaluate tools is real and immediate:

Bogdan's experience captures a frustration shared by thousands. You want to test one thing — a headline, a CTA button, a pricing page layout — and suddenly you're staring at $200–$300/month invoices. For solo founders and early-stage startups, that math doesn't work. And with Google Optimize gone, the free tier of the market has largely evaporated.

Convert.com doesn't pretend to be the cheapest option. Its pricing starts at the professional tier, and it's transparent about targeting teams that run experimentation programs, not one-off tests. But what you get for that investment is meaningfully different from what lighter tools offer — and understanding that difference is critical to deciding whether Convert is the right fit for your situation.

Convert itself has been vocal about helping practitioners navigate the crowded landscape:

This is a genuinely useful framing. The tool you choose should be dictated by your technical architecture, your team's skill distribution (marketing-led vs. engineering-led), and your compliance requirements. Convert positions itself as "best for privacy-first, enterprise testing with strong APIs"^[7] — and that positioning is accurate based on what the platform actually delivers.

Who Should Use Convert (And Who Shouldn't)

Let's be direct. Convert.com is an excellent fit if you:

• Run an agency or consultancy managing multiple client optimization programs
• Need GDPR/CCPA compliance without third-party cookie dependencies
• Want both visual editor and full-stack capabilities in one platform
• Require statistical rigor with configurable confidence levels and sequential testing
• Operate at moderate-to-high traffic volumes (tens of thousands of monthly visitors minimum)

Convert is probably not the right choice if you:

• Are testing one landing page once a quarter (you'll overpay for capabilities you don't use)
• Need a completely free tool (look at open-source options like GrowthBook or PostHog)
• Want a tool that's primarily a feature flagging system (LaunchDarkly or Split are better fits)

With that context established, let's get into the actual implementation.

Setting Up Convert.com: The Foundation

Account Structure and Project Organization

The first thing you'll encounter when setting up Convert is its project-based architecture. This isn't just an organizational nicety — it's fundamental to how the platform scales, especially for agencies.

Each "project" in Convert corresponds to a distinct website or client. Within each project, you create individual experiments (A/B tests, split URL tests, multivariate tests, or personalization campaigns). This hierarchy matters because permissions, goals, and targeting rules are scoped to the project level^[6].

The import/export capability Convert describes here is genuinely powerful for anyone running optimization across multiple properties. If you've built a proven tracking setup — say, a set of revenue goals, scroll depth triggers, and audience segments — for one client, you can export that configuration and import it into a new project in seconds. This eliminates the most tedious part of scaling an experimentation practice: the repetitive setup work.

Practical setup steps:

1. Create your account and set up your first project at app.convert.com
2. Install the tracking snippet — Convert provides a JavaScript snippet that goes in your site's tag. For single-page applications (SPAs), you'll want to use their JavaScript SDK^[5] instead, which gives you programmatic control over when experiments fire
3. Configure your project settings — set your default statistical significance threshold (Convert defaults to 95%, but you can adjust this per experiment), define your primary domain, and set up any cross-domain tracking if needed
4. Set up team permissions — if you're an agency, assign team members to specific projects so they only see what's relevant to them

The snippet installation deserves special attention. Convert's tracking code is lightweight compared to some competitors, but any client-side script adds load time. For performance-sensitive sites, Convert offers server-side testing through their full-stack SDK^[5], which eliminates the "flicker" problem entirely — that brief flash of original content before the variant loads that plagues many client-side testing tools.

Defining Goals That Actually Matter

This is where most experimentation programs go wrong, and it's worth spending real time on. Convert allows you to set up multiple goal types[^7]:

• Revenue goals — track actual transaction values
• Click goals — measure clicks on specific elements
• Page visit goals — track when users reach specific URLs (like a thank-you page)
• Scroll depth goals — measure engagement depth
• Custom JavaScript goals — fire based on any event you define in code

The critical mistake practitioners make is optimizing for a single, surface-level metric without tracking downstream effects. This is exactly the trap that one widely-discussed case study illustrates:

This is the most important cautionary tale in CRO. A conversion rate that doubles means nothing if the customers you're acquiring are worth a third of what they used to be. Convert's multi-goal tracking is specifically designed to prevent this. For every experiment, you should set:

1. A primary goal — the metric you're directly trying to improve (e.g., add-to-cart rate)
2. Secondary goals — downstream metrics that validate the quality of the primary win (e.g., purchase completion rate, average order value, return rate)
3. Guardrail metrics — things that should not get worse (e.g., bounce rate on subsequent pages, customer support ticket volume)

In Convert's experiment setup, you can add multiple goals and designate one as primary. The platform will calculate statistical significance for each goal independently, giving you a complete picture of how your variant affects the entire funnel — not just the one number you're hoping to move^[8].

