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Start free trial →A/B testing — also called split testing — is a method for comparing two versions of a webpage, email, or app screen by showing each to a randomly assigned portion of live traffic and measuring which one produces better results. Version A is usually the existing experience (the control); version B is the change you want to evaluate (the variant).
Because visitors are assigned at random and both versions run at the same time, the only systematic difference between the two groups is the change you made. That is what separates an A/B test from simply redesigning a page and comparing last month to this month, where seasonality, ad spend, and a dozen other variables move at once.
Most opinions about what will increase conversions are wrong, including opinions held by people with a great deal of experience. Ecommerce teams routinely find that the button colour everyone argued about does nothing, while a change nobody expected — reordering trust badges, rewriting a shipping line, removing a field from checkout — moves revenue by several percent.
A/B testing replaces that argument with evidence. It also caps your downside. If a redesign turns out to hurt conversions, only the share of traffic assigned to the variant is affected, and you find out in days rather than discovering it in next quarter's revenue. For a brand spending ₹15–20 lakh a month on paid acquisition, shipping an untested change to 100% of traffic is an expensive way to learn something.
There is a practical floor, though. A/B testing needs volume. Detecting a realistic 10% relative lift on a 2% conversion rate takes roughly 30,000–50,000 visitors per variant. Stores below about 5,000 monthly sessions will usually get more value from fixing obvious usability problems and improving traffic quality than from formal experimentation.
An Indian D2C coffee brand believed their product page needed more lifestyle photography. The team ran an A/B test: control kept the existing three-image gallery, variant added four more lifestyle shots and a short brew video.
The variant lost. Conversion rate fell from 2.4% to 2.0% because the extra media pushed the price, grind-size selector, and add-to-cart button further down the mobile page. The team then tested the opposite direction — fewer images, selector and price moved up — and gained 18% over the original control. Two tests, one genuinely counterintuitive answer, and no expensive redesign shipped to everyone on a hunch.
The mechanics matter as much as the statistics. Tests that flicker — where the original renders briefly before the variant swaps in — bias results and irritate visitors. Tests that assign the same visitor to different variants across sessions produce meaningless data. Tests that fire on the wrong audience answer a question you did not ask.
CustomFit.ai handles variant assignment, anti-flicker rendering, and audience targeting without developer involvement, and reports conversion rate alongside revenue per visitor so you can tell a genuine win from a discount-driven one.
Run smarter A/B tests with CustomFit.ai — 14-day free trial, no credit card required.