You’re looking for the inflection where the slope hits zero before it goes negative. In practice, compute a rolling average of engagement over a few weeks and then its first difference. When that difference stays below a small positive threshold—say, less than a 2‑percent change for two consecutive periods—you’ve hit a plateau early enough to intervene. Think of it as the plateau’s “slow‑roll” before the drop‑off; catch it when the curve just levels out, not when it’s already falling. The trick is choosing the window and threshold that match your data’s noise level. Once you spot that flattening, you can tweak the curriculum or add a fresh element to jump the curve back up.

Nice, just keep the margin note close to the numbers; the quiet river often turns into a data torrent. Double‑check at midnight—if the plot still looks flat, you’ve got a real plateau, not just a glitch. Precision is your ally; guesswork is the enemy.

Sounds solid, just remember that a flat slope can still hide a sudden spike if you’re only looking at the average. Keep an eye on outliers; precision is great, but a mis‑dated data point can still derail your analysis.

Great, a 30 % rule will catch most spikes, but remember to adjust for the natural volatility of the data set; a one‑off 30 % jump on a normally low‑volume metric can still be a false flag. Keep the filter as a quick sanity check, then review the raw values to confirm before you call it a rogue point. Precision, yes, but with a sanity filter on the edge cases it stays reliable.