How to analyse experiment results

Data without interpretation is just numbers. Analysis turns results into decisions. Did the experiment win, lose, or produce inconclusive results? Was the change statistically significant or just noise? What did we learn that applies beyond this specific test? This chapter teaches you how to read results properly, calculate confidence intervals, spot patterns, and document insights so every experiment win or lose makes you smarter.

Begin by analysing the experiment in three passes. First, validate data integrity: confirm traffic split, event firing and sample size. If the variant received less than thirty-five per cent of traffic or tracking broke for a day, mark the test invalid and rerun.

Second, read the primary metric. Use the testing tool’s statistics and insist on ninety-five per cent confidence. Do not declare a winner early; random spikes flatten with time.

Third, inspect secondary metrics and segments. A headline that lifts overall leads might hide a drop among enterprise visitors. Segment wins that hurt strategic accounts are not real wins.

Log each pass in the backlog card. Note exclusions, confidence and any surprising segment swings. This discipline feeds the story you will share next.

The numbers are clear; now you need people to act on them, which is the focus of the next section.

Write a short results story. Start with a one-line headline: “Variant lifted booked meetings by nine per cent at ninety-five per cent confidence.” Add two bullet paragraphs. The first explains why you tested the change, anchored in the original hypothesis. The second states what you recommend: roll out, iterate or drop.

Include one chart only. A simple bar or line showing cumulative conversions keeps eyes on the message. Flooding stakeholders with p-values and z-scores dilutes impact.

Deliver the story in the team channel and at the next stand-up. Invite one question then move on. Concise reporting builds trust and keeps the backlog moving.

With the story told, you must store the insight where future teammates can find it. The next section covers building that library.

Create a learning library in Notion. Each experiment becomes a row with standard fields: date, page, hypothesis, outcome, lift, audience notes and link to the results story.

Add two tags: win or loss. Losses matter because they save repeat effort. Colour-code tags for quick scanning.

Schedule a monthly review. Filter the table for wins on similar pages and compile a pattern report. For example, three headline tests may reveal that specific numbers beat generic benefits on pricing pages.

The library now houses cumulative knowledge. Next you will convert that knowledge into sharper future experiments.

Start each sprint planning session with a five-minute scan of the library. Search for tests on the same page type or audience. Copy the winning logic into new hypotheses rather than guessing again.

If a past loss contradicts a new idea, demand stronger evidence before approving the build. This gate conserves design and development hours.

Every quarter aggregate learning into a playbook: evergreen principles like “Use the buyer’s job title in call-to-action buttons lifts click-through on demo forms.” Share the playbook with marketing and product teams so experiments influence broader strategy.

The loop now feeds itself: learn, store, apply, and grow. A brief recap cements the habit.

Learning is the real asset of experimentation. Rigorous analysis secures valid results, a crisp story converts data into action, a living library safeguards insight and regular reviews turn past work into faster future wins.

Adopt this cycle and booked-meeting rates will climb with each sprint while team confidence soars. Your optimisation engine is now complete and ready to compound month after month.