Your business records may contain an answer customers would value, even if you have never published a study. Original research contributes evidence a business has collected or analyzed. ROI.LIVE starts with the decision that evidence could improve. A small study of your own operation can be useful when its limits remain visible.
Publishing a number from memory is not enough. Readers need to know who or what was counted, when the observations occurred and how the calculation was made. Without those details, even an accurate number is hard to interpret or reuse.
Choose a question the records can answer
Begin with an operational question that has a defined population. “How long did completed orders take to leave our warehouse in August?” is measurable if the relevant timestamps exist. “Why are customers happier?” needs a different design and may not be answerable from shipping records alone.
Write down the question and the kind of conclusion you want to test before collecting more data. Narrow the scope until the available records can support it, and let the findings change your view. Include the inconvenient examples. If the sample excludes canceled orders, late records or a particular product, state that exclusion.
A minimum method record
Keep the method attached to the finding
| Field | What to preserve |
|---|---|
| Question | The specific thing being estimated or compared |
| Population and sample | Who could be included, who was included and the count |
| Time window | Dates of observation and extraction |
| Definitions | Units, start and end events, costs and denominators |
| Exclusions | Missing records, filters and reasons |
| Analysis | Formula, comparison and treatment of outliers |
| Limits | Selection bias, confounders and what cannot be concluded |
Editable resource
Use the worksheet
The CSV opens in Excel, Google Sheets or another spreadsheet editor. It contains labeled fields; sample data, where included, is illustrative.
Download the editable worksheet (CSV)
Preview the fields
- Question
- Population
- Sample count
- Observation start
- Observation end
- Extraction date
- Definitions and units
- Exclusions
- Calculation
- Comparison method
- Confounders
- Limits
- Permission
- Public source URL
A small worked example
The average hides the nine-day order
Illustrative records: 1, 1, 2, 2 and 9 days to dispatch.
Four orders left within two days. One took nine days.
Read the full explanation
This dataset is illustrative. Five completed orders took 1, 1, 2, 2 and 9 days to dispatch. Their mean is three days: fifteen total days divided by five orders. Their median is two days. Reporting only the mean hides the pattern: four orders left within two days, while one took nine.
The nine-day order is where the owner has a reason to look closer. Investigate what happened and whether it appears elsewhere before changing the process. These five orders are not an industry benchmark, and a quiet week may not describe a peak month. The example gives you a question to pursue, not a cause you can already name.
Show the underlying counts beside the chart. A visual should expose the pattern, not replace the data needed to check it.
Compare like with like
A before-and-after comparison can describe a change. It rarely isolates its cause on its own. Product mix, customer mix, seasonality, pricing and staffing can move at the same time as the intervention being discussed.
If you need to know whether a change caused the result before committing more money, plan a suitable comparison before the change where practical. Record allocation, measurement periods and the intended outcome. For observational work, name the other explanations that remain plausible. A precise percentage can still leave you with an uncertain decision.
Publish enough to support reuse
Include a concise method note, the relevant table, the date and a stable source page. Share an anonymized dataset when permission and privacy allow it. If individual records cannot be released, provide sufficient aggregate detail to reproduce the published calculation and explain the restriction.
Keep the finding separate from what you want to do about it. If one group took longer to buy, changing follow-up timing is a recommendation to test. The data alone has not settled it. The failure-story guide makes the same distinction for operational accounts, so a useful observation does not become a promise the business cannot defend.
Make the finding findable and maintainable
Use a title that names the question and population. Link the study where a related article relies on its result. Give media contacts the method and limits alongside the headline. The earned-media process explains how to offer a useful source to a relevant publication.
Preserve the original release and identify substantive corrections. Newer evidence may change the interpretation. Original research does not create permanent ownership of a topic or guarantee citations, but a transparent record gives others a reason to examine and reference the work.
Questions owners ask
How large must an original study be?
The required sample depends on the question and claim. A small operational sample can be useful if its population, counts and limits are explicit; it should not be presented as an industry-wide estimate.
Can a before-and-after comparison prove the change caused the result?
Not on its own. Record other changes and use a suitable comparison when the claim requires causation. Describe observational results as observations.
What if customer-level data cannot be shared?
Publish permitted aggregates, definitions, exclusions and the calculation. Explain the restriction and avoid identifying private individuals through small groups.
Will original research guarantee backlinks or AI citations?
No. It creates material others may find useful to reference. Relevance, discovery and editorial choices still determine whether it is used.
Method
This guide presents ROI.LIVE’s editorial analysis and worked methods. Numerical examples are illustrative unless expressly identified otherwise. No ranking, citation or business outcome is guaranteed.
Substantively revised September 8, 2026. Definitions, calculations, sources and internal destinations were reviewed for this edition.
