Google Maps business data extractor: how current the data really is

How accurate is Google Maps business data?

Lead Finder extracts data that is generally more current than a purchased database, for a structural reason: businesses maintain their own listings because customers see them, so the incentive to keep hours and phone numbers correct is immediate rather than administrative. That said, accuracy varies sharply by field and by how the listing is managed. Claimed profiles, where an owner has verified control, are reliable. Unclaimed profiles were assembled by Google from other sources and may never have been touched, so their details can be years old with nothing indicating it. The permanently-closed flag is the weakest signal in the export, because it depends on someone reporting a closure, so it consistently lags reality by weeks or months.

What decays fastest

Existence decays fastest in food and hospitality, where openings and closures move within a season, and slowest in premises-heavy categories such as clinics, pharmacies and manufacturing, where equipment and licensing anchor a business in place.

Hours change constantly and are frequently wrong, particularly around holidays. Phone numbers change less often but change silently, and websites go dead without the listing reflecting it, which is why a dead website is itself a useful prospecting signal rather than merely bad data.

Why the closure flag cannot be trusted alone

Marking a business permanently closed generally requires a report or an owner action, neither of which happens promptly. A business that shut three months ago often still shows as open, and in high-churn categories that gap represents a meaningful share of the list.

Review recency is the better liveness check and it is already in the export. A business with no reviews in a category that normally accumulates them steadily is worth verifying before anyone calls, regardless of what the closure flag says.

Verifying without checking every row

Full verification is impractical at scale and unnecessary. Sampling works: check a small random subset by phone or website, measure the error rate, and apply that estimate to the list rather than assuming either perfection or unusability.

Where the campaign cost per contact is high, verify the subset you intend to contact rather than the whole export. Where it is low, calling is itself the verification and the error rate becomes an accepted cost rather than something to eliminate in advance.

Related reading: field-by-field reliability , claimed versus unclaimed profiles .