Work with the algorithm instead of guessing
You can't game the feed, but you can feed what it rewards: watch time, shares and sends, saves, and reach to people who don't follow you. The only real lever is testing your own cadence and reading the data.
AI can analyze your post metrics and surface the pattern; you paste the real numbers, because AI can't see your analytics and must never invent them.
Fastest path: one prompt, end to end
🤖 AI prompt — paste into ChatGPT / Claude
You are a social analytics strategist.
Paste 2 weeks of my post metrics (one row per post): date, format, topic, reach, % of reach from non-followers, watch time / avg view duration, shares/sends, saves, comments.
Analyze using ONLY the numbers I pasted (invent nothing):
1. Which posts earned the most non-follower reach, and what do they share (format, hook, topic, length)?
2. Which signal (saves vs shares/sends vs watch time) correlates most with reach for MY account?
3. Recommend a cadence and 3 specific format tweaks based on MY pattern, not generic advice.
4. If a column is missing or blank, tell me exactly where to find it in native analytics; do not estimate.
Output: pattern findings, then a cadence recommendation, then 3 tweaks. Use only the numbers I pasted.
No-web fallback: this prompt needs no browsing at all, because every input is a number you paste from your own analytics. If a metric is missing, the AI should ask you for it, never fill it in.
Or do it in 5 steps
- Know the 2026 signals. Feeds rank on watch time and retention, shares and sends (a send to a friend is the strongest signal, it counts as a recommendation), saves, and how far a post reaches beyond your own followers. Follower count and raw post volume barely move ranking; engagement quality does (Hootsuite's 2026 algorithm guide; Instagram's Adam Mosseri on sends).
- Optimize for shares and saves, not likes. Before you publish, ask one question: is this worth sending to a friend or saving for later? Content that earns sends and saves gets pushed to non-followers, which is where growth actually comes from now.
- Set up the metrics log. Open your native analytics (Instagram Insights, TikTok Analytics, YouTube Studio) and build one row per post: date, format, topic, reach, % reach from non-followers, average view duration, shares/sends, saves, comments. One spreadsheet is enough; you paste it into the prompt above.
- Run a 2-week cadence experiment. Hold format roughly constant, vary only posting frequency and timing, and log every post. Two weeks gives the algorithm enough behavior data to react and gives you enough rows to see a pattern. Consistency matters more than any magic time-of-day.
- Read your own data, then adjust. Your account's pattern beats any generic "best time to post". Double down on the format and cadence your non-follower reach rewards, and drop what only earned likes.
Worked example: a 2-week metrics log
Say two weeks of posting produced this (your real numbers go in each cell):
| Post | Format | Reach | % non-follower | Saves | Sends |
|---|
| How-to A | Reel | 12,400 | 68% | 210 | 95 |
| Behind-the-scenes | Reel | 4,100 | 22% | 30 | 12 |
| How-to B | Reel | 10,900 | 61% | 180 | 88 |
The two how-tos pulled the most non-follower reach and the most saves; behind-the-scenes barely left your follower base. The signal is clear: make more saveable how-tos, and treat saves (not likes) as your north-star metric. Let your data, not a blog's benchmark, set the plan.
Re-read your metrics every 2 weeks; the algorithm and your audience both shift.