Random tests burn budget. Smart A/B test ideas turn traffic into revenue. If you sell online, you need a reliable way to spot what to test next and why. This guide shows how to generate A/B test ideas that come from evidence, not hunches, so you stop leaving easy money on the table.
A/B test ideas that start with users
Start where decisions happen. Review real journeys and gather user research insight from on-site polls, moderated interviews, chat logs and returns reasons. Add heatmaps and session replays to see scanning behaviour, rage clicks and dead elements. Pair this with a quick website analytics review to find drop-offs by device, source and page type. Patterns will surface fast.
Translate what you see into testable changes. If shoppers skim, make headlines carry the benefit. If reassurance sits below the fold, move delivery and returns copy higher. If reviews are buried, surface social proof cues near price and add a photo filter in the review list.
Analyse your data like an operator
Pull funnel and product metrics weekly. Look for product page exits, low add-to-basket on mobile, slow collections in checkout and high search refinements. Mark the worst offenders. Your shortlist writes itself.
- Compare cohorts by new and returning to spot skewed results in ecommerce A/B testing.
- Check site speed before and after releases to avoid testing broken pages.
- Use simple segments by device and location to find hidden checkout friction points.
Borrow how people decide
People choose what feels safe, easy and timely. Make choices obvious. Use clear primary buttons, calm copy and simple steps. Show volume cues such as how many bought today. Use low stock notices when true. Place alternative options nearby so a no can become a maybe.
- Show trust badges performance near payment options and customer support links.
- Use a price anchoring layout that compares bundles, sizes or subscription plans fairly.
- Place returns, delivery, tax and warranty terms where they are needed, not hidden away.
- Blend qualitative notes with behavioural science in A/B testing without overclaiming.
Prioritise and size your tests
Write ideas in a hypothesis backlog template so anyone can scan intent, audience, page type and expected outcome. Do test idea prioritisation with a simple impact, confidence and effort score. Size each run with a test sample size calculator. Set a statistical significance threshold before launch and stick to it. Define your control and variant setup clearly, including tracking and guardrail metrics.
Use A/B/n testing strategies when the decision is a layout family, such as three product cards versus four, but keep traffic per variant healthy. Track conversion uplift rate, average order value and refund rate to avoid shallow wins.
Example ecommerce hypotheses
Use these A/B test hypothesis examples to get moving. Keep copy specific to your category and audience.
- Product page A/B tests. Replace a generic headline with a benefit-led headline and add a short fit or sizing nudge near the size selector to increase add to basket on mobile.
- Landing page A/B tests. Swap a lifestyle hero for a product-in-context shot, add a single primary CTA above the fold and simplify the benefits list to improve click-through to category.
- Checkout fixes. Combine postcode and address fields, auto-detect card type and move express pay options above the fold to reduce form abandonment.
Keep a living list of conversion rate optimisation ideas. Retire weak patterns fast. Double down on winners across ranges and seasons. When in doubt, ship a tidy control and learn. Small, consistent gains beat loud one-offs.
Common phrases you will use while planning and reporting include product page A/B tests, landing page A/B tests, A/B/n testing strategies, website analytics review and conversion uplift rate. Keep these visible in your documentation so the team stays aligned.
