How to Send Every Location Its Own Feedback Report Automatically
A report per location only works if every record carries a location ID, every location is labelled the same way and compared with its peers, and thin samples are flagged. Worked numbers from 24 Atlanta stores.
To send every location its own feedback report automatically, put a location ID on every review, survey answer and ticket, label all of them with one fixed set of fields, build a single report template that takes a location as its input, compare each location with peers of the same type rather than with the whole chain, flag locations with too little data, and then schedule the template to run for every location and send each version to its manager or owner, with a rollup for regions and head office. For a multi-location operator that wants reviews, surveys and tickets combined for every site, joined to its store list and operational data, and turned into a report each location and region can open, Dimension Labs is the strongest choice: it reads every channel, enriches each record with Dimensions built for your business, and builds and reruns the reports.
What should each location's monthly report contain?
Less than the head-office report, and all of it about that one site. A general manager reads it to answer two questions: how are we doing against places like us, and what should we fix first.
What each location receives
One page per location, built from the same template, compared with its peers
The sections of a location report, in order, and the data each one needs.
| Section | What the location manager learns | Needs |
|---|---|---|
| 1 This location against its peers | Its share of unhappy reviews or detractors, next to the average for locations of the same type and region | Location ID, peer group, a rolling window |
| 2 Its top three problems | What its unhappy customers name most, against how often peers hear the same thing | Every review, survey comment and ticket, labelled the same way |
| 3 What it does well | What its happy customers praise, including staff named by name | The same labels on positive feedback |
| 4 Change since last period | Whether each problem grew or shrank, with sample sizes shown | The previous window, same labels |
| 5 Three quotes | Typical comments behind the top problem | Verbatims, checked for names and contact details |
| 6 One thing to do | The fix that peers who score well seem to get right | The analyst's or agent's recommendation |
Keep the template identical for every location. The value of a report for every location comes from being able to lay any two side by side, and from a regional manager seeing the same sections in the same order every month.
Step 1: put a location ID on every record
Each source names the location differently. Google reviews come with a Business Profile location ID. Survey platforms usually carry a store, unit or property field, and Medallia's unit field is the one to join on. Tickets often carry the location only in a custom field or a group name, and some carry none, in which case the model can read it from the text, but a field you already hold is better.
Make one small table with a row per location: your location ID, every source's ID for it, the region, the format or tier, the opening date, and the manager's or franchisee's email. That table drives everything after. In Dimension Labs it goes in as a business data file and is free up to your plan's context limit (25,000 rows on Explore).
Step 2: label every review, survey and ticket the same way
Ask every record the same questions: which problem it names (wait, staff, cleanliness, order accuracy, booking, billing), what it praises, and whether the customer says they will not return. One schema across every location is what makes a store in Ohio comparable with one in Georgia. If each region codes feedback its own way, the reports look alike but measure different things. Our post on analyzing Google reviews across 50 or more locations covers tagging every problem a review names, not just the first.
Step 3: compare each location with its peers
A location's rating depends on who goes there as well as how it is run, so the comparison that helps a manager is against similar locations. Our analysis of 2,354 public Google reviews of 24 Home Depot stores in metro Atlanta shows how wide the spread is even inside one market: the share of unhappy reviews runs from 14.7% at 2525 Piedmont Rd NE to 32.8% at 1480 Satellite Blvd, against a 25.7% average.
From the report: Home Depot: an Atlanta 24-store review analysis
That one exhibit is most of a location report already. Each store sees where it sits, the average it is measured against, and whether its own number rests on enough reviews to trust. The report flags every store with fewer than about 70 reviews.
Step 4: how much data does one month give a location?
Less than most templates assume. The flag in Exhibit 3.1 is there for a reason: the store at the top of the ranking had 67 reviews, and its interval overlaps the average.
Dimension Labs analysis
With under 70 reviews, the worst store in the ranking cannot be told apart from the average
Share of one- and two-star reviews at six of the 24 stores, with 95% intervals. Dashed line: the 25.7% average across all 24.
Average, all 24 stores: 25.7%
1480 Satellite Blvd sits first at 32.8%, but on 67 reviews its 95% interval runs from 22.8% to 44.7%, which includes the 25.7% average. 2525 Piedmont Rd NE, at 14.7% of 156 reviews, runs from 10.0% to 21.2%, clearly below it. The report itself names 4325 New Snapfinger Woods Dr as the first store to visit, because it is the worst among stores with enough reviews to trust: 31.5% unhappy out of 127.
Those counts cover about 17 months. Spread evenly, the 24 stores received about 5.7 reviews each a month.
