Definition
An out-of-100 score AppRanks publishes for each tracked app's listing, computed against a fixed rubric covering listing completeness, rating health, review velocity, merchandising, and ranking consistency. The rubric is identical for every app on a given marketplace — we do not weight by paid relationship, traffic, or partnership tier. The exact category weightings and per-check criteria are documented inline on each audit page so a reader can verify exactly which signals moved the score. A score of 85+ generally means the listing has no critical gaps and competes well in its category; 60-85 means meaningful merchandising work is available; below 60 typically signals missing screenshots, thin descriptions, or rating-velocity issues that suppress install conversion. Audit scores refresh on demand when underlying inputs change. The audit also produces a prioritized recommendation list so a developer can see exactly which fixes would move the score most.
Where you see it on AppRanks: Every audit page (/audit/{platform}/{slug}) — score in the page title.
Why this metric matters
Audit score gives a developer or operator a single number to track listing health over time, separate from the more-volatile rating and review-velocity signals. A consistent 85+ across audit refreshes signals a listing where merchandising fundamentals are dialed in — the dev can focus product effort on conversion-rate experiments or growth campaigns rather than listing fixes. A score in the 60-85 band typically means 2-4 high-leverage merchandising fixes will move the headline number; the audit's prioritized recommendation list shows which fixes have the largest expected impact. Below 60, the listing usually has structural gaps (missing screenshots, thin description, no pricing transparency) that suppress install conversion before any rating-based ranking signal can compound.
How AppRanks computes it
Each audit checks the same fixed rubric — 6 weighted categories covering listing completeness, content quality, visuals, categorization, technical signals, and language coverage. Each category contains 3-8 deterministic checks scored as pass/warn/fail. The overall score is the weighted-average of category scores. Weights and per-check criteria are documented inline on every audit page so a reader can verify exactly which signals moved the score. Scores recompute on demand whenever the underlying app snapshot refreshes (12-24h cycle for active listings).
Use cases
- Developer planning Q2 listing fixes: the audit's prioritized recommendation list ranks fixes by expected score impact, so engineering effort goes to the highest-ROI item first.
- Founder doing competitive intel: comparing audit scores across category leaders surfaces structural gaps your listing could exploit (e.g., none of the top 5 have screenshots — yours becomes the conversion winner).
See also: Example audit with full score breakdown · Methodology and rubric documentation
External references: Google: Creating helpful, reliable, people-first content (E-E-A-T) · Schema.org AggregateRating specification