Mapping the Customer Journey to Experiments

Convert's blog provides a useful framework for thinking about the customer journey in the context of experimentation^[1]. The journey breaks down into stages, and each stage has distinct optimization opportunities:

Stage 1: Awareness — Landing Page Optimization

At the awareness stage, visitors are arriving at your site for the first time. They're evaluating whether you're relevant to their problem. The key metrics here are bounce rate, time on page, and scroll depth.

What to test in Convert:

• Headlines and value propositions — Use Convert's visual editor to create variants with different headline copy. The visual editor works by overlaying changes on your existing page, so you don't need to create separate pages or modify your codebase^[6]
• Hero images and social proof placement — Test whether customer logos, testimonial quotes, or usage statistics above the fold reduce bounce rate
• Page layout and information hierarchy — Use multivariate testing (MVT) to test multiple elements simultaneously if you have sufficient traffic

Setting up a landing page test:

1. In your Convert project, click "New Experience" and select "A/B Test"
2. Enter the URL of the page you want to test
3. The visual editor will load your page — click on any element to modify text, styling, visibility, or position
4. Create your variant(s) — Convert supports multiple variants per test, not just A/B
5. Set your targeting rules (URL targeting, audience conditions, device type)
6. Assign your goals and set traffic allocation
7. Use the QA Wizard to preview your variants before going live^[8]

Pro tip: Convert's audience targeting lets you segment by traffic source, so you can run different experiments for paid vs. organic visitors. This is crucial because these audiences have fundamentally different intent levels.

Stage 2: Consideration — Product and Pricing Page Optimization

Once visitors move past the landing page, they're in consideration mode. They're comparing you to alternatives, evaluating features, and looking at pricing. This is where A/B testing gets strategically interesting.

What to test in Convert:

• Pricing page structure — Test different plan presentations (3-column vs. slider, monthly vs. annual default, feature comparison tables)
• Product page elements — For e-commerce, test product image galleries, description formats, review display, and cross-sell placements
• Trust signals — Security badges, money-back guarantees, and social proof elements

According to Convert's own A/B testing guide, the consideration stage is where you should focus on reducing friction and building confidence^[1]. The platform's split URL testing feature is particularly useful here — instead of modifying elements on a single page, you can test entirely different page designs by routing traffic between two distinct URLs^[7].

Setting up a split URL test:

1. Create a "Split URL" experience in Convert
2. Define your original URL and your variant URL(s)
3. Convert will distribute traffic between the URLs based on your allocation settings
4. All goal tracking works identically to standard A/B tests

This approach is ideal for testing radically different page designs where the visual editor's overlay approach would be too limiting.

Stage 3: Acquisition — Checkout and Signup Flow Optimization

The acquisition stage is where money changes hands (or where users commit by creating an account). This is typically the highest-leverage area for experimentation because small improvements in conversion rate translate directly to revenue.

What to test in Convert:

• Form fields — Test reducing the number of required fields, changing field order, or adding inline validation
• Checkout flow — Single-page vs. multi-step checkout, guest checkout prominence, payment method display
• CTAs — Button copy, color, size, and placement (yes, these "basic" tests still produce wins regularly)

Ash's point about rejecting "winners" and publishing "losers" is critical. Not every statistically significant result should be implemented. If a variant wins on your primary metric but degrades a secondary metric, you need to make a judgment call. Convert's multi-goal reporting makes these tradeoffs visible, but the decision is still yours.

Using Convert's sequential testing for checkout optimization:

Sequential testing (also called "always valid" testing) is one of Convert's most valuable features for high-stakes experiments^[7]. Traditional A/B testing requires you to wait until you've collected a predetermined sample size before looking at results. Sequential testing allows you to monitor results continuously and stop the test as soon as you have a statistically valid winner — without inflating your false positive rate.