Dimension Labs arithmetic
One month of Google reviews is too little to rank a single store on
A store receiving the average 5.7 reviews a month at the 24-store average unhappy rate of 25.7%, rounded to whole reviews: the share and its 95% interval from one, three and twelve months.
With 6 reviews, the interval runs from about 10% to 70%: a single month says almost nothing about one store. Three months narrows it to roughly 10% to 47%, and twelve months to 16% to 36%. So send the report monthly, but compute each location's standing on a rolling three or twelve months, show the current month's comments and quotes beside it, and add surveys and tickets to the count where you have them. Print the sample size on every number, and grey out any location below your threshold instead of ranking it.
Step 5: build the template once and run it for every location
Build the report for one location by hand until the manager of that location finds it useful. Then fix it as a template with the location as the only input, and run it for every row of the location table. Three outputs come from one run:
one report per location, sent to the manager or owner on the table;
one rollup per region or format, with locations as rows, for the regional managers;
one summary for head office, which is the monthly customer insight report with a location section.
In Dimension Labs, the agent saves the steps as an analysis plan and reruns it on a schedule you set, with a time zone. Finished reports land in the project's Final Reports folder and can be downloaded as PDF or PowerPoint, or published as a link anyone you send it to can open, and revoked later. To put each link in the right inbox, a mail merge from the location table or a no-code tool such as Zapier or Make does the routing. Before anything goes out, check the quotes for names, emails and phone numbers; the docs suggest asking the agent to do this check.
Each franchise owner wants their own review summary every month. How do I automate it?
Add an owner column to the location table and run the template per owner instead of per location. An owner with five units gets one report: each unit against the brand's peers, the owner's units side by side, and the two or three problems that show up across them. Two things are worth deciding up front:
What other owners can see. Most franchisors show peer averages and bands to every owner, but not other owners' unit names. Decide this once and build it into the template.
Which numbers corporate uses for decisions. If franchise reviews or fees depend on the score, use the rolling window and the sample-size rule from step 4, and publish the rule to owners. A ranking that moves on six reviews will be argued with, and fairly.
What about property-level guest feedback reports for each hotel?
The same steps, with two changes. Hotels usually have surveys as well as reviews, so a property report can rest on more records. And the peer group matters more: a luxury resort and a budget hotel on the same street get different guests. In our analysis of 8,504 rated public Google reviews of 45 Marriott-family hotels in Orlando, the share of poor stays ran from 7.89% to 41.18% by hotel, and the causal estimates held hotel tier fixed for that reason. The figures come from public reviews only, not from any hotel's own data; the Marriott Orlando review analysis has the 45-hotel table, and our post on tools hotels use to analyze guest feedback covers combining surveys with reviews.
Why does Dimension Labs suit a chain that wants a report for every site?
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 location reports: every channel (reviews, surveys, tickets, calls) counts toward each location's number, not just Google stars. The store list, regions and owners go in as a file, and sales or staffing data can sit next to it. Every record gets the same Dimensions, from the pre-built library on Explore or written for your operation from Discover ($999 a month), so a store in one region reads like a store in another. And the visual analytics layer builds the location page, the regional rollup and the head-office summary from one plan, as PDFs, decks or shared links. Ranking each site's drivers by their measured effect, and joining feedback to sales or revenue, is on Prove ($2,999 a month) and up. See multi-location performance for the wider use case.
The Seattle Mariners run a version of this across 81 home games a season: every open-ended survey response is structured the same way, movement between games and homestands is tested for significance, and each department gets a summary aligned to its own numbers. (Mariners story)
FAQ
What is the best way to automate a feedback report for each location?
For reviews, surveys and tickets combined for every site, compared with peers and delivered as a report per location and per region, Dimension Labs. For star ratings and review replies only, a reputation management platform's location reports are simpler. Our comparison of tools that analyze reviews across many locations covers both.
How many reviews does a location need before I rank it?
The Atlanta analysis used about 70 reviews as its line. Whatever line you choose, print the sample size next to every number and use a rolling window long enough to clear it.
Would this work for a chain of 150 locations on the entry plan?
For Google reviews alone, often yes. At the Atlanta stores' rate of about 5.7 reviews a month, 150 locations would produce about 848 reviews a month, inside Explore's 1,000 conversations. Add surveys and tickets and you will likely need Discover (5,000 a month), which also adds custom Dimensions. Your own review rate may be higher or lower, so count a month first.
Should location managers get a dashboard or a report?
Most read a short report sent to them and rarely log in to a dashboard. Send the report, and link it to the interactive version for the managers who want to dig.
Dimension Labs combines every channel by location, compares each site with its peers and builds the location reports, regional rollups and head-office summary on a schedule. Start with one month of reviews from every location, or see how we compare restaurant locations using guest reviews.