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Marketing Academy · Field Work●SEO
All projects·From: SEO for Multi-Location and Franchise Businesses
MiniAudit· 35 minutes

40 Locations, One Spreadsheet: Auditing a Location Export for Duplication and NAP Risk

FirstCry (Brainbees Solutions)

Objective: Given a supplied 10-location export (page content summaries plus NAP data), decide which location pages are dangerously templated near-duplicates and which listings carry NAP inconsistencies that would confuse Google about which address is real.

You're auditing 10 FirstCry store location pages ahead of a national expansion, checking whether the existing template is safe to reuse at scale or is already cannibalizing itself.

Score each location page's genuinely-unique word count and check NAP fields for consistency, then flag the pages most at risk before the network triples in size.

Before you start

What you'll need

Free path (everything below is enough to finish)

Google Sheets
FreeScore unique content and log NAP findings

No setup, easy to hand off to the content team

Google Business Profile
FreeSearch for and identify duplicate listings per location

Free, the direct source of truth for what's actually live

The process

2 steps

Step 01 of 02

Detecting near-duplicate location page content via a unique-content word count

The lesson's duplicate-content section says a business with 40 nearly-identical pages isn't publishing 40 ranking opportunities, it's publishing 40 thin pages that cannibalize each other, and sets a 150-200 word bar for content that literally cannot be copy-pasted onto another location's page.

Across the 10-location export, 7 pages only vary the city name and address in an otherwise identical paragraph. The other 3 include a named store manager, specific service notes, and a local FAQ. Which pages are the cannibalization risk?

Google Sheets— Paste each location page's body copy into one row per location, run a manual unique-phrase comparison.

Procedure

  1. Paste all 10 location pages' body text into Sheets, one row per location
  2. Strip the city name/address, compare what remains across rows
  3. Count words that survive the strip and are not boilerplate ('Welcome to', 'Visit us today')
  4. Flag any location under 150 genuinely unique words as high cannibalization risk
Sample output
Location: Koramangala   Unique words after strip: 22   Risk: HIGH
Location: Indiranagar   Unique words after strip: 24   Risk: HIGH
Location: Andheri West   Unique words after strip: 210 (named manager, service notes, local FAQ)   Risk: LOW

Healthy

Every location clears roughly 150-200 words of content that could not be copy-pasted onto another location's page without literally being wrong.

Unhealthy

7 of 10 pages differ only in the city name token, everything else is boilerplate.

What this means

A low unique-word count isn't a style problem, it's the exact pattern the lesson says makes Google choose between your own pages and often suppress both, since a competitor with one genuinely useful page beats 7 near-identical ones.

So what do I do about it?

SymptomActionEffort
Most locations score under 150 unique wordsPrioritize adding local team bios and local FAQs to the 7 flagged pages before adding any new locationshalf day
YouYou can do this yourself, no engineering access required.

Step 02 of 02

Auditing NAP consistency and duplicate GBP listings at scale

The lesson's GBP-at-scale section flags duplicate and fake listing monitoring as a required task once a network grows, since competitors or confused customers create duplicate listings that steal ranking signal from the real one.

The export shows 2 of the 10 locations have a second, older Google Business Profile still live under a slightly different phone number. What's the actual risk, separate from just looking untidy?

Google Business Profile— Search each location's name + city in Google Maps, note every listing that appears, not just the one you manage.

Procedure

  1. Search each of the 10 location names + city directly in Google Maps
  2. Note any second listing at the same or a nearby address
  3. Compare phone number and hours between the duplicate and the real listing
  4. Flag mismatched phone numbers as a NAP inconsistency, not just a duplicate
Sample output
Koramangala: 2 listings found. Real (managed): +91-80-XXXX-1122. Duplicate (unmanaged, stale): +91-80-XXXX-0099, hours show 'Permanently Closed' incorrectly.

Healthy

One listing per physical location, phone number and hours identical everywhere the business is cited.

Unhealthy

A stale duplicate listing showing 'Permanently Closed' next to a real, open store, actively telling searchers the wrong thing.

What this means

A duplicate isn't just clutter, it splits the review count and ranking signal that should belong to one listing, and a stale 'closed' status can actively turn away searchers from a store that's open.

So what do I do about it?

SymptomActionEffort
A duplicate or stale listing shows up for a managed locationFile a duplicate-merge request through Google Business Profile support for each flagged pair30 min
YouYou can do this yourself, no engineering access required.

Final deliverable

A 10-row audit sheet scoring each location's unique-content word count and flagging any duplicate GBP listing found, ranked by risk.

See a reference example
Sample output
Lenskart, location export audit (excerpt)

HIGH RISK, template-only content
  MG Road store: 19 unique words after city-name strip, no duplicate listing found

HIGH RISK, duplicate listing
  Whitefield store: 31 unique words, PLUS a second unmanaged GBP listing showing wrong hours

LOW RISK
  Indiranagar store: 187 unique words (named optometrist, local FAQ), single clean listing

Success criteria

You're done when you can:

  • Correctly ranks all 10 locations by unique-content risk
  • Identifies both duplicate-listing cases in the sample export