I analyzed my actual tweet engagement data to find the best times to post, and generic advice was completely wrong for me

You know how every blog post says "post on Tuesday at 10am" or "Wednesday afternoon is the best"? I was following that advice for months and my engagement was... mid. Nothing special.

So I decided to actually look at my own data instead of trusting generic articles. I have around 200+ tweets posted over the last few months, and I wrote a script that analyzes my actual engagement per hour and day of the week.

Not just counting likes either. I weighted different engagement types differently: bookmarks count 4x (someone saving your tweet is huge), retweets 3x, replies 2x, likes 1x, and impressions barely count because views without action don't mean much. I also accounted for tweet age so older tweets that had more time to collect engagement don't dominate the results.

The results surprised me honestly. My best performing window was Sunday evening and Monday early morning. Like, completely opposite of what most "experts" recommend. My worst day? Wednesday. The day everyone says is the best.

The thing is, it makes sense when you think about it. "Best time to post" is different for everyone because your audience is different. If your followers are mostly in Europe, Tuesday 10am EST means nothing. If your niche is developers, they're more active on weekends when they have free time. If your audience is marketers, Monday morning is when they're catching up.

I visualized it as a 7x24 heatmap (every hour of every day of the week) and the patterns became super obvious. You can see exactly where your engagement clusters. I also calculated confidence for each cell so I know if a high score is based on 20 tweets or just 2.

Some interesting patterns I noticed:

Most people have 2-3 "golden windows" per week, not a consistent daily pattern

Weekends are underrated, especially for B2B content (less competition in the feed)

The worst time is often when everyone else is posting (more competition = less visibility)

Your best hour can be 3-4x better than your worst hour. Thats a massive difference just from timing

How you can do this yourself:

Export your last 50-100 tweets with their engagement numbers (Twitter analytics CSV works)

For each tweet, note the day of week and hour it was posted

Group by day+hour and average the engagement

Weight bookmarks and retweets higher than likes - they signal much stronger interest

Look for clusters. You'll probably find 2-3 clear winners

Or if you're technical, the X API gives you all this data and you can build a simple heatmap with any charting library.

What times work best for you guys? Would be curious to hear if anyone else noticed that the generic advice didn't match their actual data. And if you try this method let me know what you find.

Comments

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Ordinary_Breath_87322 months ago1

the bookmark weighting is the most underrated part of this whole method honestly bookmarks are someone saying “I want to come back to this” which is way stronger signal than a passive like. the Sunday evening finding makes total sense too less noise in the feed means ur content actually gets seen. the generic Tuesday 10am advice is basically just an average of everyone’s data which means it’s optimal for nobody specifically. gonna try the heatmap approach on my own data

CammiQuinn2 months ago1

Try Metricool. It gives you a heat map of when your viewers are most likely online.

Independent-Ant-72302 months ago1

This is exactly the kind of thing most people skip and then wonder why “best time to post” advice doesn’t work.

I did something similar (not as detailed) and saw the same pattern. My “best” times were completely off from generic advice, and the gap between good vs bad slots was way bigger than expected.

The competition point is real too. When everyone posts at the “optimal” time, you’re fighting for attention in a crowded feed. Slightly off-peak often performs better.

Also agree on weighting engagement. Likes are cheap, saves/bookmarks and replies tell you way more about actual interest.

Big takeaway for me was there’s no universal best time, just your audience’s behavior. Once you find those 2–3 strong windows, it’s like a free boost without changing the content itself.

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