Web A/B testing

Test your website without making every learning loop a ticket.

Diti gives product and growth teams a visual workflow for focused website experiments. Build a variation, define the audience and outcome, quality-check the experience, and learn from a controlled comparison.

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Where Diti fits

Best for focused experience changes

✓Test messaging, offers, visibility, styling, and other customer-facing page decisions.
✓Create a clear control-versus-variation comparison around one hypothesis.
✓Preview the experience before exposing it to live visitors.
✓Give product, growth, and design a shared place to review results.

The boundary

Know when code should lead

—Changes to backend logic, pricing rules, fulfillment, or sensitive customer data need engineering review.
—Treat checkout and payment flows as high-risk surfaces that require thorough QA and guardrails.
—A test should not be used to bypass accessibility, security, or release standards.
—A winning result is useful only when the assignment and measurement are trustworthy.

A practical workflow

Form a hypothesis

State the customer problem, the proposed change, and the primary metric before you edit the page.

Create and verify

Build the variation, preview it on the real page, and check important devices and states.

Measure responsibly

Allocate traffic, let the test gather enough evidence, and check guardrails before rolling out a winner.

Questions, answered

Is web A/B testing really no-code?

Diti is designed to remove repeated implementation work for supported website experience changes. Your team should still involve engineering where a test changes application logic, data handling, or other higher-risk behavior.

What is a good first website test?

Pick one high-friction page and one concrete hypothesis—for example, whether clearer delivery information increases completed checkout—not a wholesale redesign.

How long should a web A/B test run?

Run until it reaches the sample and decision criteria you set in advance. Calendar time alone is not a reliable stopping rule.

Turn the next website question into a measured decision.

Start with a meaningful customer experience problem, build a focused variation, and give the result enough evidence to be useful.

Start experimenting