Social Media Analytics Prompts: Turn Post Data Into Next Steps
Give AI a labeled table of your recent posts, one content goal, and a common measurement window. Ask it to check the data before looking for patterns, then turn those observations into a small test plan. These four prompts work from data you paste or upload. They do not connect your social accounts. Verify the calculations before changing what you publish.
Prepare a post table AI can actually compare
You have a month of posts and one question: what deserves another try? Start with 10 to 30 recent posts from one platform. That is a manageable starting batch, not a statistical minimum. Include the quiet posts too. A table of winners can only explain what your winners have in common.
Export the post report available in your analytics, or copy the fields you can access into a sheet. In YouTube Studio, open Analytics, choose Advanced Mode or See more, set the report and dates, then use Export current view. YouTube is updating Studio, so the layout may differ. Instagram and TikTok users can prepare the same table from their available post analytics without relying on a universal export button.
YouTube Help: Advanced Mode and exporting reports
Use one row per post and keep the platform’s exact metric labels. Add a short topic, format, opening line, and duration yourself if the report omits them. OpenAI recommends clear column names and one record per row for data analysis. A spreadsheet or text table also makes exact numbers easier to check than screenshots.
OpenAI Help: Data analysis with ChatGPT
The date range needs one more detail
A report for October can mix a post published on October 1 with one published on October 28. Their totals have had different amounts of time to grow. Record both the report’s dates and each post’s measurement window, such as its first seven complete days. That window is an example, not a platform rule. If you only have lifetime totals, label them and compare posts of similar ages cautiously. Do not divide views by days and call that a fair correction.
Keep paid promotion and collaborations visible as separate flags. Split platforms and incompatible view definitions into separate comparisons. A YouTube Short and an Instagram carousel can answer different questions even when they share a topic.
| Field group | What to include | How to handle a gap |
|---|---|---|
| Identity and timing | post_id, platform, published_at with timezone, report_start, report_end, measurement_window | Keep the ID. An unknown window limits the comparison. |
| Content | topic, format, hook, duration_seconds, paid, collaboration | Describe what is actually in the post; mark unknown labels. |
| Exposure and response | The exact views metric, reach, likes, comments, shares, saves | Use null for unavailable fields. A reported zero stays zero. |
| Viewing and growth | avg_watch_seconds, the named retention metric if available, post_attributed_follows | Keep unavailable values null. Account-wide follower change is not post-attributed follows. |
post_id,platform,published_at,report_start,report_end,measurement_window,topic,format,hook,duration_seconds,paid,collaboration,views_metric,reach,likes,comments,shares,saves,avg_watch_seconds,retention_metric,post_attributed_follows
Rename views_metric to the metric shown in your report. Drop columns you cannot supply, or leave them null and explain why. A blank does not mean nobody saved the post.
Copy four prompts for your weekly review
Run these in order in the same conversation, or attach the checked table again when you start a new chat. Replace the brackets with your own goal and data. If your assistant cannot run calculations, use your spreadsheet for the arithmetic and ask it to interpret the checked results.
1. Check the data before analyzing performance
This first pass catches problems that can make every later recommendation look more certain than it should. Let the assistant return a data-quality report before it offers content advice.
Help me review my own social media post data. Work only from the attached or pasted table. INPUTS Platform and account context: [platform, niche, audience] Goal: [one goal, such as more saves per account reached] Report dates and timezone: [start, end, timezone] Measurement window per post: [for example, first 7 complete days; or lifetime totals with capture date] Metric definitions and units: [paste the report's definitions] Post table: [paste or attach] First return a data-quality report, not content recommendations: - Confirm rows read, unique post IDs, included dates, and any unread rows. - List duplicate IDs, missing fields, inconsistent units, impossible values, and zero denominators. Do not silently delete or repair rows. - Treat null or blank as unavailable, not zero. Preserve reported zeros. - Separate platforms, formats, measurement windows, paid posts, and collaborations where the comparison requires it. - Distinguish post-attributed follows from account-wide follower change. - Explain which comparisons are possible and which need better data. Return: issue | affected post IDs | consequence | correction or question for me. Ask me to resolve material issues before proceeding. Never invent missing metrics or claim to have connected my account.
2. Find patterns you can trace back to posts
A useful pattern has a visible comparison behind it. Ask for the rows and group sizes so you can tell a repeated signal from one spectacular post.
Using the checked table and definitions above, compare posts for my stated goal. RULES Use only compatible platform, format, and measurement-window groups. Report excluded rows and why. Do not fill missing data. State every numerator, denominator, unit, and formula before calculating a rate. If the denominator is zero or missing, return not calculable. For each group, report sample size, median, and range for the goal metric. Show the underlying post IDs and calculated values. Keep outliers in the first result. Also show how the comparison changes without the single highest observation, clearly labeled as a sensitivity check. Do not remove a post just because it performed poorly. Compare topics or formats only where the available rows allow it. Treat groups with few posts as leads to test. OUTPUT Up to 3 observations, each with: - compared groups, sample sizes, window, and post IDs - the numbers and calculation supporting the observation - other plausible explanations or confounders - a question to investigate next Separate observations from hypotheses. Do not claim an algorithm cause, ideal posting time, hidden retention, or a guaranteed winner. Show the calculation code if you used it.
