Shruti Kaushik

LinkedIn21 min read

Best Time to Post on LinkedIn for B2B in 2026: 18 Datasets Compared

Best time to post on LinkedIn for B2B in 2026, compared across Buffer, Sprout, Metricool, SocialPilot and 14 more datasets, with India and US time zone conversions.

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Chapters
  1. 01What is the best time to post on LinkedIn for B2B? The short comparison
  2. 02Which datasets are actually primary, and which are repackaged?
  3. 03What does Sprout Social's 307,000-profile dataset say for B2B?
  4. 04Why does Buffer's 4.8 million posts point to the afternoon?
  5. 05What do Metricool, SocialPilot, SocialBee and RADAAR add?
  6. 06What does the data say about weekends?
  7. 07Does account type change the answer more than the hour?
  8. 08What do the independent studies say when they ignore timing averages?
  9. 09What data disagrees on, and how to reconcile it
  10. 10What should B2B marketers do about time zones, and how do India and US windows convert?
  11. 11How much does the first day matter, and why does it justify a time rule?
  12. 12Shruti's analysis: what the 18 datasets mean for a B2B founder
  13. 13A six week test to find your own best time
  14. 14Frequently asked follow-ups
  15. 15Methodology
  16. 16Sources

For B2B in 2026, post Tuesday to Thursday between 10 a.m. and 2 p.m. in your buyers' local time, and pick the slot where you can stay online for the next hour. The largest datasets disagree by up to seven hours, so treat this as a starting window to test, not a rule.

That answer is the overlap of the studies, not a finding from any one of them. This guide lays out each dataset with its sample size, date and weakness, shows where the studies contradict each other, converts the windows for India and US audiences, and ends with a labelled analysis section and a six week test. It sits alongside the LinkedIn statistics for 2026 and the posting frequency data.

#What is the best time to post on LinkedIn for B2B? The short comparison

No study in this set publishes a clean B2B versus B2C split. What exists is a mix of platform-wide vendor datasets, industry slices, account-type slices and a few small independent analyses. This table puts the headline claim of each next to its sample.

What is the best time to post on LinkedIn for B2B? The short comparison
Source Sample Data period Best days Best hours (local time) Main caveat
Sprout Social ~2 billion engagements, 307,000 profiles, six networks 27 Nov 2025 to 27 Feb 2026 Tue to Thu 11 a.m. to 5 p.m. Tue, 11 a.m. to 4 p.m. Wed LinkedIn-only count not disclosed
Buffer 4.8 million posts Not stated Wed, then Thu, Fri 3 to 8 p.m.; Wed 4 p.m., Fri 3 and 4 p.m. Scheduled posts only, no date range
Hootsuite 1 million+ posts, 118 countries (per a comparison) Page dated 19 Nov 2025 Tue, Wed 8 to 9 a.m. Same page also mentions 4 to 6 a.m.
Metricool ~674,000 posts, 63,000+ accounts Period not stated Tue to Thu 9 a.m. to 12 p.m., peak 10 to 11 Metricool users only
SocialPilot 683,000+ posts, 47,672 accounts Not stated Tue to Thu 10 a.m. to 12 p.m., 1 to 4 p.m. Labelled Eastern Time, no weighting
SocialBee 494,774 posts Six months to Sep 2026 Wed (interactions), Thu (rate) 7 to 9 a.m., Wed 8 a.m. top SocialBee users only
RADAAR 2.6 million posts Not stated Tue, Wed 1 to 4 p.m. Methodology undisclosed
AuthoredUp ~3 million posts Updated weekly Under 10% gap for profiles Heatmap by audience location Paid tool, no static answer
MagicPost (India) 220,220 posts To May 2026 Mon, midweek 8 to 9 a.m. IST Scores relative to author average
Flux analysis 7,913 posts, 63 profiles Jun 2025 to Jun 2026 Flat, 0.99x to 1.02x Not analysed Small sample
Postiv 62,820 posts, 1,340 creators 1 Jan 2025 to 30 Sep 2026 Mon 1.05 highest (UTC) Not analysed Creator library, UTC days

The remaining seven sources (TimeToPost, Kapwing, Adobe Express through a comparison page, Snov.io, eClincher, Kontentino and Pollen) are small, modelled, self-reported or aggregated. They appear in the sections where they add something, and in the Sources list with their weaknesses stated.

#Which datasets are actually primary, and which are repackaged?

