How to Benchmark a Restaurant Chain Against Competitors Using Reviews
In public reviews of two restaurant chains, the one with the higher star average had the lower average on written reviews. How to benchmark complaints fairly.
To benchmark a restaurant chain against a competitor with public reviews, collect both brands' Google reviews for the same months, keep only the reviews with text on both sides, tag every review with the same list of complaints, and compare complaint rates per 100 reviews, store by store. Star averages on their own can point the wrong way: in our count of two chains' public reviews, the chain with the higher average had the lower one once star-only ratings were set aside. If the benchmark has to sit next to your own surveys, tickets and sales and be refreshed every month, Dimension Labs fits best, because it tags your reviews and the competitor's with the same Dimensions and builds the benchmark report itself.
Below are the steps, a worked comparison of two chains from their public Google reviews, and the tools that do this.
How can a restaurant chain compare its guest reviews with a competitor's?
A fair benchmark holds everything constant except the brand. In practice that means five decisions, made before anyone looks at a chart:
The competitive set. Pick the competitor stores that share a trade area with yours: the same streets, the same lunch crowd. A competitor's national average mixes markets you do not compete in.
The window. Use the same months for both brands. A winter of slow drive-thrus at one chain and a summer at the other is not a comparison.
What counts as a review. Google stores star-only ratings and written reviews. Decide which you use and use the same for both, or the averages will differ for reasons that have nothing to do with the food.
One list of complaints. Tag both brands' reviews with the same topics and the same rules: rude staff, a long wait, a wrong order, food that arrived cold, a dirty room, price. A topic list written for one brand and applied to the other favours the first.
Rates, not counts. Report each complaint per 100 written reviews, and how often a review naming it is one or two stars. A busier competitor has more of everything.
Within one chain, the same logic applies to comparing your own stores; how to compare restaurant locations using guest reviews walks through grouping stores by the kind of location they are in.
What does a review benchmark of two restaurant chains show?
We counted every public Google review posted between January 2025 and April 2026 for 24 cafés of a fast-casual bakery chain and 24 restaurants of a quick-service chicken chain. They are not direct competitors, which makes them a harder test of the method than a real competitive set: different segments, different cities. Both chains' reviews were tagged with the same keyword rules for each complaint. A written review here is one with text; Google's star-only ratings, which carry a star score and no words, are counted separately. The first thing the comparison showed was a trap in the averages.
Across all its reviews, the bakery chain averages 3.79 stars and the chicken chain 3.60. But 859 of the bakery chain's 2,016 reviews are star-only ratings, and those average 4.50 stars. The chicken set as collected holds written reviews only. Compare written reviews with written reviews and the order flips: 3.26 for the bakery chain against 3.60, and 39.7% of the bakery chain's written reviews are one or two stars against 31.8%.
Dimension Labs analysis
Count the star-only ratings and the bakery chain leads; read only written reviews and it trails
Average star rating of each chain's public Google reviews, January 2025 to April 2026, two ways.
All reviews, including star-only ratingsReviews with text only
The same gap shows up on the public listings. When the chicken chain's reviews were collected, its 24 Google listings showed ratings from 4.0 to 4.6, while the written reviews behind them averaged 3.60. People who write a review are, on average, less happy than people who only tap a star. A benchmark built on listing ratings compares how many quiet, satisfied guests each brand has, which is mostly a question of traffic.
Where the two chains differ
With the same rules applied to both sets of written reviews, the chains draw nearly the same rate of complaints about rude staff (6.2 and 6.4 per 100 reviews), wrong or missing orders (4.2 and 4.7) and food that arrived cold, stale or badly made (4.1 and 4.5). Both get praised for friendly staff about as often, 16.3 and 15.8 times per 100.
Two complaints set them apart. Bakery chain reviews complain about price 4.1 times per 100 against 0.7, about 5.8 times the rate, and about a dirty dining room or restroom 4.1 times against 0.8, about 4.9 times. Waits come up somewhat more often too, 10.7 against 8.3.
Dimension Labs analysis
The chains draw the same rate of complaints about staff, orders and food; price and cleanliness set them apart
Reviews naming each complaint, per 100 written reviews, same keyword rules for both chains, January 2025 to April 2026.
Bakery chain (1,157 written reviews)Chicken chain (2,407)
That is the useful part of a benchmark. A bakery-chain manager reading only the star gap would send people to retrain counter staff, and on this evidence the counter is not where the chain falls behind. The gap sits in what guests feel they paid for and the state of the room.
Price complaints are also the least severe of the six. A review naming a price complaint is one or two stars 51% of the time at the bakery chain, against 82% for a review naming rude staff. A benchmark should report both numbers for each complaint: how often it comes up and how badly it goes when it does. Ranking by severity rather than volume is the subject of which complaints cause bad reviews.
How do we benchmark service and food quality from reviews?
Google lets reviewers add optional 1 to 5 scores for food, service and atmosphere. They are tempting for a benchmark because they look like the exact question. On these two chains they put the chicken chain ahead on all three, by 0.40 on food, 0.33 on atmosphere and 0.32 on service.
