Study lesson

The 100K Follower Posting Schedule

Most people get bored of posting before the algorithm gets to know them. This schedule isn't about working harder — it's about working at the right times, batching the work into one or two days a week, and using the first 60 minutes after every post to maximise the algorithmic snowball. Steal it. Run it for 90 days. See what happens.

The 100K Follower Posting Schedule

1. Daily posting frequency

Frequency is the entry ticket. Below these numbers, the platforms barely know you exist. Above them, you start to overlap with your own audience and erode reach. These are the proven sweet-spot ranges.

2. Best posting times (local)

Engagement spikes match the natural rhythms of when people pick up their phones. Night slot has the highest dwell time because people scroll in bed without distractions. Pick the slot you can hit consistently — the algorithm rewards regularity more than perfection.

3. Weekly content mix

The most common growth-killer is being 90% educational or 90% promotional. The 40/30/20/10 mix builds trust, personality, and authority in parallel — without burning out either you or the audience.

4. The batching system

Batching breaks the daily-creation hamster wheel. Four focused days a week produces more content than seven scattered ones. Weekends become engagement-only, which is exactly when most of your audience is actually online.

5. Engagement routine (first 60 minutes after posting)

The first 60 minutes is the algorithmic test window. Platforms decide who else gets to see your post based on engagement velocity in that window. Treating it as sacred is the single biggest reach hack.

6. Growth accelerators

Each one of these is a 10–15% leverage point on its own. Stacked together, they're the difference between accounts that plateau and accounts that compound.

7. Monthly content calendar structure

A repeatable monthly rhythm stops you reinventing the wheel every week. By Week 4, you know exactly what to make because the previous 3 weeks have generated the raw data — top performers get amplified, weak ones get retired.