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Review recency is one of the 28 app marketplace metrics and concepts defined in the AppRanks glossary. This page gives you a clear, plain-language explanation of what Review recency means, why it matters when you evaluate an app, how AppRanks calculates and verifies it, and exactly where you will see it across our public app, audit, and comparison pages. Read on for the full definition, practical use cases, and links to related terms so you can interpret Review recency with confidence.
Also known as: recent reviews, review freshness, review age
The freshness of an app's most recent reviews — measured as days since the latest review and as the share of the total review pool that landed in the last 30 days. Marketplaces increasingly weight recency in their ranking algorithms because a 4.7 average accumulated over five years is a weaker signal than the same 4.7 from the last 90 days: it tells the algorithm the listing is still being adopted and the rating reflects the current product, not a legacy version. AppRanks surfaces review recency on every audit page alongside the headline rating so an operator can see at a glance whether the rating reflects today's product or a frozen historical baseline. A common failure mode: a once-popular app holds its high average rating long after merchant adoption has stalled — recency exposes that decay months before total review count would.
Review recency is the cheapest correction to the headline-rating illusion. Two apps with 4.6 averages can have wildly different competitive positions: one with all reviews from the past 90 days is in active growth; one whose last review was 8 months ago is a stalled listing whose merchant base has already churned. For ranking-algorithm purposes most marketplaces (Shopify, HubSpot, Atlassian publicly) treat older reviews with progressively less weight, so a stale rating doesn't carry the same lift it did when it was earned. Tracking recency explicitly stops decisions being made on a rating that no longer reflects the current product.
AppRanks computes review recency by joining the per-review snapshot timestamps to the rolling 30-day window. We surface two raw numbers — days-since-latest-review and share-of-reviews-in-last-30-days — rather than a composite score so operators can read both signals independently. We do not estimate recency from rating-distribution shape; it always comes from real per-review timestamps captured at scrape time.