This is particularly valuable for checkout tests because:

• Checkout pages often have lower traffic than top-of-funnel pages, so tests take longer
• A losing variant on a checkout page directly costs you revenue every day it runs
• Sequential testing lets you stop losers faster, limiting the financial damage

To enable sequential testing in Convert, select it as your statistical method when configuring the experiment. Convert will display a "sequential confidence" metric alongside the traditional fixed-horizon confidence^[7].

Stage 4: Activation — Onboarding and First-Use Optimization

For SaaS products and subscription businesses, getting a user to sign up is only half the battle. Activation — getting them to experience the product's core value — is what determines whether they stick around.

What to test in Convert:

• Onboarding flows — Test different onboarding sequences, tooltip placements, and progress indicators
• Welcome emails — Use Convert's full-stack SDK to run server-side experiments on email content and timing
• Feature discovery — Test which features you highlight first for new users

This is where Convert's full-stack capabilities become essential. Client-side testing (the visual editor) works great for web pages, but onboarding often involves server-side logic — email triggers, in-app messaging, feature gating. Convert's JavaScript SDK^[5] and server-side SDKs let you run experiments in your backend code, making decisions about what each user sees at the server level before the page even renders.

Setting up a server-side experiment:

1. Install the Convert JavaScript SDK via npm: npm install @convertcom/js-sdk^[5]
2. Initialize the SDK with your project credentials
3. Use the runExperiment method to bucket users into variants
4. Implement your variant logic in code
5. Track conversions by firing goal events through the SDK

This approach gives you zero flicker (because the variant is determined server-side), full control over the experiment logic, and the ability to test things that aren't visible on a web page — like algorithm changes, pricing logic, or email sequences.

Stage 5: Retention — Keeping Customers Coming Back

Retention optimization is the most underappreciated stage of the customer journey, and it's where the real money is. Acquiring a new customer costs 5–25x more than retaining an existing one, yet most experimentation programs focus almost exclusively on acquisition.

Convert's emphasis on hypothesis rigor is directly relevant to retention testing. Retention experiments are harder to design because the feedback loops are longer — you might not know if a change improved retention for 30, 60, or 90 days. This makes the hypothesis structure even more critical. You need to know exactly what you're measuring, why you expect the change to work, and how long you'll wait before calling the result.

What to test in Convert for retention:

• Re-engagement campaigns — Use server-side testing to experiment with email cadence, content, and offers for churning users
• Loyalty and reward mechanics — Test different loyalty program structures, point systems, or exclusive content access
• Product experience improvements — Test UI changes that reduce friction for returning users (e.g., saved preferences, personalized dashboards)
• Cancellation flows — Test different retention offers, pause options, or feedback collection during the cancellation process

Using Convert's personalization features:

Beyond A/B testing, Convert offers personalization campaigns that let you deliver targeted experiences to specific audience segments without running a formal experiment^[7]. For retention, this means you can:

• Show returning visitors different homepage content than first-time visitors
• Display personalized product recommendations based on purchase history
• Trigger specific messaging for users who haven't visited in 30+ days

Personalization campaigns in Convert use the same targeting engine as experiments, so you can define audiences based on cookies, URL parameters, custom JavaScript conditions, or data layer variables.

Stage 6: Revenue — Maximizing Customer Lifetime Value

The final stage is about expanding revenue from existing customers — upsells, cross-sells, plan upgrades, and increasing average order value (AOV).

Oliver's point about "182 proven optimizations" reflects a common approach in the CRO community: building a library of test ideas based on accumulated wins. Convert supports this workflow through its import/export functionality — you can build a library of proven experiment configurations and deploy them across new projects quickly.

