A rise in conversion after new copy does not prove that the copy itself was what worked. The price, the days of the week, and the audience composition could have changed at the same time. Before deciding to scale, make a list of such changes and separate observation from a causal conclusion.
A simple example of mixed factors
In a hypothetical first week, the site received 100 visits and two goal visits; in the second, 100 and four. Conversion rose from 2% to 4%. But in the second week a promotion also began and a mailing went out to regular customers.
The correct wording: “In the second week, conversion was 4%; at the same time, the copy, the offer, and the source changed.” The statement “The new headline doubled sales” does not follow from this data. Moreover, a goal visit in the example does not necessarily mean a confirmed payment.
Make the comparison fit for purpose
Choose the same goal, time zone, duration, and composition of days of the week. Check for tracking changes: a new consent banner or a counter error can change the observed sample. Separately compare sources and devices, without hiding a small number of observations.
Keep a log: what was changed, when, and for what reason. If the promotion cannot be separated from the edit, name the entire set of changes as the object of observation. Do not retrospectively pick only the successful days while excluding the rest without a basis that was clear in advance.
The next verifiable step
For a future comparison, define the main change in advance and how the other factors will be controlled. The method for planning an experiment depends on the task and the number of factors; this is a principle described in the NIST guide. With a small flow, do not promise a quick, statistically reliable A/B result.
Sometimes a reasonable outcome is to keep clear copy for the quality of its explanation and continue observing, without attributing the measured growth in money to it. If you test further, write down the stopping rule and the required data before looking at the result. This order helps make decisions without invented precision; it does not guarantee a commercial effect from every change.
Diagram: 1 — record the baseline period; 2 — list the conditions that changed at the same time; 3 — choose a comparable next test.
Related task: Zero orders after twenty clicks: what can be concluded from a small test.
Sources
NIST: choosing an experimental design · Metric: terms and definitions. Reviewed on September 22, 2026. Practical scenarios and diagrams are editorial methodology; the hypothetical numbers are not customer data.
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