Understand what’s happening at every location
Compare your locations through AI-categorized customer feedback. Start with the group, investigate the categories, and open the original reviews. Explore each view below, then follow the explanations on this page.
Interactive demonstration · fictional data
Group overview
Google reviews · September volume, ratings and topics.
- Reviews in the period
- 648 10 locations with reviews
- Historical Google rating
- 4.49 / 5 Average of the locations’ historical ratings
- 1–3-star reviews
- 9.1% 59 of 648
- Written comments
- 392 Basis for category analysis
Review volume over time
Review count by September date range
The final date range covers 2 days, rather than a full week.
Star rating distribution
All ratings for the period · n=648
- 5 stars443 · 68.4%
- 4 stars146 · 22.5%
- 3 stars16 · 2.5%
- 2 stars20 · 3.1%
- 1 star23 · 3.5%
Where complaints appear
Comments with complaints, including mixed feedback
n=392 · A comment may mention multiple topics.
Strengths to build on
Comments with praise, including those with complaints
- Operations322
- Product130
- Marketing18
Customize categories to fit your business.
10 locations with the highest share of 1–3 star reviews
Among locations with at least 10 reviews. This demo includes data for 10 locations.
| Location | Reviews | Rating /5 | 1–3 stars | With a comment | Top complaint topic |
|---|---|---|---|---|---|
| 104 | 4.28 | 17.3% · 18 | 74 | Product (6) | |
| 86 | 4.38 | 12.8% · 11 | 55 | Operations (5) | |
| 78 | 4.45 | 10.3% · 8 | 49 | Operations (3) | |
| 72 | 4.50 | 8.3% · 6 | 46 | Operations (4) | |
| 68 | 4.52 | 7.4% · 5 | 42 | Operations (3) | |
| 61 | 4.50 | 6.6% · 4 | 36 | Product (3) | |
| 55 | 4.55 | 5.5% · 3 | 30 | Operations (3) | |
| 48 | 4.58 | 4.2% · 2 | 24 | Product (2) | |
| 34 | 4.55 | 2.9% · 1 | 15 | Operations (1) | |
| 42 | 4.59 | 2.4% · 1 | 21 | Operations (2) |
Share of comments mentioning a complaint in each category
Darker shades indicate a higher share. Each cell shows the percentage and comment count.
Scroll horizontally to compare all categories.
| Location / comments | Product | Operations | Marketing | Delivery Apps | Systems |
|---|---|---|---|---|---|
| Downtown Locationn=74 | |||||
| Park Locationn=55 | |||||
| North Locationn=49 | |||||
| Riverside Locationn=46 | |||||
| Garden Locationn=42 | |||||
| West Locationn=36 | |||||
| Alameda Locationn=30 | |||||
| Plaza Locationn=24 | |||||
| Promenade Locationn=15 | |||||
| Forest Locationn=21 |
Operations · Complaint subcategories
Google · All locations
- Dine-in and pickup wait times10
- Staff service and courtesy7
- Dining room coordination and accuracy12
Color scale
- 0%
- >0–<5%
- 5–<10%
- 10–<20%
- ≥20%
A review may cover multiple topics. Percentages do not add up to 100%. Calculations use the available written comments.
Sample data illustrating categories and subcategories.
- Reviews in the period
- 104 10 locations with reviews
- Historical Google rating
- 4.28 / 5 Average of the locations’ historical ratings
- 1–3-star reviews
- 17.3% 18 of 104
- Written comments
- 74 Basis for category analysis
Rating throughout the month
Historical Google rating and review count in each date range
What the comments reveal
Based on 74 comments. A review may include praise and criticism.
| Category | With praise | With complaints |
|---|---|---|
| Operations | 62 | |
| Product | 24 | |
| Marketing | 3 | |
| Delivery Apps | 0 | |
| Systems | 0 |
A sample review, analyzed by topic
Google · Sample review
1 / 5 starsReview text
“The food was delicious and the dining room is pleasant. However, we waited a long time for service and had to ask for the bill several times. We would like them to improve service coordination.”
What categorization reveals
The comment praises the food but highlights issues with wait times and coordination. Categorization helps identify which area needs to improve.
Fictional comment created to illustrate topic analysis; separate from the demo counts.
