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YES

As of 13 August 2026, AI can find your best-performing social media posts.

Most people should do this themselves; the prompt is on this page.

Can you do it?

15 minutesto a draft.

30 minutesto something you’d act on.

Cost, all in£0

Skill neededchat-fluent

Who has to check ityou

What the alternative costsThe supplied sources do not give a price for a human marketing-analysis service.

If this goes wrong: you prioritise the wrong content pattern for a campaign and can correct the plan after checking the rankings against the source data.

What to actually do

  1. Do it yourself

    The route this page recommends

    A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

    How to actually do it

    1. Open the analytics area for each relevant social platform and export the same recent period as CSV or spreadsheet files, including post date, platform, post text or identifier, format, reach or impressions, and engagement metrics.
    2. Remove columns containing private messages, email addresses, customer names or other personal data, then combine the exports into one spreadsheet while keeping a platform column.
    3. Choose the business outcome that defines performance, such as engagement rate, clicks, saves, shares, leads or reach, and decide whether posts from different platforms should be ranked together.
    4. Upload the cleaned spreadsheet to a chatbot, paste the copyable prompt, and replace the bracketed choices for the primary metric, secondary metric and number of posts.
    5. Check the returned top-post table row by row against the original spreadsheet, including dates, metric values, missing fields and the stated ranking calculation.
    6. Open the original posts and platform analytics for the shortlisted results, then confirm that the format, topic and performance patterns are not explained by paid promotion, unusual reach or a campaign-specific event.
    7. Save the verified ranking and use only the supported patterns to plan the next batch of posts.

    Prompt

    Analyse the attached social media analytics export and find my best-performing posts.

Use only the data in the file and do not invent missing values. First identify the available metrics and any missing or inconsistent fields. Rank the posts using [PRIMARY SUCCESS METRIC], with [SECONDARY METRIC] as a tie-breaker. If the data supports it, also show results by platform, post format, topic, publication day and publication time.

Return:
1. A table of the top [NUMBER] posts with post date, platform, post text or a short label, format, primary metric, secondary metric and any other relevant metrics.
2. The exact calculation or ranking rule used.
3. Three to five patterns that are supported by the data, separating observations from explanations.
4. Any posts that look unusually strong or weak because of small reach, missing data or an atypical result.
5. Three practical content recommendations based only on the analysis.
6. A short list of checks I must make against the original platform analytics before acting on the recommendations.

Do not call a post successful merely because it has a high raw count if its reach or impressions make comparison unfair. Say when posts cannot be compared fairly. Do not infer audience sentiment, sales impact or causation unless those measures are present in the file.

    Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

  2. Use a tool built for this

    Second choice

  3. Hand it to a person

    The distant third

    A person who owns the outcome does this end to end, worth it when the failure is dear.

What it gets wrong

  • AI cannot decide what best-performing means when engagement, reach, clicks, leads and sales point in different directions.
  • AI cannot reliably separate a strong post from a post boosted by paid distribution, a major news event or an unusually large audience unless those factors are in the data.
  • AI cannot know whether a high-performing post fits your current brand direction, audience strategy or campaign objective without that context.
  • AI can describe correlations in the export but cannot prove that a topic, format or posting time caused the result.

Even on a YES, the friction has a name: judgement under ambiguity and context depth.

How we scored this

Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.

AxisScore (0–2)
Output2
Inputs2
Verification2
Liability2
Effort delta2
Total10 / 10

The methodology and its thresholds are published in full.

FAQ

Can AI analyse my social media analytics?
Yes. Give it a clean export with post-level metrics and ask it to state its ranking rule, identify missing data and separate observations from explanations. Check every top-post value against the platform export.
What data does AI need to find my best social media posts?
It needs post identifiers or text, dates, platforms, formats and the metrics that matter to you, such as reach, impressions, engagement, clicks, saves or leads. Include enough context to distinguish organic results from paid or campaign activity.
Can AI tell me which type of social media post performs best?
It can compare formats, topics and publication details when those fields are present and consistently labelled in your data. It cannot prove that one feature caused better performance, so check the results against reach, promotion and campaign context.
Is AI reliable for choosing my social media strategy?
It is useful for finding patterns and reducing manual sorting, but the strategy decision remains yours. Use the platform's original analytics to verify the rankings and apply your knowledge of the audience, brand and current campaign.

Nearby answers

All Social media rulings

Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.

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