Best of North Seattle
The method

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.

50%
Quality

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.

15%
Engagement

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.

15%
Editor

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.

10%
Social sentiment

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.

10%
Trust + Freshness

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