From Google's own sub-ratings
Google's food, service and atmosphere scores put the chicken chain ahead on all three
Average of the optional 1 to 5 sub-ratings reviewers gave alongside their written review, and the share they rated 1 or 2.
| Sub-rating | Bakery chain | Chicken chain |
|---|---|---|
| Food | 3.74 24.7% gave 1 or 2 | 4.14 16.8% gave 1 or 2 |
| Service | 3.52 32.4% gave 1 or 2 | 3.84 25.9% gave 1 or 2 |
| Atmosphere | 3.68 25.7% gave 1 or 2 | 4.01 19.6% gave 1 or 2 |
Yet the written complaints about food are level between the two chains, as shown above. Sub-ratings move with the reviewer's overall mood: an angry guest marks everything down. They tell you that one brand's guests are happier, which the overall rating already said, and not which part of the meal is behind it. For food and service, benchmark the tagged text: which specific failures each brand's guests name, at what rate, and how often each one ends in a bad review.
Which store-level gaps are real?
A chain-level benchmark hides how far apart stores of one brand are. In the bakery chain, the share of written reviews that are one or two stars runs from 25.0% to 60.0% across its 24 cafés; in the chicken chain, from 14.2% to 47.5%. Both ranges are wider than the gap between the two brands. With 28 written reviews behind the smallest café, some of that spread is chance, so put an interval on every store's rate before naming a winner and a loser.
Two practical consequences for a competitor benchmark:
Match stores, not brands. Compare each of your stores with the competitor stores within its trade area. A finding that the whole brand trails a rival usually comes down to a few of your stores trailing the rival stores near them.
Keep the competitor's store ids. Store the place id of every competitor location you track, so each month's reviews land on the right store and the benchmark can be rerun the same way.
For a retailer, the same store-by-store reading is shown on 24 stores of one chain in which problems go with bad reviews at Home Depot stores.
Which tools benchmark restaurant reviews against competitors?
Tools that benchmark reviews against competitors
Which tool fits which benchmarking job
Vendor descriptions from each vendor's own page, read 2026-11-13.
| Tool | Best for | What it compares |
|---|---|---|
| Dimension Labs | A chain that wants the competitor benchmark next to its own surveys, tickets and sales, with Dimensions built for restaurants | Your reviews and a competitor's, tagged with the same Dimensions, by store and month; on Prove and up, joined to your store and sales data |
| Black Box Intelligence | A chain that wants restaurant-industry sentiment benchmarks by segment and region | Sentiment by topic such as value, speed and cleanliness against the brands you select, by segment, region or location |
| Birdeye Competitors AI | A multi-location team already using Birdeye for reviews that wants a head-to-head reputation score | Review comparison, sentiment and its Birdeye Score against competitors it identifies by industry and location |
| A spreadsheet and keyword rules | A one-off look at a handful of stores | Whatever you tag by hand; fine for a few hundred reviews, slow to repeat each month |
When a narrower job is all you need, the others are a good choice. Black Box Intelligence suits a chain that wants restaurant-industry sentiment benchmarks without tagging anything itself, and Birdeye suits a team already answering reviews in Birdeye that wants a competitor score in the same place. Dimension Labs takes in reviews from Birdeye through its API, so the two can run side by side (see the Birdeye integration).
Why do restaurant brands keep their competitor benchmark in Dimension Labs?
Dimension Labs is an omni-channel analytics agent for customer data. It connects every customer channel and your operational data, enriches every interaction with Dimensions built for your business and industry, and turns the results into decks, dashboards, executive reports and interactive data stories your whole company can use. Plans start at $299 a month with no demo.
For a restaurant benchmark, that comes down to four things:
Your guest surveys, delivery-app comments, support tickets and Google reviews sit in one place with the competitor's public reviews, so a gap found in reviews can be checked against what your own guests say elsewhere.
Store lists, sales and labor data join to the reviews by store id. The join to revenue and the effect estimates with confidence intervals come with the Prove plan ($2,999 a month) and up.
Both brands' reviews are tagged with the same Dimensions, written for restaurants: the complaint, the dish, the channel (dine-in, drive-thru, delivery), the staff member named. Custom Dimensions start on Discover ($999 a month). Operations, marketing and finance read the same tags.
The agent builds the benchmark as a deck, a dashboard or a PDF, can rerun it on a schedule, and saves each run to the project's Final Reports folder, shared as a link you can revoke.
Teams at Travelers, Dropbox, Concentrix and the NBA use Dimension Labs on their customer conversations; Travelers uses it to compare performance across business units and regions. The restaurant setup is described on the restaurant guest feedback analytics page, and the review side on review analytics and competitive benchmarking.
FAQ
What is the best way to benchmark restaurant reviews against competitors?
Tag your reviews and the competitor's with the same complaint list, for the same months and the same trade areas, and compare complaint rates per 100 written reviews store by store. For a chain that wants that kept current next to its own surveys and sales, Dimension Labs is the strongest choice; for an industry sentiment score with no tagging, Black Box Intelligence is simpler.
Should we compare star ratings or complaints?
Complaints. Star averages change with how many star-only ratings each brand collects. In our count, the chain with the higher overall average had the lower average on written reviews.
Can we benchmark against a competitor in another city?
You can, as we did here, but the result mixes the brand with the market. Use it to learn what differs, then confirm on competitor stores in your own trade areas.
How many reviews does each store need?
Enough that its rate does not swing with a few reviews. Stores with a few dozen written reviews in the window should be shown with an interval or grouped with similar stores; how to analyze Google reviews across 50 or more locations covers this.
Your own reviews are the place to begin: start with one file of your stores' reviews and add a competitor's next to it.