Qualify the job before the account
A promising ecommerce analytics lead needs more than a company in the right sector. Confirm the job: a profitable-looking campaign stops looking profitable after returns and discounts are included. Identify analysts comparing orders, returns, and acquisition sources as the possible user group, then find who can authorize a pilot; the working buyer hypothesis is an ecommerce operator deciding where to spend merchandising effort.
Ask about the current workaround, exports from store, advertising, and returns systems joined in a spreadsheet, and what would make changing it worthwhile. Do not assign a budget or procurement timeline from company size, funding, or job title alone.
Use a category-specific qualification rubric
Use the calculator below to organize your judgment after reading the source. Unknown evidence should remain unknown. Its weights are a transparent prioritization convention, not a prediction of purchase probability.
| Dimension | Evidence to seek | Reason to pause |
|---|---|---|
| Problem | Which costs and adjustments belong in the decision you are trying to make? | The person cannot describe a recent instance of the problem. |
| Product fit | Can you reconcile a small historical order cohort including a refund and a discounted sale? | stores requiring an unsupported sales-channel connector or audited accounting statements |
| Timing | the store adds channels or experiences a rise in returns | The proposed change has no confirmed date or relevance. |
| Implementation | Can one sample order be reconciled across the source systems? | attribution uncertainty and missing cost data must remain visible |
| Value | the operator can explain the cohort total using traceable source orders | The buyer and user disagree on what success looks like. |
Work through an illustrative assessment
Suppose the source says: “Our ad report shows revenue, but it ignores refunds and discount codes. How do we compare campaigns using reconciled order data?” That supports investigating the problem, but it does not yet settle purchasing authority or technical compatibility. A reviewer should ask the two discovery questions above and preserve the answers beside the source.
Compare that with: A post celebrates gross sales without asking an analytics question. This weaker example should not receive the same pain or timing score. A relevant public topic is not evidence of a current evaluation.
Even a strong problem signal should be rejected when the need belongs to stores requiring an unsupported sales-channel connector or audited accounting statements. Qualifying out prevents a misleading promise and preserves time for a prospect the product can help.
Create a reviewable handoff
A qualified opportunity should contain the original evidence, a summary of the job, confirmed and unknown constraints, the buyer/user distinction, and an agreed next step. Keep a score attached to those facts so another teammate can challenge the conclusion.
Propose a pilot only when the critical gap is addressed: attribution uncertainty and missing cost data must remain visible. Evaluate the pilot through a real observation that the operator can explain the cohort total using traceable source orders, and keep that observation separate from the initial qualification score.
About these playbooks and examples
Prepared by DemandBolt with AI assistance as practical planning resources for people building and selling software. The category scenarios and message examples are illustrative, not quotations from real prospects or claims of customer results. Adapt the hypotheses to your product, verify original sources, and review capabilities before making an offer.
The workflow follows our first-customer guide and buying-signal review method. Our PyInvesting and Rae records distinguish actual internal work from targets. Published .