Customer research for Ecommerce analytics

Define the question before collecting names

For ecommerce analytics, investigate whether a profitable-looking campaign stops looking profitable after returns and discounts are included. Research is useful when it explains a workflow, its owner, and an unresolved constraint. A list of companies in a sector cannot establish that those companies have this problem.

Begin with analysts comparing orders, returns, and acquisition sources and check how their work relates to an ecommerce operator deciding where to spend merchandising effort. Map the current process around exports from store, advertising, and returns systems joined in a spreadsheet. You are looking for the step where the workaround stops being adequate, rather than asking people to validate an abstract product idea.

Search for the language of the problem

Try the queries below in a search engine or the search feature of a relevant professional community. Read the original discussion and its date. Results are starting points for research; these query examples do not claim any particular search volume or the existence of a matching prospect.

Write an evidence note you can revisit

Illustrative source text: “Our ad report shows revenue, but it ignores refunds and discount codes. How do we compare campaigns using reconciled order data?” Save the actual source URL and date when researching a real example. Keep a short relevant excerpt and your interpretation in separate fields. Do not rewrite your interpretation as something the author said.

The working hypothesis is that the store adds channels or experiences a rise in returns. The missing information includes whether the author is an ecommerce operator deciding where to spend merchandising effort, whether the need is still open, and which implementation requirements remain unverified. Keep this constraint visible: attribution uncertainty and missing cost data must remain visible.

• Observed: the original words, the visible date, the task described, and any explicit requirements.
• To verify: whether the current process is exports from store, advertising, and returns systems joined in a spreadsheet.
• Disqualify when the need belongs to stores requiring an unsupported sales-channel connector or audited accounting statements.
• Next step: one context-appropriate question, or no contact when there is no suitable route.

Use a short discovery interview

Ask about a recent event before showing your product. Let the person explain the sequence in their own words; do not treat agreement with a leading question as validation.

• Which costs and adjustments belong in the decision you are trying to make?
• Can one sample order be reconciled across the source systems?
• What would need to be true for you to reconcile a small historical order cohort including a refund and a discounted sale?
• How would you verify that the operator can explain the cohort total using traceable source orders?

Turn notes into a research decision

Compare independently sourced accounts of the same task. Keep an example that contradicts your hypothesis, such as this ambiguous scenario: A post celebrates gross sales without asking an analytics question. It may justify more reading, but it is insufficient to label someone a buyer.

At the end of a bounded research session, decide whether to narrow the audience, ask a specific follow-up, change the pilot, or stop. Keep unknowns visible and store only the information needed for that decision. Research quality is measured by a better decision, not by how many names are collected.