ConvertLab vs Optimisely vs Google Optimise for Shopify today

ConvertLab vs Optimisely vs Google Optimise: Which Is Best for Shopify? If you run a Shopify store and want to increase conversions on product pages, you are in the right place. This head-to-head comparison explains how each platform handles experimentation on Shopify, where the technical limitations lie, and which tool is the best fit depending on your traffic, team and goals.

Quick verdict: which tool to choose

If you want a short answer before the deep dive: for most Shopify merchants ConvertLab is the best fit. It is purpose-built for Shopify product pages, requires no code, integrates with Shopify metrics, and is priced for stores rather than enterprises. Optimisely is better for large organisations that need feature flags, full-stack experimentation and advanced targeting and have engineering resources to support it. Google Optimise was a useful free option but it has been discontinued and is no longer a reliable choice.

This article compares the three platforms on the criteria Shopify merchants care about: ease of use, reliability on Shopify product pages, pricing, feature set, reporting and privacy impact. Where relevant we give practical, actionable steps you can implement today to run better A/B tests on Shopify.

How A/B testing works on Shopify: technical realities you must know

Shopify presents specific challenges for experimentation platforms. Understanding them will help you pick the right tool and design reliable tests:

• Page caching and CDN: Shopify caches storefront content heavily; client-side A/B testing tools that rely on JavaScript can experience flicker or be blocked by the cache. Server-side experiments are ideal but usually require Shopify Plus or custom back-end work.
• Checkout limits: Shopify restricts checkout customisations to Shopify Plus merchants. That affects experiments that need to change checkout behaviour or prices inside the checkout flow; most Shopify stores will only be able to run experiments that change product pages, collection pages and the cart.
• Tracking and attribution: For conversion rate optimisation you need accurate revenue and order attribution. Tools that integrate directly with Shopify orders or read the Orders API will give more reliable results than solutions that rely solely on client-side event tracking.
• Performance and Core Web Vitals: Adding heavyweight experiment scripts can slow pages and reduce scores. Lean scripts that render changes quickly and avoid layout shift are important for both user experience and experiment validity.
• Sampling method: There are two common ways to split traffic: split URL tests and client-side variant injection. Split URL is reliable but less convenient for product-level changes because it needs duplicate product pages or redirects. Client-side is easier to set up but can be affected by flicker or tracking issues.
• Statistical design: A/B tests require proper sample size, avoid peeking at significance too often and correct metrics for the goal you care about; conversion rate alone is not always sufficient: revenue per visitor, average order value and lifetime value may be more relevant.

The competitors at a glance: strengths and weaknesses

Here’s a concise comparison so you can see where each solution sits relative to Shopify merchants’ needs.

• ConvertLab: Purpose-built for Shopify product pages; visual editor, variant and price testing, no-code setup, direct integration with Shopify orders and revenue metrics, lean front-end code, pricing suitable for SMEs.
• Optimisely: Enterprise-grade experimentation and feature-flag platform; strong targeting and analytics; supports web and full-stack experiments; needs engineering resources; expensive; more configuration required for Shopify compatibility.
• Google Optimise: Historically a free option that integrated with Google Analytics; as of 2023 it has been discontinued; replacement options in the Google ecosystem are limited and often not optimised for Shopify specifics.

Deep dive: ConvertLab — why it is tailored to Shopify

ConvertLab is designed to handle the most common experiments Shopify merchants need to run: product title tests, description variations, price experiments, image and badge tests. Its strengths for Shopify stores are:

• Shopify-first integration: ConvertLab recognises Shopify product structures, reads SKU/product handles reliably and maps test variants to orders so revenue and conversion tracking are accurate.
• No-code visual editor: You can change text, swap images, test price variants and add urgency badges in the editor without touching theme code. That reduces developer time and the risk of introducing bugs into your theme.
• Price testing support: ConvertLab allows merchants to test price presentations and product-level prices on the product page; when the customer completes checkout the tool attributes revenue to the variant so you measure real impact.
• Lean client-side footprint: The script is designed to avoid layout shift and minimise impact on page performance. That matters for search ranking and user experience.
• Reporting that matches Shopify metrics: Reports show conversions, revenue per visitor and order counts in a way that matches Shopify reporting, making it easy to reconcile numbers and trust results.
• SMB-friendly pricing: Unlike enterprise platforms, ConvertLab targets merchants who want value without enterprise contracts and long implementation cycles.