What to test in Convert for revenue expansion:

• Upsell placement and timing — Test when and where you present upgrade offers
• Bundle and cross-sell strategies — Test product bundle configurations on cart and checkout pages
• Pricing experiments — Use split URL tests to evaluate different pricing structures (this requires careful ethical consideration and often legal review)
• Post-purchase flows — Test order confirmation page content, including cross-sell offers and referral program prompts

Advanced Convert.com Features for Mature Programs

Once you've mastered the basics, Convert offers several advanced capabilities that separate it from lighter tools:

Feature Flags and Gradual Rollouts

Convert supports feature flags — the ability to toggle features on or off for specific user segments without deploying new code^[10]. This is powerful for:

• Gradual rollouts — Release a new feature to 10% of users, monitor for issues, then gradually increase to 100%
• Kill switches — Instantly disable a feature if it causes problems in production
• Targeted releases — Enable features only for specific user segments (e.g., enterprise customers, beta testers)

Feature flags blur the line between experimentation and deployment. In Convert, you can set up a feature flag that starts as a simple on/off toggle and later convert it into a full A/B test with statistical analysis^[10].

Advanced Audience Targeting

Convert's targeting engine supports complex audience definitions[^7]:

• Behavioral targeting — Based on pages visited, time on site, or number of sessions
• Technical targeting — Based on device type, browser, screen resolution, or connection speed
• Geographic targeting — Based on country, region, or city
• Custom targeting — Based on data layer variables, JavaScript conditions, or cookie values

This granularity lets you run experiments that are relevant to specific user segments rather than blasting the same test to all traffic. For example, you might test a simplified checkout flow only for mobile users, or test a different pricing page only for visitors from a specific ad campaign.

Integration Ecosystem

Convert integrates with the tools most experimentation teams already use[^7]:

• Analytics: Google Analytics 4, Adobe Analytics, Heap, Mixpanel, Amplitude
• Heatmaps: Hotjar, Crazy Egg, FullStory, Lucky Orange
• Tag management: Google Tag Manager, Tealium
• E-commerce: Shopify, WooCommerce, Magento
• CDPs and data warehouses: Segment, BigQuery

These integrations matter because A/B testing doesn't exist in isolation. You need to correlate experiment data with your broader analytics, watch session recordings of users in different variants, and push experiment data into your data warehouse for deeper analysis.

For Shopify stores specifically, Convert provides a direct integration that makes it straightforward to test product pages, collection pages, cart pages, and checkout flows^[3]. The Shopify integration handles the technical complexity of testing on a platform that has its own templating system and CDN caching.

Building a Hypothesis-Driven Testing Program

Tools are only as good as the thinking behind them. The biggest differentiator between teams that get ROI from Convert and teams that don't isn't technical skill — it's strategic discipline.

Bruno's point is blunt and correct: more traffic won't fix a store that doesn't convert. But the inverse is also true — more tests won't fix a program that doesn't have a coherent strategy.

Convert's approach to hypothesis building, as outlined in their framework, requires five components[^12]:

1. Observation — What data (quantitative or qualitative) suggests there's a problem?
2. Execution — What specific change will you make, to what element, for which audience?
3. Outcome — What metric do you expect to move, and in which direction?
4. Logistics — How long will the test run, what sample size do you need, and what significance level are you targeting?
5. Inadvertent Impact — What could go wrong? What negative effects might this change have on other metrics or user segments?

This framework prevents what Convert calls "vibe testing" — running experiments based on gut feelings rather than data-backed observations. In practice, this means every experiment you set up in Convert should have a documented hypothesis before you touch the visual editor.

Real-World Results: What Convert Delivers in Practice

Case studies provide useful calibration for what's actually achievable. Convert has published several that illustrate the platform's impact:

Hivelocity & Cro Metrics: This case study documents a 10x ROI journey where a hosting company used Convert to systematically optimize their customer acquisition funnel. The key insight was that structured experimentation — not random testing — drove the compounding gains^[2].

Conversion Rate Experts & Earth Class Mail: CRE used Convert to run a comprehensive optimization program that involved testing across multiple stages of the customer journey simultaneously. The multi-goal tracking was essential for ensuring that wins at one stage didn't create losses downstream^[13].

Conversion Rate Experts & Smart Insights: This case study documents a 157% growth in conversions achieved through a disciplined testing program on Convert. The results came not from a single "big win" test but from a series of incremental improvements that compounded over time^[14].

The pattern across these case studies is consistent: the teams that get the best results from Convert are the ones that treat experimentation as an ongoing program, not a one-time project. They build hypothesis backlogs, prioritize ruthlessly, and track both primary and secondary metrics for every test.

The Experimentation Community and Ecosystem

One aspect of Convert that doesn't show up in feature comparisons but matters in practice is the community and ecosystem around the platform.

