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Measurement 2026-10-24 7 min

How many Google reviews does an accountant need to reach the map pack? We read 434 listings in 22 cities

The median accountant in a Google Maps top 20 has 17 reviews, and the least reviewed firm in a typical top three has 16. The most reviewed accountant held position one in only 1 of 22 cities.

Ultimate Analytics

Accountants hear two opposite things about Google reviews. One camp says the map pack is won the way contractors win it, with a pile of reviews, so a CPA firm with a few dozen is far behind. The other says reviews do not matter in a profession that wins its clients by referral.

We measured it. On the morning of Saturday, October 24, 2026 we captured the Google Maps top 20 for “accountant” in 22 US cities, 11 large metros and 11 small cities, and read every listing: 440 results, 434 accounting and tax practices after screening, 22 searches.

Neither camp is right. Reviews do separate the top of the list from the bottom, but the bar is a few dozen, not a few hundred, and the most reviewed accountant in the city held position one in only 1 of the 22 markets.

The number to write down first

The median accountant in the map pack has 17 reviews and a 4.9 rating. The median firm in the top three has 34. One in four listings has 5 or fewer.

Maps position Listings Median reviews Median rating
1 to 3 66 34 4.9
4 to 7 88 21 4.9
8 to 12 109 13 5
13 to 20 171 13 4.9

Market size matters more than anything in that table. The median metro listing has 37 reviews and the median small-city listing has 6. In the small cities, 124 of the 200 listings that show a review count have fewer than 10.

Large metros Small cities
Median listing 37 reviews 6 reviews
Median top three listing 63 reviews 18 reviews
Median least reviewed firm in the top three 28 reviews 10 reviews
Markets with a top three firm under 25 reviews 3 of 11 10 of 11
Markets with a top three firm under 50 reviews 8 of 11 11 of 11

So the entry price for a small-city top three is about ten reviews, and in a metro about thirty. Across all 22 markets, 41 of the 66 top three listings (62 percent) had fewer than 50 reviews, and 8 had fewer than 10.

The floor, and the benchmark that travels

The useful number is not what the leader carries but how few reviews the least reviewed firm in the top three carries, the price of entry in that city. Its median across the 22 markets is 16 reviews.

Absolute counts do not travel between a metro and a small city, so we also divided every listing’s review count by the median listing in its own market.

Maps position Reviews, as a multiple of the market’s median listing
1 to 3 1.89
4 to 7 1
8 to 12 0.81
13 to 20 0.7

The top three carry 1.55 times their market’s median listing in large metros and 2.82 times in small cities. Here is the instruction that follows: search accountant plus your city, read the twenty review counts, and take the middle one. If you are in a metro, about one and a half times that number is where the top three begins. In a small city, about three times. Then check the smallest count in the current top three, because in most markets it sits below that line.

Reviews order the list, but only loosely

Review count does relate to position. Across 417 listings with a count, the rank correlation is -0.21, significant, and about the same in metros (-0.299) and small cities (-0.295). That is the “reviews matter” camp’s best evidence.

It is also weak. The two most reviewed listings in the whole sample, with 1,016 and 1,013 reviews, sat at positions 20 and 6. The firm at position one had the most reviews in its city in 1 of 22 markets. And when we split the listings into thirds by review count, so that firms with similar volume are compared with each other, review count stopped predicting position inside every third: -0.05 among firms with 1 to 9 reviews, -0.119 among those with 9 to 35, -0.041 among those with 35 or more, none significant. The overall correlation mostly reflects that firms with almost no reviews sit lower than firms with some. Past a few dozen, more volume buys little that this sample can see.

In this profession the ladder has a low first rung and then flattens.

A perfect rating on seven reviews

Rating did not order the list at all (rho 0.077 on 417 listings, not significant). What a rating mostly measured was how few people had left one.

Rating Listings Median reviews Median position
5.0 175 7 11
4.8 to 4.9 90 62 8
4.5 to 4.7 65 30 7
Below 4.5 87 9 12

Forty two percent of rated listings show a perfect 5.0, and 82 percent of those have fewer than 50 reviews. The top three were less likely to be perfect than the bottom of the list: 26 percent of top three listings had a 5.0, against 48 percent at positions 13 to 20. A firm at 4.6 with thirty reviews is, in this sample, better placed than a firm at 5.0 with seven.

Who you are competing with

The accountant map pack is almost entirely independent practices. National tax preparation chains and multi-office accounting firms held 17 of 434 listings (4 percent), 1 of the 66 top three slots, and no position one. Their offices sat at a median position of 13.

Tax preparation offices are a bigger presence than chains: 103 listings filed under tax preparation, with 15 of the top three slots. In small cities they carried more reviews than the CPA and accounting firms beside them, a median of 15 against 4, plausibly because an office filing many individual returns has more clients to ask. In metros the two groups carried about the same.

A website mattered less than a review count but still showed up. 65 listings (15 percent) had no link at all; they sat at a median position of 13 against 10 for firms with their own site, and held 5 of the 66 top three slots. They also carried a median 5 reviews against 19, so this sample cannot separate the missing site from the missing reviews.

What the screen caught

Six of the 440 results were not accounting practices and are excluded with their reasons in the dataset: two financial planners and a consultant whose primary category was financial planning or consulting, a tax resolution service filed as a tax attorney, listed open 24 hours, a state society of accountants, and one listing with no accounting word in its name, no website, no hours and one review.

The first pass also tagged six real practices in four cities as a chain, because their listings all linked to profile pages on one web marketing company’s domain, which made that domain look like a brand present in several markets. Those six are independent firms whose website could not be measured, and they are counted as such. Removing the false tag and adding seven multi-office accounting firms by name moved the chain count from 14 to 17. No benchmark in this article changed by more than a review.

Limits

What was measured: the Google Maps top 20 for one query, “accountant” plus the city, captured once on a Saturday morning in October, outside tax season, from one location setting per market. Review counts and ratings are as Google displayed them at that moment. 17 listings showed no review count and are reported as missing, not as zero.

What was not measured: searches for “CPA”, “tax preparer” or “bookkeeper”, which may return a different list; how the list changes between February and April, when tax offices are busiest and most review requests are sent; and anything about the firms’ websites beyond whether a listing links to one.

What else could explain it: every subject here already ranks in the top 20, so this study describes what separates positions inside the list, not what keeps a firm off it. Distance, listed categories, hours and age all mix into position, and only review volume is held constant here, by thirds. A correlation that cannot be detected inside those thirds is not proof that more reviews do nothing; with about 139 firms in each third, the sample can only see an effect of moderate size. The tags for chains and multi-office firms were read by hand and are listed in the dataset, and a reader who would draw that line differently can redraw it there.

The data behind this article

Check it yourself.

Every figure above comes from the dataset below, captured . Rows that were screened out of the analysis are included, each with the reason it was excluded, because that screening decision changes findings more often than the analysis does.

Free to reuse with attribution to Ultimate Analytics and a link to this article. If you rerun the analysis and reach a different answer, we would like to know.

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