Practical example: to test two product titles, you open ConvertLab’s visual editor on the product page, highlight the title and create variant B with your new wording. Choose traffic split (for example 50/50), set the primary metric to revenue per visitor or add-to-cart rate, and launch. ConvertLab will map orders back to variants automatically so you can measure actual impact without manual tagging.

Deep dive: Optimisely — power with complexity

Optimisely is one of the best-known experimentation platforms and is built for large organisations that require control across web, mobile and back-end systems. Its advantages and considerations for Shopify merchants are:

• Full-stack experimentation and feature flags: You can run server-side experiments and use feature flags in production, which is useful for product teams and engineering-led roadmaps.
• Powerful targeting and segmentation: Optimisely supports advanced audience targeting, multi-page experiments and complex rollouts.
• Enterprise reporting and integrations: Connects to data warehouses and analytics platforms for deep analysis.
• Requires engineering resources: To get the most from Optimisely you typically need developers to integrate its SDKs and manage experiments; on Shopify that can mean bespoke work to ensure experiments are stable and that orders are attributed correctly.
• Price and scale: Optimisely is priced for enterprise budgets; for smaller merchants this is often prohibitive.
• Shopify-specific friction: Checkout customisations are restricted on Shopify; to run server-side experiments end-to-end you may need Shopify Plus and engineering effort to keep price and checkout consistent across variants.

Optimisely is excellent when you have complex experimentation needs across platforms and a dedicated team to manage experiments, but it can be overkill for most Shopify merchants who want fast, product-level tests without heavy developer involvement.

Deep dive: Google Optimise — what happened and what to use instead

Google Optimise was a popular free tool because it integrated with Google Analytics and offered a simple visual editor. However Google announced the sunset of Google Optimise in 2023. The outcome for Shopify merchants:

• Google Optimise is discontinued: It is no longer a supported or recommended option. Existing users were encouraged to migrate to other platforms.
• GA4 experiments are limited: Google’s suggested alternatives rely on Google Analytics 4 experiments or other paid Google products; these are not a direct replacement and have limitations in terms of UI editing and Shopify-specific order attribution.
• Shopify compatibility issues: Even before sunset, Google Optimise relied on client-side JS and Analytics events; this worked for simple tests but could be unreliable for price tests or for stores that require precise order mapping.

For merchants still looking for a free or low-cost experimentation tool, the discontinuation means you should migrate to a Shopify-focussed app or consider small paid tools that offer better compatibility and support.

A/B testing app comparison: what to evaluate for Shopify

When comparing convertlab vs optimisely or other convertlab alternatives keep these evaluation criteria front and centre:

• Shopify integration: Does the tool read Shopify product handles and order data directly? Can it map variants to orders without manual tagging?
• Ease of use: Can non-technical store owners create tests with a visual editor, or are developers required?
• Price testing capability: Can the tool test product-level price changes and attribute resulting revenue correctly?
• Impact on site performance: How heavy is the script and does it cause layout shift or slow page load?
• Reporting and statistical rigour: Does the platform report conversion rate, revenue per visitor, confidence intervals and handle multiple comparisons appropriately?
• Support and reliability: Is the vendor responsive and does the app receive regular updates for Shopify changes?

Scoring each tool across these areas will highlight which one matches your priorities. For most Shopify merchants the ability to implement tests quickly, accurately attribute revenue and avoid developer time is decisive; that is where ConvertLab is purpose-built to excel.

Practical checklist: setting up reliable A/B tests on Shopify product pages

Follow this checklist when you design and run experiments on Shopify product pages:

• Define the primary metric: For product pages choose revenue per visitor or conversion rate to purchase; for price tests revenue per visitor or profit per visitor may be preferable.
• Estimate sample size: Use your baseline conversion rate, the minimum detectable effect you care about and your daily traffic to estimate how long the test should run. If your traffic is low, focus on experiments that produce larger expected effects.
• Segment wisely: If you expect different behaviour across devices or traffic sources, segment or use targeting rules rather than mixing populations in a single test.
• Avoid peek bias: Do not stop the test early when a variant looks better; follow the planned duration and sample size to avoid false positives.
• Prevent cross-contamination: Use consistent user assignment so returning visitors see the same variant; ensure cookies or local storage persist assignment across sessions.
• Measure secondary metrics: Track add-to-cart rate, average order value and return rate to ensure the uplift is healthy; a higher conversion rate with lower AOV may not be desirable.
• Account for seasonality: Run tests long enough to include typical traffic cycles and sales days; avoid running short tests during sales or abnormal traffic spikes.
• Plan follow-ups: If a variant wins, validate the result by running holdout tests or conducting an A/B test with a larger audience to confirm the outcome before rolling out site-wide.

