Data-driven attribution for fashion e-commerce

Understand the platform context

Google Analytics describes a model that uses converting and non-converting paths to allocate credit. Its documented attribution reports offer data-driven, paid-and-organic last click and Google-paid-channels last click. First-click, linear, time-decay and position-based models are no longer offered in those reports.

Those details apply to that product. A separate attribution vendor, a warehouse model or another platform can have different inputs and rules. Avoid using the same data-driven label as evidence that two reports are directly comparable.

Inspect the observation coverage

Ask which channels, devices and conversion events enter the model. A sophisticated method cannot make every unobserved interaction visible. Record consent and identity limitations as part of interpreting the available paths.

For fashion, also inspect the commercial event. Purchase value before returns answers a different question from retained order value after the return period. A model can be applied consistently while still measuring a value that does not fit the decision.

Keep interpretation separate from allocation

Use modelled credit as one measurement view. Review whether a proposed action remains sensible given campaign objectives, customer mix, stock and margins. A change in assigned credit should prompt an investigation rather than an automatic change in spend.

For a claim about additional sales caused by a specific intervention, evaluate the appropriate experimental evidence. A reporting model comparison and a controlled business experiment are different analytical tasks.

Turn the guide into a useful review

1. Name the platform and conversion event.
2. Record channel and identity coverage.
3. Check purchase and returns definitions.
4. Review model output alongside commercial context.