Before comparing numbers it helps to sort the pile, because several pages that rank for this query only republish other people's figures.

First-party platform-scale data (vendors with their own posts or engagements):

Independent or small-sample analyses:

Modelled, surveyed or aggregated (not performance data):

  • TimeToPost's India B2B SaaS page is explicitly "modelled not counted" and built from published research.
  • Snov.io and eClincher compile other vendors' findings; eClincher cites more than 8 million posts combined but gives no per-study figures.
  • Adobe Express's piece, as summarised by a comparison page, is a survey of 607 self-reported respondents, not engagement data.
  • Kontentino's tool states "2025 data" and no sample.

The practical consequence is that "Tuesday to Thursday, mid-morning" appears on dozens of pages, but it traces back to about six first-party datasets. Counting pages is not counting evidence.

#What does Sprout Social's 307,000-profile dataset say for B2B?

Sprout is the most useful single source for B2B because it publishes industry pages. Its overall LinkedIn table, in local time, shows:

  • Monday: 1 to 2 p.m.
  • Tuesday: 11 a.m. to 5 p.m.
  • Wednesday: 11 a.m. to 4 p.m.
  • Thursday: 11 a.m., then 1 to 5 p.m.
  • Friday: 11 a.m., then 1 to 2 p.m.
  • Saturday and Sunday: none recommended.

The industry slices are where the B2B signal sits. For Technology and Software, Sprout lists Monday 12 to 1 p.m., Tuesday 10 a.m. to 2 p.m., Wednesday 10 a.m. to 4 p.m., Thursday 10 a.m. to 3 p.m. and Friday 11 a.m. to 1 p.m. plus 3 p.m. For Financial Services, the windows are Monday 10 a.m. and 12 to 1 p.m., Tuesday 10 a.m. to 3 p.m., Wednesday 8 a.m. and 10 a.m. to 3 p.m., Thursday 8 a.m. to 2 p.m. and Friday 11 a.m.

Two things stand out. Financial Services opens earlier than Technology on Wednesday and Thursday (8 a.m. versus 10 a.m.), and Technology runs later on Wednesday (4 p.m. versus 3 p.m.). Sprout's tech page attributes the Wednesday and Thursday early-afternoon peaks to overlapping workdays across time zones, which is the vendor's reading and not a tested result.

For comparison, the consumer-facing slices differ in shape, not in direction. Retail lists Thursday 8 a.m. to 3 p.m. and Friday 9 a.m. to 12 p.m. plus 2 to 3 p.m. Travel and Hospitality lists Tuesday 10 a.m. to 2 p.m. and Friday 11 a.m. plus 1 to 3 p.m. The weekday pattern is the same for every industry, which means Sprout's data does not support a large B2B versus B2C timing gap. It supports a small shift of an hour or two.

Two caveats from Sprout's own page: the dataset spans six platforms and the LinkedIn-only count is not stated, and the page does not describe the statistical method behind the windows.

#Why does Buffer's 4.8 million posts point to the afternoon?

Buffer's analysis is the outlier, and the most important one to understand. It reports a peak window of 3 to 8 p.m. on weekdays with Wednesday 4 p.m., Friday 3 p.m. and Friday 4 p.m. as the top slots. Its day ranking puts Wednesday first, then Thursday, then Friday, with Monday lowest and Tuesday close behind. Its per-day top three include late evening slots, for example Monday 10 p.m. and Sunday 10 p.m.

Three details matter for how much weight to give it:

  • Buffer says it normalised to the reader's local time, so a 4 p.m. recommendation means 4 p.m. wherever the audience is.
  • The page gives no raw engagement rates for hours or days, no date range for the 4.8 million posts, and no industry split.
  • The sample is posts scheduled through Buffer. People who schedule are disproportionately brands, agencies and creators, whose audiences may check LinkedIn after work.

A second-hand comparison page dates the Buffer study to 22 July 2026, while the Buffer page itself shows 9 September 2026. I use the page's own date and flag the mismatch, which suggests the article was updated between those dates.

The same Buffer page adds that morning engagement from 6 to 11 a.m. is lower than the afternoon and evening, which directly contradicts Hootsuite's 8 to 9 a.m. headline and SocialBee's 7 to 9 a.m. result.

#What do Metricool, SocialPilot, SocialBee and RADAAR add?

These four mid-sized datasets all come from scheduling vendors, and they split into a morning group and an afternoon group.