Sample size and duration: practical guidance for Shopify stores

Merchants often underestimate how much traffic is required to detect small improvements. A few practical rules of thumb:

• If your product page conversion rate is low (for example 1 to 3 percent), detecting a small relative uplift (5 to 10 percent) will usually require tens of thousands of visitors per variant; this can translate to weeks or months on low-traffic pages.
• Focus tests on pages with the highest traffic: category pages, best-seller product pages and paid acquisition landing pages give you much faster results than low-traffic product detail pages.
• Test larger changes to increase the expected effect size: headline rewrites, pricing changes, or adding social proof can produce larger uplifts than microcopy tweaks and therefore require smaller sample sizes.
• If traffic is limited, prefer sequential testing: run a small test to validate direction, then scale the change to more traffic in a second, larger test.

Actionable calculation method: take your average daily product page visitors, multiply by the expected conversion rate to estimate daily conversions, then use a standard sample size tool or calculator to find the required visitors per variant. ConvertLab provides built-in guidance and duration estimates when you create a test, which helps you pick experiments that are feasible for your traffic.

Example tests you can run today

Here are three practical experiments you can implement quickly on Shopify product pages:

• Product title test:
  • Hypothesis: A value-focussed title will increase add-to-cart rate and sales.
  • Action: Create variant B with a title that includes a key benefit and a second variant with the original title.
  • Metrics: Primary metric: revenue per visitor; Secondary: add-to-cart rate, click-through to checkout.
  • Duration: Based on traffic; ConvertLab will estimate duration when you set up the test.
• Short vs long description:
  • Hypothesis: A concise description reduces friction and increases conversion on mobile.
  • Action: Variant A long-form; Variant B concise bullet points with icons.
  • Metrics: Conversion rate by device and revenue per visitor; segment by mobile/desktop.
• Price presentation test:
  • Hypothesis: Showing a limited-time price with a crossed-out original price increases urgency and conversion.
  • Action: Variant B shows original price struck-through, new price and countdown badge; keep checkout price consistent.
  • Metrics: Revenue per visitor and average order value; ensure the variant maps to orders accurately.
  • Note: Be mindful of legal and platform policies around price presentation and ensure prices shown match what customers pay at checkout.

Dealing with performance and layout shift

Client-side experiment scripts can cause visual flicker or layout shift if a variant is injected after the page has rendered. To mitigate these effects:

• Use a pre-render or synchronous injection method for key elements such as the headline and price so changes appear before the page is painted.
• Keep variant code lightweight and avoid large images that load only for one variant; prefer CSS swaps or low-weight DOM changes.
• Measure Core Web Vitals before and after launching experiments and monitor any regressions; a slightly faster page with a small reduction in conversion is still a net loss.

ConvertLab’s editor is built to reduce layout shift on Shopify product pages; it employs techniques to render changes quickly and minimise visual flicker for buyers.

When to consider Optimisely or other convertlab alternatives

ConvertLab is a great fit for most Shopify merchants, but there are scenarios where you might evaluate Optimisely or other alternatives:

• You are an enterprise with a central experimentation programme that must run experiments across web, mobile apps and back-end services; Optimisely’s full-stack features and enterprise governance are useful here.
• You need advanced feature flagging and progressive rollouts integrated with a development workflow; Optimisely or a feature-flag platform may be appropriate.
• You have high engineering capacity and want to run server-side price experiments that include checkout customisations; Shopify Plus combined with an enterprise experimentation stack could be the right path.
• You prefer a vendor that integrates directly with your BI or data warehouse and requires custom analysis pipelines; enterprise platforms are stronger in this area.

For most merchants who primarily want to iterate on product pages and price tests with minimal friction, a Shopify-first app like ConvertLab will be faster to implement and easier to maintain.

