How we rank businesses
A composite score, weighted, with the math published. No pay-to-play. No surprise tier bumps. Sponsored placements (when they exist) are clearly labeled and never affect ranking.
Quality is the largest weight in the composite (50% of Quality, which itself is 75% of the overall score). It is built from review aggregation across multiple sources, weighted 50% external aggregations, 25% directory-style aggregations, and 25% community reviews submitted on this site. When community reviews are not yet available for a business, the external aggregations fully cover the weight (no penalty). Every aggregation is Bayesian-smoothed with a category-mean prior so a place with three glowing reviews cannot leapfrog a place with three hundred mostly-glowing ones. Thin-data businesses get pulled toward the category average until enough signal accumulates to justify a higher (or lower) rank.
Internal signals from the people using this site. Saves, votes, clicks, return visits. A pick that locals keep coming back to and recommending climbs. A pick that no one ever bookmarks fades, even if its quality score looks fine.
Human-curated visit scores. A local with taste actually shows up, eats the food, sits in the chair, talks to the staff, and writes notes. As of today: 0 of 189 businesses carry an editor score. That number will climb every week. Editor coverage is transparent on each business page.
Buzz across neighborhood social channels, AI-classified for theme and tone. We never quote, screenshot, or republish anyone's posts. We extract paraphrased themes ("locals love the patio," "service has slipped lately") and weight them.
BBB membership, chamber listings, claim status (verified owners get a small bump), and last-verified date. A pick that has not been touched in two years gets stale and loses points until someone re-verifies.
Why Bayesian smoothing matters
Without smoothing, a coffee shop with a single five-star review beats a coffee shop with four-hundred reviews averaging 4.6. That is obviously wrong. The category-mean prior pulls every score toward what is typical for the category until enough reviews accumulate to overcome the prior. Confidence math caps how high a low-data place can rank, no matter how good its average looks.
What we never do
- Quote, screenshot, or republish text from review platforms or social channels.
- Fabricate quotes or reviews. Paraphrased themes only.
- Sell ranking position. Sponsored placements are labeled and ranked separately.
- Hide score changes. Every adjustment is logged.
Last updated: 2026-05-24