NAP

When Google's crawler encounters a business on Yelp, on the Better Business Bureau, on Apple Maps, and on Google Business Profile, it compares the Name, Address, and Phone across every appearance. If they agree, Google increases confidence that the business is real and trustworthy. If they disagree — different phone numbers, abbreviated street types, missing suite numbers — Google reduces confidence and ranks the business lower in the map pack and Local Finder.

This signal has been documented in Google's own guidance and verified through every major industry study since at least 2014. It hasn't gone away in the AI-search era — if anything, it's grown more important because AI Overviews and ChatGPT recommendations pull from the same underlying business graph Google uses for local results.

Almost no business with multiple online listings has perfect NAP consistency. Inconsistencies sneak in through:

• Manual data entry — different people typed the address into different directories years apart
• Address changes — the business moved, but old listings on smaller directories never got updated
• Phone changes — number ported to a new provider, but Yelp still has the old one
• Aggregator pollution — one bad entry in a data aggregator (Factual, Neustar Localeze, Foursquare Pinpoint) feeds dozens of downstream directories
• Formatting differences — St vs Street, Suite 200 vs #200, (212) 366-1182 vs 212-366-1182
• Ownership changes — new owner updated Google but not the long tail of secondary listings

Severity varies. Google's tolerance is higher than people assume for formatting (it understands St and Street mean the same thing), and lower than people assume for substantive differences (a different phone number is a serious signal that something is wrong).

Rough severity ranking from highest to lowest impact:

• High severity: different phone numbers, different street addresses (not formatting — actually different streets), or significantly different business names
• Medium severity: different suite/unit numbers, different ZIP codes, business name variations (LLC vs Inc, abbreviations)
• Low severity: street type abbreviation differences (St vs Street), phone number formatting, capitalization

The hard part isn't identifying mismatches — it's getting them corrected on every directory. Pathways depend on where the mismatch lives:

• Major directories (Google, Yelp, BBB, Apple Maps): claim the listing and update directly
• Data aggregators: submit a correction to the aggregator and wait for downstream propagation (typically 4-12 weeks)
• Smaller directories: contact-form requests, often slow and inconsistent
• Listing management services: pay a service to handle the long tail at scale

For agencies managing multiple locations, the workflow has shifted. Most operators now run a weekly automated audit via the Citation Audit API, get an agent to draft the correction tickets, and assign the queue to a virtual assistant. The audit-fix-monitor loop runs on cron rather than quarterly project work.

NAP consistency was a 2014-era ranking signal. It still applies, but the surfaces it influences have expanded. Google's AI Overviews surface business recommendations, and the AI is drawing from the same business knowledge graph that NAP signals feed. ChatGPT and Perplexity make local business recommendations that pull from web-scale business data — and inconsistent NAP makes a business appear less trustworthy to LLMs the same way it does to Google's classic algorithm.

The work hasn't gone away — it's the same audit-fix-monitor loop. What's changed is who does the work. An agent connected to a data API can audit a business in 8 seconds, prioritize fixes by severity, and draft tickets. A dashboard with a manual export can't.