Conversion rate optimisation (CRO) is one of the highest-return activities available to an ecommerce business. Unlike paid media, where spend scales linearly with results, CRO improvements compound — a 20% improvement in conversion rate across your existing traffic delivers 20% more revenue from every pound of marketing spend, permanently.
Despite this, most ecommerce businesses approach CRO haphazardly — making changes based on instinct, following advice from generic blog posts, or copying what competitors are doing without understanding why it works or whether it applies to their specific audience.
Good CRO is a systematic, evidence-based practice. It starts with data (what is actually happening on your site), generates testable hypotheses (why users aren't converting), and validates changes through controlled experiments before rolling them out.
This guide covers 10 specific tests — drawn from patterns seen across real ecommerce accounts — that consistently produce measurable conversion uplift. They are starting points, not guaranteed outcomes: your audience, product category, and price point will influence which tests produce the biggest impact for your specific site.
Before You Test: The Data Foundation
Effective CRO requires quantitative and qualitative data. Before running any tests, ensure you have:
- Google Analytics 4 (or equivalent): Ecommerce event tracking — product views, add-to-cart, checkout starts, purchases — with a clean, verified data layer
- Heatmap and session recording tools (Hotjar, Microsoft Clarity, or equivalent): To observe where users click, scroll, and abandon
- On-site search analytics: What users search for and whether they find it
- Checkout funnel analysis: Step-by-step drop-off rates from cart to purchase
Without this data, you're testing blind. A month of data collection before beginning a CRO programme is almost always a worthwhile investment.
Test 1: Product Image Optimisation
Hypothesis: Higher-quality, more varied product imagery reduces uncertainty and increases add-to-cart rates.
What to test:
- Multiple images per product (minimum 4–6, including lifestyle context shots)
- 360-degree view or video demonstrations for complex products
- Zoom functionality on hover or click
- Showing the product in use or at scale (with a size reference)
Why it works: Uncertainty is the enemy of conversion. Users who cannot clearly visualise a product before purchase are more likely to abandon or, worse, buy and return. Comprehensive imagery reduces uncertainty without requiring the user to seek information elsewhere.
Measurement: Add-to-cart rate by product; product detail page engagement (time on page, image interaction rate).
Test 2: Above-the-Fold CTA Prominence
Hypothesis: Moving the "Add to Cart" button above the fold (visible without scrolling) on product pages increases add-to-cart rate.
What to test:
- Position of the primary CTA relative to product images and description
- Button size, colour contrast, and copy (e.g., "Add to Basket" vs "Buy Now" vs "Add to Cart")
- Sticky add-to-cart button that remains visible as users scroll through long product descriptions
Why it works: Users who have decided to purchase should not have to scroll to find the conversion action. On mobile especially, below-the-fold CTAs create unnecessary friction.
Measurement: Add-to-cart rate; scroll depth vs. CTA interaction correlation.
Test 3: Social Proof at the Point of Purchase
Hypothesis: Displaying recent reviews and aggregate ratings directly adjacent to the "Add to Cart" button increases conversion.
What to test:
- Star rating + review count directly beneath the product title (not just at the bottom of the page)
- "X people bought this in the last 24 hours" (real-time social proof)
- Review snippets from verified purchasers near the CTA
- Trust badges (secure payment, money-back guarantee, UK delivery)
Why it works: Purchasing decisions — particularly from new visitors — are heavily influenced by social proof. Reviews already on your site are often underutilised because they're buried below the fold in a reviews section that most users never reach.
Measurement: Conversion rate segmented by pages with/without prominent review display; A/B test with review widget position variable.
Test 4: Checkout Guest Option Prominence
Hypothesis: Making the guest checkout option more prominent than account creation reduces checkout abandonment.
What to test:
- Removing the account creation page from the checkout entry entirely (account creation offered post-purchase instead)
- Repositioning "Continue as Guest" as the primary CTA, with account login as secondary
- Auto-fill from browser/password manager signals on the guest email field
Why it works: Requiring account creation is consistently the single highest-friction element in the checkout flow for new customers. Research across thousands of ecommerce sites finds that prominent account registration pages increase checkout abandonment by 20–40%.
Measurement: Checkout start-to-purchase completion rate; new customer account creation rate (should remain stable if post-purchase registration is offered).
Test 5: Shipping Cost Transparency and Free Shipping Threshold
Hypothesis: Displaying shipping cost or free shipping threshold earlier in the purchase journey reduces cart abandonment caused by unexpected costs at checkout.
What to test:
- Show shipping cost estimate on the product page (even before adding to cart)
- Display "Free shipping on orders over £X — add £Y more" in the cart
- A/B test the effect of qualifying for free shipping on average order value vs. a flat shipping fee
Why it works: Unexpected shipping costs are the most commonly cited reason for checkout abandonment in UK ecommerce research. Transparency earlier in the funnel doesn't eliminate the barrier, but it prevents the abandonment spike caused by surprise at checkout.
Measurement: Cart-to-checkout rate; checkout-to-purchase rate; average order value (if testing free shipping threshold messaging).
