Microsoft Clarity.
A clearer next move.

What is getting in the way on our mobile product pages?

The useful starting point for a Microsoft Clarity integration is a business question your team needs to answer. Connecting a source creates access to signals; understanding those signals requires clear definitions, commercial context and a decision someone can own.

Faccelerate brings that conversation into a workflow: connect the relevant data, investigate the change with CeeCee, prioritise the opportunity and prepare a next step for your team. Keep the source evidence and assumptions visible throughout.

The data behind the decision

These are the source areas to discuss when defining your setup. Actual coverage depends on the available connector, account permissions and supported fields.

Use behaviour evidence to form a hypothesis

Aggregate conversion data can tell you where the journey changed, but it rarely explains the experience. Behaviour evidence helps a team inspect what customers encounter. For fashion, that might mean size selection, fit information, product imagery or the visibility of delivery and returns information.

Avoid turning one session into a conclusion

An individual recording is useful context, not proof that every shopper has the same problem. Look for a repeated pattern in a relevant segment and compare it with quantitative performance. Consent settings, masking and sample availability affect what can be observed.

Make the next experiment specific

Combine a clearly defined friction point with the affected page and device. State what you expect to improve and how you will judge the result. Your team can then review a proposed change rather than receiving a vague instruction to improve user experience.

From signal to action

Use this example as a starting point for a workflow that fits your team. It illustrates a possible investigation, not an automated result or a guaranteed outcome.

1. Choose a page and device segment from the conversion investigation.
2. Review available behaviour evidence for a repeated pattern.
3. Create a CRO hypothesis with a review step and success metric.

Record the owner, the approval required and the success measure. An action might be a deeper investigation, a prepared draft or a controlled experiment. Avoid treating an unusual metric as proof of a cause.

Set up a useful, trustworthy view

Start with the business question and the account or property that contains the relevant evidence. Agree the reporting period, currency, timezone and metric definitions before comparing systems. Document any historical gaps, excluded data or expected reporting delays.

Validate a small sample against the source platform. When numbers differ, check filters, attribution and refresh timing before building a combined view. Establish who owns the connection and how the team will respond when permissions expire or data stops refreshing.

A useful first workflow is small enough to verify: one question, the necessary source fields and one repeatable team decision. Expand coverage once the definitions and outputs are trusted.

Permissions, limitations and review

Available aggregate data and recording access depend on Clarity permissions and supported endpoints. This page does not promise automated access to every recording. Confirm data masking, consent and the exact connector scope.

Reading data and making live changes are separate permissions. Confirm which actions are supported, which require approval and which remain manual. The team should be able to trace a recommendation back to its evidence and understand what the system could not verify.

For technical platform details, see the official Microsoft Clarity documentation ↗. Platform documentation describes the source service; it does not establish the availability of a specific Faccelerate integration.

Turn this context into focused work

Explore the specialist workflows that can use this evidence.