- Written comments
- 392 of 648 reviews
- Comments with complaints
- 46 Negative and mixed comments
- Complaints by category
- Operations: 29 · Product: 22 A complaint may mention multiple categories
Scroll horizontally to read comments and original ratings.
| Date / ID | Location | Complaint category | Review text · excerpt | Original rating |
|---|---|---|---|---|
| Sep 28R0963 | Park Location | The food was very good, but we were left waiting at the entrance. The food tasted good, although the temperature and preparation could improve. | 4/5 | |
| Sep 25R1133 | North Location | The food tasted good, although the temperature and preparation could improve. The payment system failed and we had to repeat the checkout process. | 5/5 | |
| Sep 22R0827 | Downtown Location | The food was excellent, but we waited a long time to be served. The food tasted good, although the temperature and preparation could improve. | 5/5 | |
| Sep 18R1286 | Garden Location | Service was friendly, but the wait and coordination could improve. The advertised offer did not match the price shown at checkout. | 5/5 | |
| Sep 14R1337 | West Location | Service was friendly, but the wait and coordination could improve. The advertised offer did not match the price shown at checkout. | 4/5 | |
| Sep 9R1184 | Riverside Location | Service was friendly, but the wait and coordination could improve. The advertised offer did not match the price shown at checkout. | 4/5 |
Fictional data for illustration. Stars rate the overall experience; complaints are identified in the text.
Custom categories can include Product, Operations and Marketing. Mixed comments can also reveal opportunities to improve.
Group, location, and date filters stay applied when switching views.
A simplified example of Cacao’s five views. Names, comments, and figures are fictional and do not represent customer results. The example categories are customizable.
Location-Level Reporting: from group performance to individual reviews
Corporate teams can miss negative feedback when reviews are spread across separate Google profiles. Cacao centralizes connected profiles so the team can investigate locations without checking each profile individually.
Categorization makes that information comparable. AI identifies the topics customers discuss and the sentiment within each topic. A comment can praise the product and criticize service, giving teams a more specific view than its star rating alone.
Start with the group, compare locations, inspect the category matrix, investigate a branch, and read the original complaints. Group, location, and date filters stay applied as you move between views.
An account can contain several customer-created groups, with several locations in each group. Each location belongs to one group. Google reviews and Cacao surveys are analyzed as separate sources.
For a fictional 800-location chain, location-level reporting lets corporate compare its groups, identify branches with a higher negative-review share, and inspect the comments behind each result. Use the same period for volume and negative-share comparisons; historical ratings remain separate from period metrics.
1. Group overview: see the group at a glance
Start with the group’s review activity, historical reputation, and the topics customers mention most.
In this fictional example, 648 reviews include 392 written comments. Operations appears in 29 comments with complaints and Product in 22—two areas to investigate across locations.
- Read review volume, historical rating, low-rating share, and written comments together.
- Use the star distribution and date intervals to understand the feedback behind the totals.
- Compare praise and complaints by category. The group rating averages the locations’ historical ratings.
2. Location comparison: find branches to investigate
Compare locations using the same period, then identify the branches that need a closer look.
Downtown has 18 low-rated reviews out of 104, or 17.3%; Park has 11 out of 86, or 12.8%. Read the underlying comments to understand what is driving the difference.
- Read negative percentages alongside review counts and written-comment sample sizes.
- Use the top complaint topic to guide your investigation.
- Prioritize sustained deterioration between comparable periods, even when the historical rating remains strong.
3. Category matrix: compare the same topics across locations
AI categorization turns review text into comparable topics, making differences between branches easier to investigate.
Downtown has six Product complaints among 74 written comments: 8.1%. Each cell uses all written comments at that location as its denominator.
- Read each location’s sample size alongside the cell percentage and count. Darker colors indicate a higher share of complaints.
- A review counts once within each category and can mention several topics, so row percentages need not add up to 100%.
- Explore subcategories and open a cell to read matching reviews. Customers can edit categories and correct classification and sentiment.
4. Location detail: understand one branch’s experience
Bring the location’s metrics, historical rating trend, and category-level feedback into one view.
The sample review praises the food but criticizes wait times and coordination. Categorization separates these opinions so the team can investigate the right part of the experience.
- Read period review volume, historical rating, low-rating share, and written comments together.
- Compare praise and complaints within each category; one comment can contain both.
- The rating trend remains historical. Date filters do not turn it into a period average.
5. Complaints: read the evidence behind each issue
Inspect what customers actually said before discussing the problem with the responsible team.
The demo contains 46 comments with complaints among 392 written comments. This differs from the 59 reviews rated 1–3 stars: complaints are identified in the text, including in high-rated reviews.
