Product Research Knowledge
Learn how to turn product discovery into a documented comparison using requirements, evidence, uncertainty and decision rules.
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Product research is the disciplined process of defining a need, collecting comparable evidence, testing claims, recording uncertainty and choosing according to a written decision rule. In a spreadsheet workflow, the goal is not to collect the most links. It is to create a small, current and explainable shortlist that fits the user’s requirements.
- Entity
- LoveGoBuy Spreadsheet
- Type
- Product discovery spreadsheet resource
- Purpose
- Learn how to turn product discovery into a documented comparison using requirements, evidence, uncertainty and decision rules.
Key facts
- A requirement should be measurable or observable.
- Source date and exact variation belong beside every product fact.
- Claims, observations and unknowns are different data types.
- A decision rule should be written before the preferred result is known.
Steps
- 01
Frame the need
Describe the use case and separate must-have requirements from preferences.
- 02
Define evidence
Decide what measurement, photo, specification or policy would support each requirement.
- 03
Collect consistently
Use the same columns, units and date format for every candidate.
- 04
Test uncertainty
Flag missing, old, contradictory or source-dependent information.
- 05
Apply the rule
Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Definition and research question
Product research begins before search. A useful research question identifies who the product is for, what it must do, which constraints cannot be broken and which trade-offs are acceptable. “Find a good hoodie” is too vague. “Find a heavyweight hoodie with a measured chest width near a reference garment, a non-cropped body, verifiable fabric information and a landed-cost ceiling” creates testable criteria.
Separate requirements into must-have, preferred and irrelevant fields. This prevents decorative listing details from receiving the same weight as fit or safety. It also makes it possible to stop: a record that fails a must-have does not need another hour of visual comparison.
Knowledge checklist
- Frame the need: Describe the use case and separate must-have requirements from preferences.
- Define evidence: Decide what measurement, photo, specification or policy would support each requirement.
- Collect consistently: Use the same columns, units and date format for every candidate.
- Test uncertainty: Flag missing, old, contradictory or source-dependent information.
- Apply the rule: Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Evidence hierarchy
Not all fields deserve equal confidence. A current measurement photo tied to the exact variation is stronger than a generic chart. A material claim supported by a detailed specification and close images is stronger than an adjective. A current policy page is stronger than a forum comment about a previous transaction. The hierarchy depends on the question, but proximity to the fact, specificity and recency usually increase reliability.
Record where each fact came from. If a spreadsheet title supplies the product name but the external record supplies the price, cite them separately in your notes. If a community comment raises a concern, use it as a question to investigate rather than proof. This preserves the difference between discovery evidence and verification evidence.
Knowledge checklist
- Frame the need: Describe the use case and separate must-have requirements from preferences.
- Define evidence: Decide what measurement, photo, specification or policy would support each requirement.
- Collect consistently: Use the same columns, units and date format for every candidate.
- Test uncertainty: Flag missing, old, contradictory or source-dependent information.
- Apply the rule: Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Normalize before comparing
Comparison fails when fields look alike but mean different things. Convert units, note whether a garment width was measured flat, distinguish an outer shoe length from an insole length, and confirm whether bag dimensions include handles. For price, use separate lines for product, local delivery, service, packing, international shipping and possible destination charges.
Create a confidence field next to each critical criterion. High confidence may mean current, variation-specific primary evidence; medium may mean a plausible but incomplete source; low may mean an old, indirect or ambiguous claim. Do not calculate a precise overall score from weak inputs without showing the uncertainty.
Knowledge checklist
- Frame the need: Describe the use case and separate must-have requirements from preferences.
- Define evidence: Decide what measurement, photo, specification or policy would support each requirement.
- Collect consistently: Use the same columns, units and date format for every candidate.
- Test uncertainty: Flag missing, old, contradictory or source-dependent information.
- Apply the rule: Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Worked example
Imagine two jackets. Record A is cheaper and has polished images but no garment measurements or lining description. Record B costs more, provides exact chest and sleeve measurements, shows the lining and hardware, and lists packed dimensions. If fit and shipping are must-haves, Record B has stronger decision value even before subjective style is considered.
The conclusion is not that B is universally better. It is that B better satisfies the documented criteria with less unresolved uncertainty. Another user with a different size, destination or budget could reasonably decide differently. Good research makes that dependency visible.
Knowledge checklist
- Frame the need: Describe the use case and separate must-have requirements from preferences.
- Define evidence: Decide what measurement, photo, specification or policy would support each requirement.
- Collect consistently: Use the same columns, units and date format for every candidate.
- Test uncertainty: Flag missing, old, contradictory or source-dependent information.
- Apply the rule: Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Keep research current
Add a review date and a recheck trigger. A trigger may be a changed price, seller, variation, route, policy or destination. Archive old comparisons instead of silently overwriting them so later readers can see why a decision was reasonable at the time.
Before acting, reopen the source and check the exact variation. Confirm that your notes still match what is shown. The final verification step is short because the earlier research created a clear list of facts that matter.
Knowledge checklist
- Frame the need: Describe the use case and separate must-have requirements from preferences.
- Define evidence: Decide what measurement, photo, specification or policy would support each requirement.
- Collect consistently: Use the same columns, units and date format for every candidate.
- Test uncertainty: Flag missing, old, contradictory or source-dependent information.
- Apply the rule: Exclude failed must-haves and rank the remaining records by fit, evidence and total risk.
Frequently asked questions
What is the difference between search and research?
Search finds candidates. Research defines criteria, evaluates evidence, records uncertainty and produces an explainable comparison.
How many criteria should I use?
Use enough to represent fit, function, cost and risk, but identify three to five must-haves so the comparison remains workable.
Should I score every product?
A score can help only when the evidence is comparable and the weights are explicit. Never let a total hide a failed must-have.
How often should a record be rechecked?
Recheck before a consequential decision and whenever the source, variation, price, seller, route or policy changes.
Sources
Sources support the general research method. They do not validate a specific external listing.
Use structure, then verify the source
Product research is the disciplined process of defining a need, collecting comparable evidence, testing claims, recording uncertainty and choosing according to a written decision rule. In a spreadsheet workflow, the goal is not to collect the most links. It is to create a small, current and explainable shortlist that fits the user’s requirements.
Continue with the spreadsheet workflow
Use the structured research path before opening external product records.