Morning group. Metricool's article, built on its 2026 LinkedIn study, reports the best posting between 9 a.m. and 12 p.m. with a peak around 10 to 11 a.m., Tuesday to Thursday strongest, activity dropping from 6 p.m. and on weekends. It did not state the period or time zone treatment. SocialBee's 494,774 posts found Wednesday 8 a.m. the top slot, 7 to 9 a.m. the strongest window on any day, and morning engagement rates of 6.6% to 8% against about 3.5% to 5% around lunch and later afternoon. SocialBee also flagged 12 to 2 p.m. as a time to avoid, with 20% to 30% fewer interactions than morning posts. That directly contradicts Sprout's inclusion of midday in its peak window.

Midday and afternoon group. SocialPilot's 683,000+ posts give a headline window of 10 a.m. to 12 p.m. and 1 to 4 p.m., Tuesday to Thursday, labelled Eastern Time. RADAAR's 2.6 million posts name Tuesday and Wednesday 1 to 4 p.m. as best and claim weekdays see about 80% of user activity, though the page discloses no method and no date.

SocialPilot's industry list is the closest thing to a B2B menu in the vendor set, though it gives no per-industry samples: Technology and Software Tuesday to Thursday 9 to 11 a.m., Finance Tuesday and Wednesday 7 to 11 a.m., Marketing Tuesday to Thursday 9 a.m. to 12 p.m., Manufacturing Tuesday and Wednesday 8 to 9 a.m., Legal and Consulting Tuesday and Thursday 9 to 11 a.m.. It also says Monday performs poorly for many B2B brands.

Notice the pattern: finance and manufacturing windows start earlier than tech and marketing windows in both Sprout and SocialPilot. Two independent vendors agreeing on the direction of that gap is stronger evidence than either agreeing on an exact hour.

#What does the data say about weekends?

Almost every vendor tells B2B marketers to avoid weekends, and the independent data is less sure.

The accurate summary: the vendor datasets that include company pages show weekends as weaker, and the datasets built on individuals show them as near neutral. That fits the account-type finding in the next section.

#Does account type change the answer more than the hour?

It may be the biggest swing in the whole literature, and it is the finding most pages bury. AuthoredUp's analysis, as reported by a comparison page, found about 1.7 times more day-to-day variation between best and worst day for Company Pages than for personal profiles, where the gap was under 10%. AuthoredUp's own tool also separates typical profiles, the top 5% of profiles and company pages because the patterns differ, built on about 3 million posts.

Metricool's 2026 study supplies the context for why. Personal profiles average 3.05 posts per week against 2.34 for Company Pages and drive 237% more comments per post, while Company Pages generate 1.16 shares per post against 0.08 for profiles. Average impressions per post are near-equal: 831.96 for Company Pages and 817.67 for personal profiles. Personal profiles show a 63% higher engagement rate.

For a founder, that means the pooled vendor averages are weighted by whichever account type dominates the vendor's user base. A scheduling tool full of brand pages produces a "brand page" answer. If you post from a personal profile, the weak-weekend and strong-Wednesday advice matters less than vendors imply.

#What do the independent studies say when they ignore timing averages?

Two datasets test the premise that a universal best time exists.

The first is the 7,913-post analysis of 63 profiles from June 2025 to June 2026, Eastern Time adjusted. In aggregate the day effects were near flat, between 0.99x and 1.02x. But 50% of individual profiles had a gap of 2x or more between their best and worst day, with a median gap of 2.0x and a maximum of 8.5x. Friday was the most common best day at 20% of profiles, Tuesday 18%, Saturday 15%, Sunday and Monday 13% each, Wednesday and Thursday 11% each. The author cautions that the largest gaps sit with infrequent posters, so some are noise.

The second is Postiv's 62,820 posts in UTC days: Monday 1.05, Tuesday 1.00, Wednesday 1.00, Thursday 0.99, Friday 0.98, Saturday 0.96, Sunday 1.00. A 1.05 versus 0.96 range is a 9% spread, and the strongest day is Monday, the weakest day in Buffer's ranking.

Read together, these say the aggregate day effect is small, individual variation is large, and the sign of the effect is not stable across datasets. Kapwing's informal review of one CEO's account saw Thursday strongest and its best posts clustered between 7 and 11 p.m. Pacific, with a Wednesday 4:25 p.m. post winning on impressions. One account is an anecdote, which is exactly the point: any single account can sit anywhere in the distribution.

#What data disagrees on, and how to reconcile it

The conflicts that matter for a B2B decision, with the most plausible reconciliation for each.

