Conversion optimisation for fashion e-commerce

Locate the change

A site-wide conversion movement can hide different behaviours by device, audience or product category. Identify the affected segment before proposing a solution. Check event definitions and recent tracking changes at the same time.

In fashion, size selection, stock, delivery and returns information may affect the decision. They are areas to investigate, not explanations that can be assumed from a single conversion-rate number.

Turn a signal into a hypothesis

A useful hypothesis names the shopper group, the proposed change and the reason it might help. Attach the observations that support it and make any uncertainty visible. A session recording is useful context but does not by itself establish how common a problem is.

Choose an outcome before running the test. If the proposal aims to reduce hesitation, completed purchases may matter more than a rise in one intermediate click. Include suitable commercial guardrails, such as returns or contribution value, when the data permits.

Keep a learning record

Document the variation, timing, affected audience and approval. Record other material changes during the evaluation period so later readers can interpret the result. A test with an uncertain result should remain uncertain.

Use the learning to refine the next question. Repeatedly delivering an attractive variation without a clear measure or decision owner does not create a useful optimisation process.

Turn the guide into a useful review

1. Identify the affected journey and segment.
2. Validate the measurement before diagnosis.
3. Define one change and the expected mechanism.
4. Review the outcome and preserve the learning.