Test 6: Product Page Copy — Features vs. Benefits
Hypothesis: Rewriting product descriptions to lead with benefits (what the product does for the customer) rather than features (what the product is) increases conversion.
What to test:
- Rewrite top 20 product descriptions to lead with a benefit statement
- Structure: One-line benefit headline → key benefits bullet list → technical specification details lower on page
- A/B test copy variants on high-traffic, mid-conversion-rate products
Why it works: Most manufacturer-provided product descriptions are feature lists. Users don't buy features — they buy outcomes. "1200-thread-count Egyptian cotton" is a feature. "The softest sleep of your life" is the benefit. Both may be accurate; the benefit-led version consistently outperforms.
Measurement: Add-to-cart rate on rewritten product pages vs. original.
Test 7: Cart Abandonment Recovery — Timing and Offer
Hypothesis: Optimising cart abandonment email timing and content increases recovery revenue.
What to test:
- Email 1 timing: 30 minutes vs. 1 hour vs. 3 hours post-abandonment
- Email 2 timing: 24 hours vs. 48 hours after email 1
- Content of email 1: Reminder (no discount) vs. reminder with a time-limited discount
- Subject line variations: Question format vs. product name vs. urgency
Why it works: Cart abandonment recovery email sequences are typically the highest-ROI email programme for ecommerce businesses, yet most are sub-optimally configured. Even small improvements in open rate, click rate, or conversion rate on these emails compound significantly over time.
Measurement: Recovery revenue by variant; email open rate, click rate, conversion rate segmented by email sequence position.
Test 8: Product Recommendations — Relevance vs. Bestsellers
Hypothesis: Personalised "You may also like" recommendations based on browsing behaviour outperform static bestseller lists for increasing average order value.
What to test:
- Category-matched recommendations (same category as currently viewed product)
- Collaborative filtering ("customers who bought this also bought")
- Complementary product logic (cross-sells based on product type — e.g., showing accessories with a main product)
- Position of recommendations (on product page vs. cart page vs. post-checkout)
Why it works: Generic "bestsellers" recommendations have low contextual relevance. Recommendations that are logically connected to the user's current product interest have substantially higher click and add-to-cart rates.
Measurement: Recommendation widget click rate; add-to-cart rate from recommendation clicks; average order value before and after.
Test 9: Mobile Checkout Optimisation
Hypothesis: Reducing tap targets, form fields, and steps specifically on mobile checkout increases mobile conversion rate.
What to test:
- Number of form fields (remove any that aren't strictly necessary)
- Input type attributes on form fields (numeric keyboard for phone/postcode fields, email keyboard for email)
- One-page vs. multi-step checkout (test which your mobile audience prefers)
- Apple Pay / Google Pay as a primary checkout option on mobile
- Auto-address completion from postcode lookup
Why it works: Mobile traffic now accounts for 60–70% of sessions for most ecommerce sites, but mobile conversion rates are typically 40–60% lower than desktop. Much of this gap is attributable to checkout friction that was never optimised for touch interfaces.
Measurement: Mobile conversion rate; mobile checkout form completion rate; mobile vs. desktop conversion rate gap.
Test 10: Post-Purchase Upsell
Hypothesis: Offering a relevant upsell on the order confirmation page or in the post-purchase email increases revenue per customer without affecting primary conversion rate.
What to test:
- Order confirmation page upsell: "Complete your order with this add-on" — single-click add (charged to the same payment method)
- Post-purchase email upsell: Targeted recommendation sent 2–3 days after the original purchase
- Timing of post-purchase email based on product type (consumables: shorter window; durables: longer window)
Why it works: The confirmation page and immediate post-purchase period are the highest-trust moments in the customer relationship. The purchase decision is made; concerns about delivery and quality are temporarily set aside. A relevant, well-priced addition at this moment encounters much lower sales resistance than an equivalent cold offer.
Measurement: Post-purchase upsell conversion rate; impact on revenue per order; customer lifetime value (tracked over 90 days for repeat purchase rate).
Running Tests Effectively
A few principles that apply across all of the tests above:
Test one variable at a time. Multivariate testing is more complex than it sounds and requires substantially more traffic to reach statistical significance. Start with simple A/B tests — one change, two variants, clear hypothesis.
Define your success metric before you start. Decide in advance what constitutes a win. Post-hoc "the test didn't improve conversion but it improved time on page, which is interesting" is a sign of a poorly defined test.
Run tests to statistical significance. Don't end a test early because one variant is winning. Statistical significance of 95%+ requires more data than most people assume. Tools like Optimizely, VWO, and Google Optimize all calculate this for you — trust the calculator.
Document everything. A test log that records what was tested, the hypothesis, the result, and the action taken is one of the most valuable assets a CRO programme produces. Learnings compound over time.
Our CRO service includes a full audit, hypothesis prioritisation, test design, and statistical analysis — with reporting built around revenue impact rather than vanity metrics. Get in touch to discuss what a structured CRO programme could do for your ecommerce business.