3. Investigate an unusually strong or weak post
An outlier is a reason to inspect the post and its circumstances. The numbers alone cannot tell you whether the opening, topic, or distribution conditions explain it.
Investigate post [post_id] against comparable posts in the checked table. Return a compact diagnostic note: 1. State what is unusual numerically, with the baseline group, sample size, metric definition, and window. 2. Check whether age, paid promotion, collaboration, format, duration, or missing data could explain the difference. 3. Separate what the table shows from hypotheses that need the actual content or private analytics. 4. Tell me which specific post, opening line, timestamp, traffic source, or analytics field to inspect next and why. 5. Suggest one content change to test only if the available evidence supports a useful hypothesis. Do not infer a retention curve from average watch time, attribute account-wide follows to one post, or diagnose a restriction from low views. If the comparison is invalid, say what would make it usable.
4. Turn the review into next week’s tests
Decide what to make next week and when to check the result.
Turn the supported observations above into at most 3 feasible content tests for next week. INPUTS Posts I can make: [number and formats] Production limits: [time, budget, footage] Facts and audience promise to preserve: [list] Metric and formula I will use: [one primary metric] Measurement window and review date: [window, date, timezone] For each test return: - observation and post IDs it comes from - hypothesis, explicitly labeled - the one variable to change - comparison post or matched set, with important conditions held similar - concrete next-post brief: audience question, opening, payoff, format - primary metric, formula, window, and a guardrail metric - a decision rule I choose before posting, plus what would remain inconclusive - what evidence I must collect and when to review it Do not invent results, benchmarks, certainty, or a universal uplift threshold. If I have too few comparable posts, suggest a small exploratory batch and explain what it can and cannot tell me. Leave publishing and final decisions to me.
See how one review becomes a content test
Constructed example
A home-cooking creator wants more saves per account reached. All four posts below are organic, have no collaborations, and come from the same platform. Each row covers the first seven complete days after publication. Use these figures to check the math, not to set a performance target.
| Post | Topic and format | Reach | Saves | Saves ÷ reach |
|---|---|---|---|---|
| A | Knife tip, 30-second Reel | 2,000 | 40 | 2.0% |
| B | Pantry swap, 30-second Reel | 2,400 | 60 | 2.5% |
| C | Fast dinner montage, 30-second Reel | 10,000 | 80 | 0.8% |
| D | Pantry swap, 30-second Reel | 2,200 | 55 | 2.5% |
Post C has the most saves in total, but B and D have the higher save rate. C’s calculation is 80 ÷ 10,000 × 100 = 0.8%. The two pantry-swap posts each reach 2.5%. That gives you a topic to explore for a save-focused goal. You still need to learn why people saved those posts and whether another pantry swap earns a similar response.
A practical next test is another pantry-swap Reel with the same length and payoff style, changing only the opening: show the finished swap before explaining it. Compare it with B and D over the same first-seven-day window. Track saves ÷ reach as the primary metric and reach as a guardrail, so a higher rate on very little exposure does not look like an automatic success.
Before posting, write down what would make the result useful enough to repeat and what would need another trial. If the new post reaches only a handful of accounts, keep the result exploratory. You can repeat the comparison with another matched post rather than declaring a new strategy from a single percentage.
For help choosing the denominator, read our guide to save rate vs. views.
Check the answer before changing your content
Spot-check the rows behind the strongest recommendation. Recalculate a rate, inspect the group size, and check that the same observation window was used. OpenAI advises reviewing generated code, outputs, and assumptions when relying on data analysis. Ask for the code or intermediate table if the result feels too neat.
OpenAI Help: Data analysis with ChatGPT
The calculator’s rate is a different question from the save-rate example above. Keep the numerator and denominator in the label wherever you compare results. Never compare your reach-based save rate with a follower-based engagement benchmark.
When the table points to a content question, open the actual post. Kabo’s AI Video Analyzer can give timestamped content feedback on a public TikTok, Reel, or Short. Use that feedback to form an editing hypothesis; it cannot supply your private retention report or prove the cause of a performance change.
Inspect the public video’s content
Posting-time findings need the same caution. If every tutorial went out in the morning and every montage at night, your table cannot separate time from format. Try a matched content comparison before rearranging your whole schedule. Why the same video can get different views
Keep the checked table, the answer you used, and your chosen test together. Next week, add the new rows using the same definitions and window. The review becomes useful when you can compare a prediction with what happened, including an inconclusive result.
Sources and method
Product and export details checked on October 22, 2026. OpenAI documents structured inputs, code-backed analysis, and calculation review. YouTube documents exporting the current Analytics view. For other platforms, start with the post reports you can access. The prompts, comparison advice, and cooking example are Kabo editorial material. Results depend on your data and the model you use.