How to A/B test an ecommerce chatbot

An A/B test compares two experiences during the same period: one includes the chatbot, while the other keeps the existing journey. Its purpose is to learn whether a change caused a difference, not to confirm a conclusion chosen in advance. For an ecommerce chatbot, experimental design matters as much as the technology.

State a testable hypothesis

Avoid broad goals such as “improve the experience.” Connect the test to observed friction. A useful hypothesis might concern reaching a relevant product after a natural-language request or completing a choice among similar items.

Define the expected behavior and a possible negative signal in advance. This prevents the success criterion from changing after the data is visible.

Keep the groups comparable

Assign visitors to the experiences consistently and preserve that assignment through the session. Avoid showing chat only to people who already perform actions associated with strong intent, as this creates an imbalanced comparison.

Keep promotions, catalog data, creative material, and other changes equivalent. If several elements change together, a difference cannot be attributed to the chatbot.

The Valen chat is available on mobile and desktop, as demonstrated on a demo store. Analyze devices separately when the hypothesis supports it, because available space, interaction, and behavior may differ.

Choose a primary metric

The primary metric should represent the hypothesis. For product discovery, it might be reaching a page consistent with the request. For decision support, it might be the transition from conversation to a selected product.

Valen lets shoppers search the catalog and ask product questions during the conversation, as demonstrated on a demo store. This makes it possible to define events along the journey, but it does not determine the outcome a store will see.

How Valen fits

The Valen chat is available on mobile and desktop, and Valen records sessions and events, as demonstrated on a demo store. Valen also keeps a registry of capabilities that can be enabled separately for each store, as demonstrated on a demo store.

These capabilities make the tested experience configurable and observable. Unlike an isolated chatbot, the experiment can follow movement between conversation and catalog without presuming an effect on conversion.

Add guardrail metrics

An apparent improvement may hide costs. Observe incorrect answers, loading impact, exits after opening chat, ignored recommendations, and unresolved requests. Check whether the chatbot shifts work to the store team instead of resolving it.

Decide timing and analysis in advance

Duration depends on traffic and the frequency of the selected event. Avoid stopping the test as soon as a favorable difference appears. Plan the period, document exclusions and technical errors, and read the primary metric together with the guardrails.

Segment results when there is a reason established before the test. Too many segments increase the chance of finding random differences.

After the test

An uncertain result is not the same as failure. It may show that the friction is rare, the chatbot does not affect the decisive step, or a more focused experiment is needed. Preserve the hypothesis, configuration, and outcome so the same test is not repeated without new information.

Book a demo to define an experiment aligned with your store journey.