Common mistakes merchants make when testing on Shopify

Avoid these mistakes to get reliable, actionable results:

• Running underpowered tests: Small traffic plus small expected effect leads to inconclusive results; either run larger tests or test bigger changes.
• Changing multiple elements at once without a plan: If you change the title, price and image at the same time, you cannot attribute the winning effect to any single change unless you use a multivariate design and have enough traffic.
• Not persisting variant assignment: If users see different variants on return visits results will be noisy; ensure consistent allocation via cookies or server-side assignment.
• Ignoring attribution mismatches: Make sure the experiment’s conversion metrics map to Shopify orders; mismatch between your tool and Shopify’s backend will erode trust in the results.
• Stopping tests early: Avoid concluding tests on short-term spikes; follow pre-defined significance and sample size plans to reduce false positives.

How to interpret results and make decisions

When a test completes, use the following framework to decide whether to roll out, iterate or scrap a change:

• Confirm significance and sample size: The reported uplift should be statistically significant and the test should have reached its planned sample size and duration.
• Check secondary metrics: Ensure AOV, return rate and average margin have not moved in an unfavourable direction.
• Segment the data: Look at performance by device, traffic source and geography; a global winner that hurts a major segment might need more nuanced rollout.
• Validate with a larger cohort: If possible, expose the winning variant to a larger audience as a follow-up validation before making permanent theme changes.
• Document learnings: Record what changed, why it worked or didn’t, and update your testing backlog with follow-up ideas.

ConvertLab alternatives and when they make sense

Besides Optimisely and the now-discontinued Google Optimise, several tools compete in the experimentation space. Consider these categories when evaluating convertlab alternatives:

• Enterprise experimentation platforms: Optimisely, Split; best when cross-platform experiments and developer-driven rollouts are required.
• General-purpose web A/B testing tools: VWO, AB Tasty; they offer robust web editors but will need careful Shopify integration and can be pricier or heavier on performance.
• Shopify-specific testing apps: Smaller apps (for example A/B Testify, Neat A/B Testing) that focus on simple tests and low-cost plans; these can be a fit for tiny stores but may lack features like price testing and robust order mapping.
• DIY approaches: Using Google Analytics events or manual split URLs; feasible for very simple tests but error-prone and difficult to scale.

Choose the alternative that matches the level of technical investment you can make and the scale of experiments you plan to run. If you need a balance of accuracy, Shopify integration and ease of use, ConvertLab is purpose-built for that use case.

Recommended testing roadmap for the next 90 days

If you want a pragmatic plan to start improving conversions right away, follow this 90-day roadmap:

• Week 1: Install a Shopify-focussed testing app such as ConvertLab; audit your highest-traffic product pages and list the top 10 ideas for tests ranked by potential impact and feasibility.
• Weeks 2–4: Run 2 to 3 quick tests on product titles and images; pick pages with the most traffic so you can get results within a few weeks. Use revenue per visitor as the primary metric if you can map orders reliably.
• Month 2: Launch a price presentation test or urgency badge test on best-sellers; track AOV and return rate as secondary metrics. Ensure price shown aligns with checkout behaviour.
• Month 3: Iterate on winners, scale the best changes site-wide and document the learnings into a playbook for future tests. Start more ambitious tests like bundling or free-shipping thresholds if traffic supports them.

This approach balances quick wins with longer-term validation and ensures you build a data-driven optimisation programme without overwhelming your team.

Conclusion and next steps

For Shopify merchants deciding between convertlab vs optimisely and other convertlab alternatives, the right choice depends on scale and resources. Optimisely is excellent for enterprise teams with engineering capacity and cross-platform needs; however for the majority of Shopify stores ConvertLab offers the best balance of Shopify integration, ease of use, accurate revenue mapping and cost-effectiveness.

Start with high-impact product page experiments: title, price presentation and image treatments. Use a Shopify-specific tool that maps orders automatically and avoids heavy scripts that hurt performance. Follow proper experiment design: calculate sample size, persist variant assignment and validate winners before rolling out site-wide.

Call to action

ConvertLab is purpose-built for Shopify product pages. No complex setup, no code, no enterprise pricing. Try it free. Install ConvertLab on the Shopify App Store and run your first product title, description or price test in minutes.