Snapshot: Google Maps Performance for San Francisco Restaurants
Use this page to read Google Maps in two layers: city vs region for near-market context, then city vs country for broader positioning. Data collected on 2026-03-27.
Google Maps rating in San Francisco is 4.48, which is +0.18 vs California and +0.02 vs US.
| Scope | Average rating | Avg reviews per location | Sample size |
|---|---|---|---|
| San Francisco | 4.48 | 617 | 84 |
| California | 4.30 | 925 | 1 |
| US | 4.46 | 1,248 | 7 |
What is the average Google Maps rating for San Francisco Restaurants?
The average rating shown here is 4.48, calculated from listing-level rating totals across 51,829 published reviews in the current locality-level snapshot. The 5,137 sampled review texts are used separately for themes, quotes, and distribution rows.
City vs Region Benchmark (Google Maps)
Compared with California, San Francisco is currently above baseline by 0.18 points.
Use this as your nearby-market check before deciding whether a city issue is truly local or part of a wider regional pattern.
City vs Country Benchmark (Google Maps)
Compared with US, San Francisco is currently above baseline by 0.02 points.
This baseline is most useful for quarterly target-setting and national positioning on Google Maps.
Decision Guidance for Operators
Because San Francisco is above California by 0.18 and above US by 0.02, your next decision should prioritize local consistency on Google Maps.
If city and baseline deltas move in opposite directions over time, treat that as a signal to split reporting by market tier rather than rely on one blended KPI.
Data Methodology
Public Google Maps reviews. Data collected on 2026-03-27.
Ratings and published review totals come from page-level listing data. Themes, quotes, and sampled rating distribution rows come from 5,137 sampled review texts analyzed for this article.
- Published reviews across analyzed locations: 51,829
- Review texts analyzed: 5,137
- Locations in scope: 84
- City/region/country benchmark rows use the same platform-specific aggregate method.
This is a pooled locality-level snapshot; individual listings may differ sharply.
Continue Reading
If you want to go deeper, these related resources are the best next stops.