- Keep the date, review ID, location, categories, and original rating together.
- Filter by category or search the comments to investigate a specific issue.
- Open matching reviews from the matrix with filters preserved, including complaints in four- and five-star reviews.
Compare periods to spot sustained deterioration
Use a weekly review to compare the same locations and categories over comparable date ranges. Pay particular attention to a branch that repeatedly worsens in the same category, rather than judging it only from a single week or its accumulated rating.
Cacao’s on-screen period comparison uses two manually selected periods to compare total review volume and the share of 1–3-star reviews. To investigate category changes, inspect the category matrix for each selected date range and read the matching comments. These are distinct ways of comparing the information.
An illustrative comparison: 600 reviews with 12% rated 1–3 stars in one period, followed by 648 reviews with 9.1% in the next. Volume increased while the low-rating share decreased. The comments and category breakdown help investigate what changed.
For the interpretation method, read how to analyze Google reviews. For a repeatable review schedule, see how to monitor Google reviews across multiple locations.
Use alerts to notice problems between weekly reviews
The weekly dashboard review reveals patterns. Alerts help the responsible team notice negative reviews between scheduled checks, so corporate does not have to discover them by occasionally opening individual profiles.
Cacao’s category-based negative-review alerts classify and route reviews according to the configured rules. Analytical categories can describe several topics in a review; alert routing selects one category for that notification.
Your team can use the information to contact the responsible person, discuss the problem with the location, agree on a correction, and review subsequent feedback. The operational work and confirmation of resolution remain your team’s process.
Keep the right locations visible to each team
Corporate, regional, and local teams can work within their assigned scope through role-based location access. Connecting the profiles and assigning access gives corporate a way to review the network without opening each Google profile separately.
When a review needs a reply, continue through Cacao’s review response workflow. For sharing the findings, see the multi-location reporting guide.
Who is this Google reviews dashboard software for?
Cacao adapts the same centralized dashboard to different multi-location operations, from international retail networks to restaurant groups, home services, and healthcare practices.
Steren
800 Google Business Profiles across 9 countriesCorporate, country, and regional teams use one dashboard to compare performance across a large international network.
Explore retail review management
Pizzeria Popular
20,000+ reviews generated in five monthsRestaurant teams can monitor review growth and location performance across a multi-brand, multi-country operation.
Explore restaurant review management
Vital Supermayorista
20 locations and 1,000+ replies in the first monthRetail teams centralize store reviews, supervised AI responses, and location-level reporting in one workflow.
Explore retail review management
Chamtac Construction Services
12 Google profiles across 6 service divisionsService-area teams keep local profiles organized while tracking reviews and visibility across every market.
Read the Chamtac case study
Torreblanca Dental Practices
12 Google profiles managed from one platformDental teams centralize review generation, response workflows, and local performance tracking across practices.
Explore healthcare review managementSee how your locations compare—and what customers are saying about each one
Start your 7-day free trial to explore AI categorization, location comparisons, and the reviews behind the results on your own.
Get startedFrequently asked questions
What does Cacao’s Google reviews dashboard show?
Five connected views: Group overview, Location comparison, Category matrix, Location detail, and Complaints. Teams can compare connected locations, investigate praise and criticism by category, and open the review text behind the metrics.
How does AI categorization help compare locations?
AI identifies categories and sentiment within review text. This lets teams compare the same aspects of the experience across branches. A review can discuss several categories and contain both praise and criticism.
Does changing the date range change the Google rating into a period average?
No. The location rating and its trend are historical. The group rating is the unweighted average of its locations’ historical ratings. Date filters apply to period review volume, negative share, and category analysis.
Are complaints the same as 1–3-star reviews?
No. Complaints are identified in written text, including in four- and five-star reviews. The 1–3-star share counts low-rated reviews as a proportion of all reviews received in the period, whether or not they contain text.
What can I compare between two periods?
Choose two periods manually to compare total review volume and negative-review share on screen. To investigate category evolution, inspect the category results for each date range and read the supporting comments.
Can I compare Cacao surveys and Google reviews as one dataset?
No. Google reviews and Cacao surveys are separate analytical sources; their ratings and comments are not combined into one dataset.
How do alerts complement the dashboard?
Use the dashboard for a weekly review of location and category performance. Negative-review alerts help notify the configured responsible person between reviews. Investigating and resolving the operational issue remains the team’s process.
Can regional and local teams see only their assigned locations?
Yes. Role-based location access determines which locations and groups each user can access.