What makes an A/B test readout useful to a product team?

My preferred first page answers four questions:

What changed, and by how much?

How uncertain is the estimate?

Did an important guardrail get worse?

What decision does the evidence support, and what remains unresolved?

A result can be statistically significant and still too small to matter. An inconclusive result can still leave a meaningful gain or loss plausible. The decision needs more than a green badge.

I built an independent A/B analyzer to make these checks easier to inspect. The project includes a live demo, downloadable reports and documented limitations: https://contra.com/p/mgoXb3qY-ab-test-analyzer-correct-p-values-power-and-fdr

If you work on product or CRO, what do you wish analysts put on the first page more often?

This is a strong framing of experiment readouts: separating signal, uncertainty, guardrails, and the actual decision keeps the team honest. The reminder that significance is not the same as usefulness is especially important.

Created a demo AI automation workflow for lead capture and follow-up. The workflow captures leads through Google Forms, stores data in Google Sheets, sends automated email responses, updates a CRM, and notifies the team through Slack. This project demonstrates my skills in AI automation, workflow design, integrations, and business process automation.

Lead capture and follow-up are excellent areas for practical AI automation because missed responses directly affect revenue. A useful system should capture the enquiry, understand the lead’s intent, update the CRM, notify the right team member, and trigger appropriate follow-ups...