1. Morning versus afternoon. Hootsuite names 8 to 9 a.m., SocialBee 7 to 9 a.m. and Metricool 10 to 11 a.m.. Sprout says 11 a.m. to 5 p.m., RADAAR 1 to 4 p.m. and Buffer 3 to 8 p.m. Reconciliation: the metrics differ. SocialBee reports engagement rates on impressions, so early posts that reach fewer people but with high rates can win. Buffer's and Sprout's totals reward volume of interactions. A founder with a small, loyal audience resembles the first case.

2. Whether midday is good or bad. Sprout's 11 a.m. to 5 p.m. windows overlap what SocialBee calls the weakest time, 12 to 2 p.m. Neither page discloses who its users are, so the cause cannot be isolated. The disagreement is real and unresolved.

3. Which day is best. Wednesday leads in Buffer, SocialBee (interactions) and RADAAR; Thursday leads in SocialBee (rate) and Kapwing's account; Monday leads in Postiv's UTC data and MagicPost's India scoring. These differences are mostly inside the noise: SocialBee's best day had 13.8 interactions per post against roughly 13 to 13.5 for the others, a gap of about 2% to 6%.

4. Weekdays versus weekends. Covered above: pooled vendor sets say weak, individual-normalised sets say neutral.

5. The 36% number. In the video literature a "36%" figure is attached to opposite claims, which is a reminder to check what each percentage measures. It is not a timing issue, so it is dealt with in the companion piece on carousel, text and video performance.

6. Dates and vintage. Buffer's page date conflicts with a secondhand date, SocialPilot's page carries leftover 2023 and 2024 metadata, RADAAR shows no date, and Metricool's study compares only January and February of two years. The seasonal skew from a two-month window is unmeasured. Treat every figure as a snapshot, not a trend.

#What should B2B marketers do about time zones, and how do India and US windows convert?

Most large vendors say they report local time, so the instruction is: schedule for the reader's clock, not yours. Sprout says all times are in each audience's own time zone. Buffer says it normalised to local time. SocialPilot labels its main table Eastern Time with no stated method for weighting audiences, and Postiv's weekday table is in UTC, so those two should not be applied blindly to other regions.

The conversions below are my arithmetic, not a published finding. India Standard Time is UTC+5:30. US Eastern is UTC-4 until the clocks change on 1 November 2026 and UTC-5 after; Pacific is UTC-7 then UTC-8.

What should B2B marketers do about time zones, and how do India and US windows convert?
Consensus window, local time US Eastern in IST (EDT / EST) US Pacific in IST (PDT / PST) UK in IST (BST / GMT)
8:00 a.m. 5:30 p.m. / 6:30 p.m. 8:30 p.m. / 9:30 p.m. 12:30 p.m. / 1:30 p.m.
10:00 a.m. 7:30 p.m. / 8:30 p.m. 10:30 p.m. / 11:30 p.m. 2:30 p.m. / 3:30 p.m.
12:00 p.m. 9:30 p.m. / 10:30 p.m. 12:30 a.m. / 1:30 a.m. 4:30 p.m. / 5:30 p.m.
2:00 p.m. 11:30 p.m. / 12:30 a.m. 2:30 a.m. / 3:30 a.m. 6:30 p.m. / 7:30 p.m.

The consequence for an India-based founder selling to US buyers is uncomfortable. A US Eastern 10 a.m. to 2 p.m. window is 7:30 p.m. to 11:30 p.m. IST while US daylight time lasts. Only an evening or late-night posting routine reaches the US midday peak live, and that is where scheduling and a plan for replies matter most.

For India-focused audiences, the evidence is thin and conflicting:

  • MagicPost's 220,220 Indian posts, scored 0 to 100 relative to each author's average, rank Monday 9 a.m. IST top at 100, with Monday 8 a.m. and Wednesday 8 a.m. at 96, Friday 8 a.m. and Monday 10 a.m. at 88, 8 a.m. the best hour overall at 81 and evenings on a plateau of about 62 to 63. It is vendor data, and its sampling and scoring are not explained.
  • TimeToPost's India B2B SaaS baseline recommends Tuesday to Thursday, roughly 9:30 to 11 a.m. IST for domestic content and 6:30 to 9 p.m. IST for US-facing content. Its own chart peaks, converted from UTC, fall at 2:30 p.m. IST (score 100), 3:30 p.m. (96) and 4:30 p.m. (84), which contradicts the narrative, and the page admits the data is modelled, not counted.
  • SocialPilot's country section labels some times "IST," but the label is ambiguous because the abbreviation also fits Irish and Israeli time, and the page does not explain its conversions. I did not use its country figures.

