THE THESISLow traffic does not justify weak evidence; it requires stronger priors, larger changes, richer metrics and explicit decision costs.
Start with decision value
Estimate the upside, implementation cost and risk of being wrong. Do not run a test whose maximum plausible value is lower than the cost of waiting. Prioritize high-leverage uncertainties in offer, audience and journey.
Use informed priors carefully
Priors can use historical performance, similar pages and qualitative research, but sensitivity analysis must show how conclusions change under skeptical assumptions. A prior is documented evidence, not executive preference.
Increase signal per visitor
Test meaningful journey changes, instrument micro-conversions tied to the final outcome and segment only when the hypothesis requires it. Combine behavior with moderated research and sales feedback to explain the mechanism.
Use explicit decision thresholds
Report the probability of improvement, expected loss and credible interval. Ship when expected downside is tolerable relative to the opportunity, continue when more information is valuable, and stop when the likely effect is commercially trivial.
Practical questions
Does Bayesian mean we can stop anytime?+
Not without a policy. Frequent peeking still changes behavior and costs; define decision checks and thresholds before launch.