Every Google Maps extractor produces the same columns, because they all read the same public listings. The interesting question is not which tool has better data. It is which of those columns you should believe.
Treating an extracted list as uniformly accurate is how teams end up with a 30 percent bounce rate and a sales rep who stops trusting the CRM. The fields decay at very different rates, and knowing which is which changes how you clean the list.
Reliability by field
Ordered roughly from most to least trustworthy.
Business name: high
Names are stable and owner-maintained. The main annoyance is inconsistency rather than error: “Smith & Sons Ltd”, “Smith and Sons”, and “Smith & Sons Limited” are the same business across different listings.
Never use the name as a de-duplication key. Use the place identifier.
Coordinates: high
Latitude and longitude are structural, and Google has strong incentives to keep them right. If you are segmenting by territory or radius, coordinates are the field to segment on rather than the postal address.
Address: high, with a caveat
Accurate for premises-based businesses. Unreliable for service-area businesses like plumbers and mobile locksmiths, who often list a home address or hide the address entirely. If your list is full of trades, expect a meaningful fraction of blanks or residential addresses, and do not treat that as a data fault.
Category: medium
Owner-selected from Google’s taxonomy, which means it reflects how the business wants to be found rather than what it primarily does. A business listed as “Marketing Agency” may be a one-person SEO freelancer or a 200-person group. Category is a decent filter and a poor qualifier.
Star rating and review count: medium, and time-sensitive
Both are accurate at the moment of extraction and start drifting immediately. Review count in particular moves fast for consumer businesses.
This matters if your pitch depends on it. “I noticed you only have 4 reviews” is a strong opener and an embarrassing one if they have had 60 since your list was built. Anything that references review counts should be extracted within days of the outreach, not months.
Phone number: medium, and the biggest source of waste
The failure mode here is not wrong numbers. It is stale numbers: businesses that closed, moved, or changed provider without updating the listing. Google is slow to reflect closures, particularly for small businesses that never claimed their profile.
Expect somewhere in the range of 5 to 15 percent dead numbers on an unfiltered list, higher in categories with high churn like restaurants and salons.
Opening hours: low
Frequently stale, frequently wrong around holidays, and often absent. Useful as a signal that a listing is actively maintained. Not useful as fact.
Email: low, and mostly absent
Google Maps listings do not carry an email field. Any extractor producing emails is visiting the website linked on the listing and looking for a published address, then falling back to pattern guessing.
Two things follow. First, coverage is capped by how many businesses have a website at all, which in local trades is often under half. Second, a tool advertising near-complete email coverage is either guessing patterns like info@domain or reselling a third-party database. Guessed addresses bounce, and bounces damage your sending domain.
Realistic hit rate for genuinely published emails: 20 to 40 percent of rows, lower in trades, higher in professional services.
Cleaning sequence
Order matters, because each step reduces the work of the next.
- De-duplicate on place identifier. Grid-based extraction produces overlap by design. Do this first, before you count anything.
- Drop rows with no phone and no website. They cannot be contacted through any channel. Typically 3 to 8 percent of a list.
- Normalise phone numbers to E.164. Local formatting varies wildly and breaks dialler imports and duplicate detection.
- Flag permanently closed listings. Where your extractor captures the closure flag, filter on it. This is the single highest-value filter available.
- Segment by review count before you segment by anything else. A business with 3 reviews and a business with 300 are different companies with different budgets and different problems, regardless of shared category.
- Verify before dialling at volume. A phone validation pass costs a fraction of a cent per row and is cheaper than a rep’s time discovering dead numbers manually.
Freshness beats size
The instinct is to build the biggest list possible and work through it for months. This is backwards.
A Google Maps list decays at roughly one to two percent per month on contactability, faster in high-churn categories. A 50,000-row list worked over a year is, by the end, materially worse than a 5,000-row list refreshed quarterly, and it costs your team more calls to reach the same number of live businesses.
This is also where flat-licence extraction and per-row extraction diverge in practice. When re-running a sweep costs nothing extra, refreshing quarterly is obvious. When it is billed per row, refreshing feels wasteful and lists get stretched past their useful life. The pricing model quietly shapes the data quality.
What to tell your team
Extracted Google Maps data is a good prospecting list and a bad database of record. It tells you a business exists, roughly where it is, roughly how visible it is, and how to try reaching it. It does not tell you the business is still trading, that the number connects, or that the person answering is the decision maker.
Set expectations at that level and the list performs well. Present it as verified contact data and it will disappoint everyone who touches it.
Lead Finder captures place identifiers, closure flags and review counts so the cleaning sequence above can be run against the raw export. See what it extracts, or read how grid coverage works.