How to find your own best time to post on LinkedIn from your analytics export
TL;DR: Use your own LinkedIn analytics export, not a generic timing chart, to find repeat posting windows that correlate with stronger engagement. Clean the file into one row per post, bucket posts by hour and day, compare those buckets against engagement, sanity-check the pattern, then feed the winning window back into TimeToPost or test it in future posts.
What to export from LinkedIn before you start
You do not need a giant spreadsheet museum. For this analysis, export only the fields that let you answer one question: which LinkedIn posting times line up with stronger engagement in your own history?
Keep these columns if you have them:
- post publish date and time
- engagement metric(s) you care about, such as likes, comments, shares, clicks, or views
- a row label or content field that clearly identifies the row as a LinkedIn post
- any post id or URL that helps you spot duplicates
If your export includes extra stuff, fine. Just do not let it bully you.
One important catch, TimeToPost records metrics only for posts it publishes, so any historical LinkedIn analytics you want to analyze must come from the source export, not from TimeToPost’s records. TimeToPost’s own analytics feature computes best posting times from your engagement history, and its API exposes that through GET /api/analytics/optimal-times?platform=linkedin when the plan includes the best-time-to-post feature, per the TimeToPost API reference and scheduling docs.
Prepare the export for timing analysis
Clean the file into a simple table with one row per post. That is the whole game.
Do this before you bucket anything:
- remove non-LinkedIn rows
- remove totals, summaries, and duplicate exports
- make sure each post appears once
- convert timestamps into one consistent format
- convert everything to a single timezone
Why the timezone fuss? Because 9:00 AM in one place is not 9:00 AM everywhere else. If you compare mixed timezones, your spreadsheet starts lying with a straight face.
A clean table usually looks like this:
| post_id | published_at | likes | comments | shares | clicks |
|---|---|---|---|---|---|
| 101 | 2026-04-07 08:15 | 22 | 4 | 2 | 11 |
| 102 | 2026-04-08 08:20 | 31 | 5 | 1 | 13 |
| 103 | 2026-04-09 13:05 | 12 | 1 | 0 | 6 |
If your export is messy, clean it first. Timing analysis built on sloppy timestamps is just astrology with columns.
Bucket posts by hour and day
Now group each post into two buckets:
- hour of day, such as 08:00, 09:00, 10:00
- day of week, such as Monday, Tuesday, Wednesday
That gives you two useful views without overengineering the spreadsheet into a science project.
In a spreadsheet, you can add helper columns like:
=HOUR(B2)for hour-of-day=TEXT(B2,"dddd")for day-of-week
If your tool uses different formulas, that is fine. The point is the grouping, not the ceremony.
Compare the buckets against engagement, not vanity volume
The goal is not to find where you posted the most. It is to find where your own posts performed better.
So pick one engagement metric, or one summary metric if you have one, and summarize by bucket. Then look for repeated winners.
For example, sort or pivot by:
- average likes per hour
- average comments per day
- total engagement per hour
- engagement rate per post, if your export gives you enough data to calculate it
A single spike does not prove much. One post going weirdly hard at 2 PM does not mean 2 PM is magic. It may just mean the post was unusually good, or your audience was caffeinated, or the algorithm felt generous. The spreadsheet does not know. It just reports.
What you want is a bucket that shows up more than once with stronger engagement, especially across multiple posts and not just one vanity geyser.
If you want a simple follow-up test after this export pass, the linked guide on how to A/B test your posting times instead of trusting a generic "best time to post" chart is a good companion, because it turns the export-based guess into a repeatable check.
Worked example: turn a LinkedIn export into a posting window
Here is a small hypothetical example.
| post | published_at | day | hour | likes | comments |
|---|---|---|---|---|---|
| A | 2026-05-06 08:10 | Wednesday | 8 | 18 | 3 |
| B | 2026-05-13 08:25 | Wednesday | 8 | 21 | 4 |
| C | 2026-05-20 08:05 | Wednesday | 8 | 19 | 2 |
| D | 2026-05-07 13:15 | Thursday | 13 | 8 | 1 |
| E | 2026-05-14 13:00 | Thursday | 13 | 9 | 0 |
A quick summary shows that Wednesday at 8 AM appears three times and each of those posts performs better than the Thursday 1 PM group.
Verifiable draft rule:
- post LinkedIn content on Wednesdays between 8:00 and 9:00 AM
- treat Thursday 1 PM as a weaker backup slot, not the primary window
That is not a law of nature. It is a draft schedule rule based on your own export, which is the only kind of timing advice that actually has skin in the game.
Sanity-check the result before you treat it as your best time
Before you crown a winner, sanity-check it.
Ask three questions:
- Does the same window show up across enough posts, or is it only one lucky week?
- Do weekdays differ from weekends in a meaningful way?
- Does the pattern still hold after removing outliers or unusually promotional posts?
That last one matters because promotional posts often behave differently from normal posts. If one launch announcement dominates the data, it can drag the whole chart around like a shopping cart with one bad wheel.
If the result disappears once you remove outliers, that is useful information too. It means you do not yet have a stable pattern.
How to put the result back into TimeToPost
Once you have a candidate LinkedIn window, you can feed that thinking back into TimeToPost. The product’s best-time-to-post feature computes optimal times from your own engagement history, and the docs say GET /api/analytics/optimal-times?platform=linkedin returns best hours and days for LinkedIn when your plan includes that feature, with bestHours, bestDays, and dataPoints in the response, per the API reference.
If you are using the dashboard, TimeToPost also surfaces suggested slots in the composer based on the same idea, your own engagement data instead of generic averages, according to the scheduling docs.
If you want to automate the workflow in an agent or client, the MCP docs are relevant because the server includes a get_optimal_times tool and the hosted MCP server can read best posting times from your engagement history.
If your export is too thin to trust yet
Sometimes the export just is not enough.
If you only have a handful of posts, or the timestamps are inconsistent, the safest move is to keep collecting data and revisit the analysis later. A tiny sample can point you in the right direction, but it cannot honestly claim to know your best LinkedIn time with much confidence.
That is not failure. It is the spreadsheet saying, politely, “come back when you have more evidence.”
Next step: save the chosen slot and test it
Pick one or two candidate LinkedIn windows from the export, schedule future posts there, and compare the next round of results to confirm or adjust the pattern.
That is the loop:
- export your own data
- bucket by hour and day
- compare against engagement
- choose a draft posting window
- test again with fresh posts
If the pattern holds, great. If it shifts, great again. You are learning something real instead of borrowing someone else’s chart and hoping your audience got the memo.
Related links:
- What to export before canceling your scheduler subscription, useful if you want a broader export checklist before switching tools.
- How to a/b test your posting times instead of trusting a generic "best time to post" chart, useful if you want to validate the export-based result with a simple follow-up test.