The CRO community — events like Dialogue Donderdag, communities like the Experimentation Hub, and the growing ecosystem of specialized tools — provides the intellectual infrastructure that makes any A/B testing tool more valuable. Convert has been intentional about supporting this ecosystem, from their ambassador program to their integration with community-built tools.

This ecosystem approach matters because A/B testing is not a solo activity. The best experimentation programs involve collaboration between marketers, developers, designers, data analysts, and product managers. Having a tool that integrates with the broader workflow — from hypothesis generation to experiment execution to analysis and knowledge management — is what separates productive programs from ones that stall after the first few tests.

Common Mistakes and How to Avoid Them

After covering the capabilities, let's address the pitfalls. These are the mistakes that waste the most time and money in Convert (or any A/B testing platform):

1. Peeking at results too early

This is the single most common statistical error in A/B testing. If you check your results daily and stop the test as soon as you see a "winner," you'll have a false positive rate far higher than your configured significance level. Convert's sequential testing mitigates this, but only if you actually use it. If you're using fixed-horizon testing, set your sample size upfront and don't stop early^[4].

2. Testing too many things at once with insufficient traffic

Multivariate testing is powerful but requires exponentially more traffic than simple A/B tests. If you have 10,000 monthly visitors, stick to A/B tests with 2–3 variants maximum. Save MVT for pages with 100,000+ monthly visitors.

3. Not QA-ing variants across devices and browsers

Convert's QA Wizard lets you preview variants before they go live^[8]. Use it. Every time. A variant that looks perfect on your MacBook might be completely broken on an Android phone with a small screen. Test across devices, browsers, and screen sizes before launching.

4. Ignoring the "why" behind results

A test result tells you what happened, not why. Integrate Convert with a session recording tool (Hotjar, FullStory) so you can watch how users actually interact with your variants. The quantitative data tells you the variant won; the qualitative data tells you why it won — and that insight is what makes your next test smarter.

5. Running tests without sufficient statistical power

Before launching any experiment, use a sample size calculator to determine how long you need to run the test. Convert provides guidance on this in their setup flow, but the responsibility is yours. Running a test for three days on a page with 500 daily visitors will not give you reliable results, regardless of what the dashboard says^[4].

The appeal of pre-built test libraries (like Connor's "top 50 winning CRO tests") is understandable — everyone wants shortcuts. But the most valuable thing about these resources isn't the specific tests; it's the pattern recognition. Use them as inspiration for hypotheses, not as a substitute for understanding your own users and data.

Oliver's "47 battle-tested A/B test breakdowns" similarly serve as a starting point, not a destination. The tests that worked for Shopify stores selling luxury goods may not work for your SaaS pricing page. Always validate with your own data.

Privacy and Compliance: Convert's Differentiator

One area where Convert genuinely stands apart from many competitors is privacy compliance. In a post-GDPR, post-CCPA world, how your testing tool handles user data matters — both legally and ethically.

Convert operates without third-party cookies by default^[7]. This is significant because:

• Many A/B testing tools rely on third-party cookies for user identification, which are being phased out by browsers
• GDPR and CCPA impose strict requirements on how user data is collected, stored, and processed
• Convert stores data in EU-based servers and provides data processing agreements (DPAs) out of the box

For organizations in regulated industries (healthcare, finance, education) or those with European customers, this isn't a nice-to-have — it's a requirement. Convert's privacy-first architecture means you can run experiments without adding another item to your legal team's compliance checklist.

Pricing and ROI Considerations

Let's address the elephant in the room: Convert isn't cheap. Plans start in the hundreds of dollars per month, which is a real investment for small teams.

The ROI calculation comes down to traffic volume and conversion value. If you're running an e-commerce store with $100,000/month in revenue and a 2% conversion rate, a test that improves conversion by just 0.2 percentage points (to 2.2%) adds $10,000/month in revenue. Convert pays for itself in the first week.

If you're a blog with no direct monetization and 5,000 monthly visitors, Convert is almost certainly overkill. The math doesn't work.

For agencies, the calculation is different. Convert's multi-project architecture means you're paying one subscription to manage all your clients. If you're managing 10 client optimization programs, the per-client cost is a fraction of what you'd pay for individual tool subscriptions — and the operational efficiency of the import/export system and centralized management compounds that value.