India's two sources agree on weekday mornings for domestic reach and disagree on everything else. Neither is a measurement you can audit.

For US audiences, the six first-party sets converge on late morning to mid afternoon Eastern, with Buffer pulling toward 4 p.m. For Pacific audiences, the same local-time logic applies, but note Kapwing's CEO account, where evening Pacific posts did well. For UK and European audiences there is no specific published dataset in this set, so the local-time rule is the only safe guidance.

#How much does the first day matter, and why does it justify a time rule?

Timing advice exists because early engagement shapes distribution. Metricool's 2026 study gives numbers: about half of lifetime impressions occur within 48 hours, and the day-one share of interactions is about 40% for personal profiles and about 35% for Company Pages. The same release reports that a direct question in a post lifts comments by 77% and an explicit request to comment lifts them by 80%.

That connects timing to behaviour you control. If 40% of interactions arrive on day one, the first hour determines how many of them you can influence by replying. Combine those and the rule is: choose a slot you can attend, not the slot a heatmap picks.

#Shruti's analysis: what the 18 datasets mean for a B2B founder

This section is my own synthesis. It uses only figures quoted above and introduces no new data.

Finding 1: There is a defensible weekday consensus, and it is narrower than the pages claim. Counting the datasets that name a specific window, Tuesday, Wednesday and Thursday appear in the headline recommendation of Sprout, Metricool, SocialPilot, Hootsuite (Tuesday and Wednesday), RADAAR (Tuesday and Wednesday), Snov.io and eClincher. Buffer ranks Wednesday and Thursday first and second. SocialBee's best day by interactions is Wednesday and by rate Thursday. That is why I would put Monday and Friday second for B2B: Buffer ranks Monday lowest and Sprout's Monday windows are narrow, though Postiv's UTC data and MagicPost's India scoring are real counterexamples.

Finding 2: The hour is the contested variable, and it sorts by metric. The morning datasets (Hootsuite, SocialBee, Metricool) emphasise rate-based measures, and the afternoon datasets (Sprout, RADAAR, Buffer) emphasise interaction totals. If your objective is comments from a small set of relevant buyers, the rate-based morning evidence is closer to your situation. If you are posting to a broad audience for reach, the afternoon evidence applies. I would not average these into one hour.

Finding 3: The account-type gap is the most underweighted result. A personal profile varies under 10% across days in AuthoredUp's data, while a Company Page varies about 1.7 times. A founder posting from a personal profile can spend nearly no effort on weekday optimisation and should spend it on reply timing and format. A company page owner should plan around midweek.

Finding 4: For a B2B founder, buyer time zone beats everything. The conversions above show a US-facing founder in India must choose between live replies and the US peak. My recommendation is to post at the buyer's late morning and accept that replies will happen in your evening, or to post at your own morning and accept that US buyers see it as an evening post. Neither choice is supported by direct evidence, so it needs testing.

Finding 5: Most of the published disagreement is about measurement. Rate versus count, personal versus page, vendor user base, and local time versus UTC explain most of the spread. No dataset here controls all four.

What I would not claim. I would not claim that any single hour has a measured B2B advantage, that India has a proven best hour, or that weekends are dead for individuals. The evidence supports none of those.

For the broader question of what to post once timing is settled, see the founder LinkedIn content and pipeline data and the ghostwriting guide for founders. For cadence, see how often founders should post.

#A six week test to find your own best time

The method is mine, designed around the variation findings above.

  1. Pick three slots. Use the consensus window in your buyers' time zone: for example early morning, late morning and early afternoon, on Tuesday to Thursday.
  2. Rotate them. Over six weeks, with three posts a week, each slot gets six posts on varied days, so no slot owns a day.
  3. Hold other variables fixed. Keep format mix and topic mix stable, because Postiv and the Flux analysis both show format effects of the same order as day effects.
  4. Score against your own median. Use each post's engagement divided by your median post, the same lift method the Flux analysis and Postiv used, because it removes audience size.
  5. Record comments separately. Metricool found personal profiles drive far more comments than Company Pages, and comments are the pipeline-relevant signal.
  6. Decide only with a clear gap. The Flux author warns that large individual gaps are most common among infrequent posters and may be noise. With six posts per slot, treat a difference as real only if it is large and repeats across weeks.

