Use an AI prompt to summarize A/B clickstream results

Turn raw A/B clickstream data into a stakeholder-ready, one-page report with p-values and 95% CIs. Ideal for analysts — repurpose as slides, emails, or exec briefs.

September 29, 2025

Today’s prompt gives a ready-made method to convert raw A/B clickstream data into a one-page, non-technical brief that flags meaningful metric gaps with p-values and 95% CIs. Ideal for analysts, PMs, or growth teams who need quick, stakeholder-ready recommendations — and easy to repurpose as slide decks, exec summaries, or email digests.

Today's Prompt:

You are tasked with analyzing the performance metrics of two distinct A/B test groups derived from website clickstream data. Your goal is to compare these groups across specified key metrics, identify and highlight any statistically significant differences between them, and report the associated p-values and confidence intervals for each comparison. The analysis should be synthesized into a clear, concise, and actionable one-page report intended for a non-technical audience, such as stakeholders or decision-makers.

The two groups are labeled as {GROUP_A_NAME} and {GROUP_B_NAME}. The comparison must focus on the following metrics: {LIST_OF_METRICS_TO_COMPARE}.

Details and Guidance: 

- Use appropriate statistical tests for each metric depending on their data type (e.g., proportions, means, medians, time-on-site, conversion rates, etc.).

- Clearly state the sample size of each group.

- Calculate p-values to determine statistical significance and indicate the significance threshold used (e.g., p < 0.05).

- Include 95% confidence intervals for the differences between groups for each metric.

- Briefly comment on any metrics that show significant differences, including the direction and potential impact.

- Use charts or tables to visually summarize key results, if possible.

- Avoid technical jargon—explain all terms (e.g., “p-value”) in plain language.

- When reporting, focus on clarity and implications for decision-making (e.g., which group performed better, and what actions might be suggested).

Output Structure: 

- A one-page report (max ~500 words), with the following structure:

1. Executive Summary: Short paragraph summarizing key findings.

2. Comparison Table: Table listing each metric, group A value, group B value, difference, p-value, and 95% CI.

3. Statistical Highlights: Bulleted explanation of metrics with significant differences and what they imply.

4. Methodology Note: Brief note outlining statistical tests used and significance criteria.

5. Plain-language Definitions: Short section to explain p-value and confidence interval.

- Tone should be clear, concise, and directed at business stakeholders.

Template Variables to be filled in: 

- {GROUP_A_NAME}: Name or label of group A

- {GROUP_B_NAME}: Name or label of group B

- {LIST_OF_METRICS_TO_COMPARE}: List of metrics to be compared (e.g., conversion rate, average session duration, click-through rate)

External Context Required: 

Please [ATTACH FILE: a CSV or Excel file containing raw or summarized clickstream data for both groups, including columns for group indicator, user/session IDs, and all relevant performance metrics].

Or, paste a relevant data extract with the necessary columns for analysis.

Why we like this prompt:

1. Explicit Communication: 

The prompt provides a clear and unambiguous description of the analytic task (“compare two A/B test groups on specified metrics”), ensuring the AI understands both the context and the exact nature of the assignment.

2. Relevant and Helpful Details: 

Specific guidance on statistical methods, reporting requirements (e.g., p-values, confidence intervals), and even advice on how to handle various metric types gives the AI precise instructions necessary to perform a thorough and accurate analysis.

3. Structured Output Request: 

The required report format—consisting of an executive summary, comparison table, statistical highlights, methodology note, and definitions—outlines an exact structure and tone, aligning expectations for clarity and business relevance.

4. Goal Clarity: 

By directly stating the audience (“non-technical stakeholders or decision-makers”) and the need for plain-language explanations, the prompt clarifies the intended communication style and the ultimate purpose of the output.

5. Variable Customization and Data Guidance: 

Template variables and explicit instructions for attaching or pasting the appropriate dataset ensure the AI can tailor its analysis to any use case, while still receiving all necessary information to execute the task properly.

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