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Marketing Academy · Field Work●Growth Marketing
All projects·From: Network Effects
MiniAudit· 30 minutes

Real Network Effect or Just Growth? Auditing Yelp's City-Level Liquidity

Yelp

Objective: Given a 3-city export of Yelp business-listing counts, monthly active reviewers, and reviews-per-business, classify which cities have crossed the 'can users reliably find what they need' liquidity threshold and which are still in cold start.

You're a growth analyst at Yelp assessing whether three newer metro markets are ready for a local ad-sales push, or whether the review density is still too thin to support it.

Compute reviews-per-active-business for each city, apply the lesson's marketplace liquidity definition, and flag which city is cold-start, which is tipping, and which has compounded past critical mass.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets
FreeCompute and sort the reviews-per-business ratio across cities

Free, no account friction, sufficient for a 3-row comparison

The process

1 step

Step 01 of 01

Classifying critical mass by liquidity, not raw user count

The lesson defines a marketplace's critical mass threshold by liquidity: can a user reliably find what they need, not by total signups.

City A has 40,000 listed businesses and 2,000 reviews. City B has 6,000 listed businesses and 18,000 reviews. Which city is actually closer to critical mass?

Google Sheets— Import the 3-city export, add a computed reviews-per-business column, sort descending.

Procedure

  1. Import the export with columns: city, businesses_listed, monthly_active_reviewers, total_reviews
  2. Add a formula column: =total_reviews/businesses_listed
  3. Sort by that ratio descending, not by raw business count
  4. Flag any city under a 0.5 reviews-per-business ratio as cold-start
Sample output
City, Businesses, Reviews, Reviews/Business
City B, 6,000, 18,000, 3.00 -> LIQUID
City C, 15,000, 9,000, 0.60 -> TIPPING
City A, 40,000, 2,000, 0.05 -> COLD START

Healthy

A smaller city with a high reviews-per-business ratio, users can reliably find a reviewed business.

Unhealthy

A large city with thousands of listings but almost no reviews per business, raw scale masking cold start.

What this means

Total listings measure reach, not liquidity. A city with fewer, well-reviewed businesses is closer to critical mass than a sparsely-reviewed sprawl.

So what do I do about it?

SymptomActionEffort
Sales team wants to launch paid ads in the largest city by listing countRedirect ad-sales launch to the city with the highest reviews-per-business ratio first30 min
YouYou can do this yourself, no engineering access required.

Final deliverable

A 3-city liquidity ranking with a cold-start / tipping / liquid classification and one launch recommendation.

See a reference example
Sample output
Zillow market-readiness memo (excerpt)

LIQUID: Austin (3.1 reviews/listing) -> greenlight local ad-sales outreach
TIPPING: Raleigh (0.6 reviews/listing) -> hold 1 quarter, reassess
COLD START: Boise (0.08 reviews/listing) -> needs manual seller-review seeding before any paid push

Success criteria

You're done when you can:

  • Correctly computes reviews-per-business for all 3 cities
  • Classifies each city as cold-start, tipping, or liquid using the ratio, not raw counts
  • Recommends the ad-sales launch city based on liquidity, not listing volume