#Frequently asked follow-ups

Is Tuesday really the best day? Tuesday is in most headline windows but Buffer ranks it near the bottom with Monday, and Postiv's UTC data scores it 1.00, the median. Wednesday is the safer choice.

Does the 8 a.m. advice work for B2B? Hootsuite, SocialBee and MagicPost support it; Buffer's morning data contradicts it. If you can reply at 8 a.m., test it.

Do hashtags, links or format matter more than timing? In Metricool's data, at least one hashtag was associated with 87% more impressions for Company Pages and 85% for personal profiles, while links cut impressions by 27% on personal profiles and added 51% on Company Pages (Metricool). These are larger numbers than any day effect above, though they come from one vendor's dataset.

#Methodology

I searched for 2025 and 2026 LinkedIn timing studies on 30 October 2026 and read each page directly where the fetch tool allowed. Sources were included if they published a sample size, a date, or enough method to judge them. I classified each as first-party platform-scale, independent small sample, modelled or surveyed, or aggregated. I did not average results across studies, because the sources use different metrics (engagement rate, interactions, relative scores) and different time handling.

Limits you should know about:

  • Sprout Social, Hootsuite and AuthoredUp pages were partly blocked during this session. For Sprout I read the industry subpages. For Hootsuite and AuthoredUp I relied on the published comparison by SocialCrawl and on figures I recorded from the same pages when I checked them on 16 October 2026 for a related article.
  • Several pages carry inconsistent metadata, noted where relevant (SocialPilot's older-year tags, a Socialinsider sample count that differs between its intro and tables in the companion article, the Buffer date mismatch).
  • Almost every dataset is from a social media tool, so account types skew toward users of those tools.
  • No source is a B2B-only dataset. The B2B segments rely on industry proxies from Sprout and SocialPilot.
  • Time zone conversions and the consensus tally are my own calculations from the cited windows. The date range of evidence runs from 2025 into September 2026.

Nothing here is based on client data or paid reports. All numbers come from the pages listed below.

#Sources

Quick questions

What is the best time to post on LinkedIn for B2B in 2026?

Tuesday to Thursday, roughly 10 a.m. to 2 p.m. in your buyers' local time, is the window most datasets overlap on. Sprout Social's 307,000-profile dataset puts Tuesday and Wednesday strongest from 11 a.m., Metricool's heatmap peaks at 10 to 11 a.m., and SocialPilot names 10 a.m. to 12 p.m. Buffer's 4.8 million posts are the main exception and favor 3 to 8 p.m.

Is the best time to post on LinkedIn different for B2B and B2C?

No published study in this comparison splits B2B from B2C directly. The closest proxy is Sprout Social's industry pages, where Technology and Software peaks Tuesday 10 a.m. to 2 p.m. and Wednesday 10 a.m. to 4 p.m., while Financial Services starts earlier, at 8 a.m. on Wednesday and Thursday. All of them list weekends as weak.

What is the best time to post on LinkedIn in India (IST)?

MagicPost's analysis of 220,220 Indian LinkedIn posts scores Monday 9 a.m. IST highest, with 8 a.m. the best hour overall. That is vendor data with no disclosed sampling, and a modelled B2B SaaS baseline from TimeToPost disagrees with it, so treat 8 to 11 a.m. IST on weekdays as a test window rather than a rule.

Does the day of the week matter on LinkedIn?

Less than the hour for personal profiles, more for company pages. AuthoredUp found under 10% between the best and worst day for personal profiles but roughly 1.7 times for Company Pages. A 7,913-post study normalised to each profile's median found day-of-week effects between 0.99x and 1.02x.

Should I post when my audience is online or when the data says?

Post in your audience's local time zone and pick a slot where you can reply for the next hour. Every large vendor study except one normalises to local time, and Metricool's data shows about 40% of a personal profile post's interactions arrive on day one.

How should a founder test their own best time to post?

Run a six week test with three fixed slots, rotate them across Tuesday to Thursday, keep format and topic mix constant, and compare each post to your own median. The method is described in the Shruti's analysis section below.

About the author

runs Wise Mango, a LinkedIn ghostwriting studio for SaaS and AI founders, and Klype (klype.ai), an AI platform that writes in a founder’s voice. She wrote The LinkedIn Blackbook and was named one of Forbes Game-Changing Leaders in 2025. Every figure on this page links to its source.