Nico's TestIt represents the other end of the market — affordable, simple tools for makers who need basic A/B testing without the enterprise feature set. There's nothing wrong with starting there. But if your experimentation program matures to the point where you need advanced statistical controls, server-side testing, multi-project management, and enterprise integrations, you'll eventually outgrow lightweight tools and need something like Convert.

Conclusion

Optimizing a customer journey isn't a single test or a single tool — it's a discipline. Convert.com provides the infrastructure for that discipline: a visual editor for quick front-end tests, a full-stack SDK for server-side experimentation, sequential testing for faster decisions, multi-goal tracking for holistic measurement, and a project architecture that scales from one site to dozens.

But the tool is only as good as the thinking behind it. The practitioners getting real results from Convert are the ones who start with data-backed hypotheses, track both primary and guardrail metrics, resist the urge to peek at results prematurely, and treat every test — win or lose — as a learning opportunity that informs the next experiment.

The customer journey optimization playbook on Convert looks like this:

1. Map your journey stages and identify the highest-leverage optimization opportunities at each stage
2. Set up multi-goal tracking so you never optimize one metric at the expense of another
3. Build hypothesis backlogs grounded in quantitative and qualitative data, not gut feelings
4. Use the right test type for each situation — visual editor for simple changes, split URL for radical redesigns, full-stack SDK for server-side logic
5. Leverage sequential testing for high-stakes experiments where you need to stop losers fast
6. Integrate with your analytics and session recording tools so you understand the "why" behind every result
7. Document and share learnings across your team or client portfolio using Convert's import/export capabilities

The teams that win at experimentation aren't the ones with the most sophisticated tools — they're the ones with the most disciplined processes. Convert gives you the infrastructure. The discipline is up to you.

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Sources

^[1] What Is A/B Testing and What Can You Test? - Convert — https://www.convert.com/blog/a-b-testing/ab-testing-guide

^[2] Convert Case Study: Hivelocity & Cro Metrics' 10x ROI Journey — https://www.convert.com/case-studies/accelerate-experiment-roi

^[3] How to Run A/B Tests on Your Shopify Store — https://www.brillmark.com/how-to-run-a-b-tests-on-your-shopify-store-tools-process-common-mistakes

^[4] A/B Testing Guide: The Proven 6-Step Process for Higher Conversions — https://conversionsciences.com/ab-testing-guide

^[5] Convert.com JavaScript SDK — https://github.com/convertcom/javascript-sdk

^[6] Getting Started - Help Center - Convert Experiences — https://support.convert.com/hc/en-us/articles/getting-started

^[7] CRO Tool Must-Haves - Convert Experience Features — https://www.convert.com/features

^[8] How to Properly Set Up a Convert.com Experiment (Without Losing ...) — https://testlab.gg/blog/how-to-properly-set-up-a-convert-experiment

^[9] What's new - March 2024 - Help Center — https://support.convert.com/hc/en-us/articles/25459014382989-what-s-new-march-2024

^[10] Feature Flags and Rollouts: The Complete Experimenter's Guide — https://www.convert.com/blog/full-stack-experimentation/what-are-feature-flags-rollouts

^[11] Convert.com Case Studies: AB Testing Ideas & Results — https://www.convert.com/case-studies

^[12] Conversion Rate Optimization Guide for Marketers in 2025 — https://www.convert.com/conversion-rate-optimization

^[13] Convert Case Study: CRE and Earth Class Mail — https://www.convert.com/case-studies/conversion-rate-experts

^[14] Convert Case Study: CRE and Smart Insights' 157% Growth — https://www.convert.com/case-studies/convert-case-study-cre-smart-insights

Further Reading

• [The Ultimate 2026 A/B Testing Buyer's Guide: Optimizely vs. Convert.com vs. VWO vs. Adobe Target — Features, Pricing, and Who Each Platform Is Really Built For](/buyers-guide/the-ultimate-2026-ab-testing-buyers-guide-optimizely-vs-convertcom-vs-vwo-vs-adobe-target-features-p) — An in-depth look at The Ultimate 2026 A/B Testing Suite Buyer's Guide: Optimizely vs. Convert.com vs. VWO vs. Adobe Target