Data-backed research, controlled experiments, and practical growth hacks built from measurable signals—not myths.
TikTok’s algorithm can’t be “seen” directly. It can only be inferred through patterns: retention curves, engagement timing, audience matching, and long-term account behavior. AlgoRanker turns those patterns into clear, testable guidance.
This is the core mental model: most videos die early; some get re-tested; a few expand rapidly when early signals are unusually strong.
This site treats TikTok growth as a measurement problem. We track public outcomes (views, likes, comments, shares), analyze timing and ratios, and run controlled experiments to separate correlation from causation when possible.
We explicitly label what is merely associated vs what was tested in controlled conditions.
We prioritize percentiles and variability because virality is heavy-tailed.
Posting time, hook style, and engagement ratios behave differently across niches.
A transparent dataset overview is the fastest way to earn trust—because it shows where the conclusions come from.
TikTok doesn’t optimize for “views” in isolation. Observed behavior is consistent with optimizing the probability of sustained user attention—approximated through several measurable signals.
Watch time relative to length, completion rate, and early drop-off are consistently predictive across niches.
Repeat viewing and “silent” signals (like saves) often correlate with longer distribution windows.
Early engagement speed (timing) tends to matter more than total engagement volume—especially in the first hour.
When possible, we test the same content across multiple accounts, timing windows, and engagement conditions.
Hacks include measurable outcomes: effect ranges, confidence levels, and when the hack does not apply.
No “secret sauce” claims. Just probabilities, trade-offs, and practical guidance creators can verify.
How well delivery patterns avoid detection-like anomalies (timing, ratios, and behavioral consistency).
Whether a boost increases the probability of organic amplification (measured, not assumed).
How closely growth matches natural distributions: delivery speed, ratio coherence, and engagement curves.
Account realism signals: age distribution, activity patterns, profile completeness, and behavioral variety.
Retention over time: drop-off rates, reversals, and long-term effects on account-level metrics.
TikTok’s algorithm cannot be directly observed. It can only be inferred through repeated measurement. We combine large-scale tracking with targeted tests to estimate which signals matter most, and when.
The goal is not to claim certainty. The goal is to give creators the highest-probability path, with clear trade-offs.
Start with data-backed growth hacks (practical, creator-focused), or review service rankings (risk-aware, signal-based).