Concepts referenced in this article, defined.
Run rigorous A/B tests and personalize every visit on Shopify or any storefront โ no engineers required.
BigCommerce doesn't have native A/B testing. To run controlled experiments on your BigCommerce store, you need a third-party testing tool installed via JavaScript. This guide walks through how to set up A/B testing on BigCommerce, what to test for maximum impact, the platform-specific limitations you'll encounter, and which tools work best for different team setups.
BigCommerce's native capabilities for conversion optimization are limited to:
For actual A/B testing โ where traffic is randomly split between a control and variant, and results are measured with statistical significance โ you need an external tool.
For BigCommerce, these tools install cleanly:
VWO: Install via JavaScript snippet in BigCommerce's Script Manager. Supports visual editor, code editor, and heatmaps. Good for teams that want behavioral analytics alongside testing.
Convert: Privacy-first alternative. Install via Script Manager. Supports visual and code variants. Unlimited tests on higher plans. Good for agencies and privacy-sensitive brands.
Optimizely: Enterprise option with full-stack testing. Higher implementation complexity and cost.
Google Optimize: Discontinued in September 2023. Not available.
Verify installation using your testing tool's debugger (most offer a browser extension or debug panel).
BigCommerce checkout fires standard ecommerce events, but you need to confirm your testing tool captures them. The key events to track:
Purchase (primary): Fires on the order confirmation page. In BigCommerce, this is typically /order-confirmation or a similar URL. Configure your testing tool to track purchases when this URL loads.
Add to Cart: BigCommerce fires a cart addition when the Add to Cart button is clicked. This can be tracked via a JavaScript event listener or via BigCommerce's Stencil event system.
Checkout Initiated: Fires when a user navigates to /checkout. Configure as a secondary conversion goal.
Before launching any test:
Start with a high-traffic, high-impact element:
Keep your first test simple: one change, one primary metric, run for 2โ4 weeks minimum.
The PDP is where buying decisions are made. High-impact tests:
Headline and product name framing: "Premium Stainless Steel Water Bottle" vs. "Stay Hydrated All Day โ 24-Hour Insulation" โ benefit-led vs. feature-led copy.
Image order: Hero product shot first vs. lifestyle shot first. Lifestyle images often convert better for fashion and home categories; product detail shots win for technical/functional categories.
Price anchoring: Showing MSRP crossed out vs. not. Showing per-unit price for bundles vs. total. Testing these is particularly valuable for Indian D2C brands where price sensitivity is high.
Reviews placement: Reviews at the top of the page vs. below the fold vs. highlighted in a sticky sidebar.
Scarcity and urgency signals: "Only 8 left in stock" vs. no stock indicator. "Order within 3 hours for same-day dispatch" vs. generic shipping messaging.
Filter interface: Sidebar filters vs. top filter bar. Mobile users often engage more with collapsed, expandable filters than sidebars that take up half the screen.
Product card layout: 2-column vs. 3-column grid on mobile. Image size ratio. Whether to show price prominently or less so.
Sort order default: "Most Popular" vs. "New Arrivals" vs. "Best Rated" as the default sort often changes conversion significantly.
Hero banner: Headline, subheadline, CTA text, and image all affect homepage click-through to product pages. Test one element at a time.
Category featured: Which product category do you feature prominently? Test which category driver produces the most downstream conversions.
Trust signals above the fold: Showing "10,000+ happy customers" or delivery promise in the hero section vs. further down.
BigCommerce's checkout is partially locked down โ some changes require the BigCommerce Checkout SDK or developer involvement. What's typically testable:
Full checkout redesigns typically require BigCommerce's custom checkout solution (available on higher plans) and developer involvement.
Checkout restrictions: BigCommerce's standard checkout has limited customization without the Optimized One-Page Checkout or custom checkout API. You can't freely restructure the checkout layout without a developer.
Theme-level limitations: Unlike Shopify, BigCommerce doesn't have a visual theme editor with sectioned layouts. Testing structural layout changes often requires code-level variant building.
Script conflicts: BigCommerce stores often have multiple third-party scripts (analytics, chat, reviews, etc.). Heavy script loads can slow page rendering and cause testing tools to flicker (showing the original page momentarily before the variant loads). Minimize script conflicts by checking load order in Script Manager.
Stencil theme architecture: BigCommerce uses the Stencil theme framework. A/B testing variants built in a visual editor may not perfectly respect Stencil's template inheritance, especially for complex layout changes. Developers familiar with Stencil are recommended for non-trivial structural tests.
Use a sample size calculator before starting tests:
For this example, you need approximately 6,800 visitors per variant (13,600 total) to detect a 0.5% improvement reliably.
If your product page gets 500 sessions per week, this test needs 27 weeks to conclude. This is why low-traffic page tests are rarely worth running โ start with your highest-traffic pages.
| Tool | Best For | Price | Setup Complexity |
|---|---|---|---|
| VWO | Mid-market, needs behavioral analytics | $199+/mo | Medium |
| Convert | Privacy-focused, agency use | $99+/mo | Medium |
| Optimizely | Enterprise programs | $1,500+/mo | High |
| AB Tasty | Enterprise, personalization | $800+/mo | High |
| Hotjar (analytics only) | Behavioral research only | $32+/mo | Low |
For BigCommerce brands looking for a combined A/B testing and personalization solution at accessible price points, VWO or Convert are the most practical starting points.
Note on CustomFit.ai: CustomFit.ai is currently purpose-built for Shopify. If you're evaluating a platform migration from BigCommerce to Shopify, CustomFit.ai's Shopify-native no-code testing is a strong argument for the migration.