A one-page test plan should make the decision and measurement approach easy to understand. It cannot replace the analysis needed to choose a sample, construct a comparison or interpret uncertainty.
Use the templates below as a recommended starting point for supplier, broker and advisor work. They are not official Walmart or Sam's Club forms. The plan allows four useful outcomes: proceed, revise, stop, or gather more evidence.
Copy this one-page plan
| Field | What to write |
|---|---|
| Decision | The business choice this test will inform and who will make it. |
| Change | The approved intervention and the condition it will be compared with. |
| Scope | Retailer, channel, item list, locations or audience, and explicit exclusions. |
| Assignment | What is randomized, how allocation happens, and who records the assignment. If it is not randomized, say so. |
| Timing | Pre-period, treatment period, outcome follow-up, reporting lag and relevant calendar events. |
| Primary metric | Exact numerator, denominator, unit, source, period and aggregation rule. |
| Design and precision | Smallest useful effect, sizing assumptions, expected precision or power, and planned analysis. |
| Guardrails | Material harms to watch, operational thresholds, monitoring cadence and stop authority. |
| Execution | Required retailer permissions, field instructions, exposure checks and exception log. |
| Data checks | Expected population, keys, source freshness, missingness, restatements and reconciliation. |
| Decision rule | How benefit, uncertainty, costs and guardrails will lead to a next step. |
Keep a short linked appendix for the sample-size work, metric definitions and analysis code. The brief summarizes those decisions; it should not hide missing work behind the word “validated.”
Use one primary outcome and purposeful supporting measures
Select the outcome that directly informs the decision. Additional measures can diagnose the mechanism or protect against harm, but listing more metrics does not make a weak test stronger. If several outcomes can independently trigger a statistical win, account for multiple testing in the analysis plan.
Examples of distinct concepts include:
- Conversion rate: a defined number of converting visits or people divided by eligible visits or people.
- Units per visit: quantity divided by visits; this can change without a change in conversion probability.
- Retail POS sales: shopper purchases in the stated scope; not automatically supplier revenue or profit.
- Return rate: a specifically defined return numerator and eligible sales cohort, with sufficient follow-up.
- Availability: the chosen in-stock measure with its own eligibility and weighting rules.
Only include a measure if the authorized data can support it. A supplier's sales report does not necessarily contain store traffic, household behavior, retailer labor cost or customer sentiment.
Three illustrative designs, without invented results
Approved product-page content change. Define the eligible audience and the persistent assignment supported by the platform. Check exposure, caching and cross-device limitations. Decide whether the metric is conversion per eligible visitor or units per visit. Do not assume a supplier can run a randomized PDP test in a retailer's system.
Store display change. If store-level execution is feasible, randomize eligible locations within pre-defined groups. Size the study using store-level variability and correlation. Monitor execution and availability alongside sales. If returns are the outcome, account for the lag between purchase and return.
Supplier-owned email subject test. Randomly allocate eligible subscribers, change the subject only, and define the denominator and measurement window. Use clicks or subsequent action where appropriate; document automated traffic filtering. Monitor unsubscribes as a guardrail. No fixed conversion count guarantees a decision.
These are planning examples, not client results or pre-approved retailer tests.
Small samples need an honest plan
Neither fifty conversions per version nor two weeks is a general minimum that makes a test reliable. The required information depends on the baseline, variability, meaningful effect, assignment, follow-up and analysis.
When treatment varies over time, repeating the same A/B pattern on the same weekdays can confound treatment with the weekday. A valid switchback needs a designed schedule, appropriate time blocks and a plan for effects that persist after switching. Research on switchback experiments shows why carryover changes both design and inference.
If the available sample cannot answer the causal question, use the activity to learn about execution or usability, or plan a different study. Explain what remains unknown rather than extending the test until a favorable number appears.
Copy this one-page scorecard
- Question and scope: what changed, where, and during which period.
- Evidence basis: randomized experiment, nonrandomized comparison, or descriptive observation.
- Primary result: estimate in meaningful units, uncertainty interval, and comparison with the business threshold.
- Data and execution: eligible coverage, deviations, missingness and changes to the original plan.
- Guardrails: results, unresolved harms and operational judgment.
- Decision: proceed, revise, stop, or gather more evidence, with a reason.
- Follow-through: owner, next action, authorization needed and review date.
Keep uncertainty on the main page when it affects the decision. A label such as “statistically significant” does not establish commercial importance. The ASA's guidance explains why context and uncertainty belong in the interpretation.
Put the brief to work
Read Instrumentation and Data Quality before relying on the metrics, and Ten-Store Holdouts when the footprint is limited.
Retail Reason Intelligence helps clarify retail operating questions and the context around a decision. For help adapting this brief to your supplier or advisory work, get in touch.