Read a result in five steps
1
Use the actual test period
Set the reporting range to the dates you want to evaluate. Exclude known pre-test or post-test periods when they do not belong in the comparison.
2
Start with the primary metric
Read the metric you chose for the hypothesis—such as CVR, RPS, AOV, or an earlier interaction rate.
3
Compare the observed direction and size
Which variant is currently ahead on the primary metric, and by how much in observed terms? Treat this as what the data shows so far, not automatic proof of future lift.
4
Use supporting metrics to understand the journey
Review sessions, clicks, add-to-cart, checkout, orders, revenue, and AOV/RPS where relevant. These can help locate where behavior differed.
5
Check whether anything complicates the result
Consider traffic/weight changes, routing rules, direct page traffic, product or offer changes, tracking issues, and differences in the compared destinations.
Core experiment metrics
For the core visit-level interaction rates, repeated actions in the same visit do not make that visit count as multiple converted sessions for the rate.
Which metric should I care about?
Choose based on the hypothesis, not the metric that happens to look best later.CVR
Use when the main question is:“Does this experience turn a higher percentage of visitors into buyers?”Common tests:
- hero message;
- proof placement;
- page story;
- purchase clarity.
RPS
Use when revenue per visitor is the most important overall business outcome. This can be useful when a change may affect both conversion and the amount purchased.AOV
Use when the test is specifically about basket size or order value. Do not use AOV alone to judge a page that changes purchase conversion. A variant can have higher AOV but fewer buyers.CTR / Add-to-Cart / Checkout Rate
Use these as primary metrics only when the hypothesis is intentionally about that earlier behavior. Otherwise they are often more useful as diagnostics.Example: CVR rises while AOV falls
Suppose the variant shows:- higher observed CVR;
- lower observed AOV;
- RPS roughly flat.
Example: Add-to-Cart Rate rises but CVR does not
Possible observation:- more sessions add to cart;
- purchase conversion remains flat or falls.
- Was cart/checkout the same across variants?
- Did the offer expectation change?
- Did the variant encourage lower-intent cart additions?
- Was there a product or plan mismatch?
Experience analytics and experiment results can differ
An underlying Jurni experience can receive traffic outside the experiment—for example:- direct page URL;
- another campaign;
- a link that bypasses the experiment Smart Link;
- QA or internal traffic.
Date ranges matter
Use the same dates when comparing views. Be especially careful when:- the experiment started partway through the selected range;
- traffic weights changed;
- routing rules changed;
- the offer or product changed;
- an ad campaign was paused or restarted;
- tracking was fixed during the test.
Traffic weights and routing changes
If you alter traffic allocation during the test, record when and why. A combined full-period result can still be useful, but your team should understand that different parts of the test ran under different allocation conditions. Routing Rules also mean some traffic can be intentionally directed to one variant rather than randomized through normal weights. Traffic assignment →Statistical significance / confidence
A variant being numerically ahead does not automatically mean you have a reliable winner. Use the confidence/statistical information actually shown in the Jurni experiment workflow available to your account. Do not apply universal rules such as “500 sessions means significant” or “95% is always required” unless the methodology your team is using explicitly defines them. When a newer confidence/winner-status workflow is available, read the status together with the selected metric, minimum-data conditions, and the actual test context rather than treating the label as permission to stop thinking. Confidence & winners →Custom events
Some implementations use mapped downstream events such as quiz steps, email capture, or other custom actions. These are not standard variant-level metrics throughout every experiment graph. If a custom event is the success condition for your test, agree how it will be measured and read before launching.If Jurni and another platform disagree
Check:- Same date range?
- Same traffic scope?
- Is campaign traffic entering through the experiment Smart Link?
- Does the external platform use the same session/attribution definition?
- Did consent block one system but not the other?
- Is the underlying page receiving direct traffic?
- Are purchases attributed through the same checkout path?