How do global brands manage ratings and reviews from stores in dozens of countries at a glance? - A case study on integrated monitoring of Google Maps reputation.

"When did the star rating at this location drop like this?"

I remember a quarterly global Customer Experience (CX) review meeting. The slide showed a drop in the star rating of an overseas major city location from 4.4 to 3.6. That's when I first noticed it. Upon reviewing the feedback, complaints like "made a reservation but had to wait for two hours" and "the staff didn't keep their promises" had been piling up for the past two months, with no responses from the manager. The location had already been pushed down from the top of city searches, and it would take half a year to turn it around.

This is a recurring scene in a brand with stores in dozens of countries worldwide. With hundreds to thousands of stores, reputations are built separately for each store.

• It's impossible for people to check the star ratings of each store on Google Maps every day. Eventually, signals of a specific location's decline get swallowed up in a quarterly summary that says 'overall ratings are good.'
• Even if a manager doesn't respond to low-star reviews for months, there's no way to count 'how many locations have been neglected.'
• Across the street, competing brands respond to negative reviews within a day to win back customers, but we have no map showing where we are falling behind in customer service.
• As long as the star rating, a single number, looks fine, there's no signal if waiting times deteriorate within that 4.2 rating or if facilities are deteriorating.

Trying to click on each location one by one won't help catch falling stores in the act.

Collecting Google Maps Reviews and Responses based on our own and competitors' standards

Using Hashscraper, you can collect Google Maps reviews for both our own stores and major competitors' stores under the same category. This includes store ratings, review counts, star rating distributions, and whether each review received a response from the store.

Actual Crawling Data Example - Our Store Reviews (No Response)

{
  "Brand": "자사",
  "Store": "○○ 매장 (도시 A)",
  "Country": "Brazil",
  "Store Rating": 3.6,
  "Review Count": 412,
  "Review": {
    "Reviewer": "M. S.",
    "Rating": 2,
    "Date": "2026-06-20",
    "Body": "예약했는데 두 시간을 기다렸습니다 ...",
    "Owner Reply": { "Replied": "N", "Date": null, "Text": null }
  },
  "Collected At": "2026-07-08"
}

Actual Crawling Data Example - Competitor Brand Store Reviews (Response Received)

{
  "Brand": "경쟁 브랜드",
  "Store": "△△ 매장 (도시 A)",
  "Country": "Brazil",
  "Store Rating": 4.2,
  "Review": {
    "Rating": 2,
    "Date": "2026-06-19",
    "Body": "대기 시간이 길었어요",
    "Owner Reply": {
      "Replied": "Y",
      "Date": "2026-06-20",
      "Text": "불편을 드려 죄송합니다. 예약 응대 절차를 개선하고 있습니다 ..."
    }
  }
}

By comparing these two sets of data, it becomes apparent that while one side responds within a day to the same type of complaints in the same city, the other remains silent. Since response status is structured at the review level, you can extract response rates per location and identify reviews with low ratings but no responses as indicators.

Use Case: Reputation Management for Global Brand Stores in Multiple Countries

A global brand operating stores in dozens of countries had to view store reviews and competitor brand reputations using the same criteria. With hundreds of stores and dozens of languages, it wasn't easy to see which locations were receiving low ratings and whether managers were responding to reviews.

Crawling Configuration

• Targets: Our own stores + major competitor brand stores, across multiple countries
• Data Collection: Store ratings, review counts, star rating distributions, individual reviews (rating, content, date), whether the store owner responded and the response content
• Analysis: Classifying and sentiment analysis of review content by attribute (response, waiting time, facilities, cleanliness, consultation quality, etc.)
• Crawling Frequency: Regular collection

Analyzable Items from Crawling Data

Quantitative Results

By consolidating scattered store reputations into a single table, it became possible to identify declining locations in that week without waiting for quarterly reports.

• Declining Locations — Filtered by the 'Top locations with star rating decline in the last 30 days' view, you can pinpoint the cause by directly opening the reviews that contributed to the decline.
• Neglected Locations — Automatically select locations with 'low ratings but 0% response rate' to identify them by name, rather than assuming they are doing well.
• Competitive Gap — By drawing the city/market-level rating and response rate gaps on a map, decisions can be made on which markets to focus on.

Different Answers from the Same Data depending on the Role

Different departments ask different questions based on the same integrated dataset. Until now, answers were given based on 'intuition' or 'self-reporting.'

One particularly frustrating aspect from the HQ perspective was budget allocation. When a regional entity demanded resources by saying, "Our market naturally has low ratings because customers are picky," it sounded plausible, but there was no evidence to support or refute it.

By collecting all stores at the same frequency and with the same criteria, it becomes possible to quantify statements like "This market has double the average wait time complaints compared to other locations" within the 4.2 rating. This way, budgets are allocated based on real issues in problematic markets rather than to regions with loud voices.

Breaking Down Reviews by Attributes instead of Lump Sum Ratings

Simply looking at a single number like the star rating may make a store seem fine, but it doesn't show which attributes are quietly deteriorating. Hashscraper uses AI to analyze review content in a structured format.

• Attribute Classification & Sentiment Analysis: Grouping sentences by mentioned attributes (response, waiting time, facilities, cleanliness, consultation quality, etc.) and determining positive or negative sentiment
• Multilingual Processing: Translating and categorizing reviews in multiple languages to ensure a balanced sample across countries and aggregate issues based on global standards
• Response Quality Check: Collecting not only response status but also response content to identify locations that respond to negative reviews with formal copy-paste replies

Thanks to this, even within the same 4.2 rating, diagnostics at the attribute level like "recent increase in negative mentions of waiting time" or "this market has an unusually high number of facility complaints" are possible. This allows for prioritizing improvements based on recurring complaints across hundreds of reviews rather than a single loud review.

From Raw Data to Reports and Dashboards

The collected data is provided in raw form. However, to actually use this data, someone needs to process it into reports or dashboards. If each country's personnel manually organize star ratings in different Excel formats, headquarters would spend days just aggregating them, and cross-country comparisons would be abandoned due to mismatched criteria.

Hashscraper can assist with this final step as well. By providing visual dashboards or regular reports that consolidate store ratings, response rates, declining locations, and attribute-specific complaint trends by country, operational staff can understand the situation without having to manually check each store. If raw data is needed, it can be directly integrated into internal systems via Excel, email, API, or database connections.

• Review & VOC Analysis Solution: Review & VOC Data

Why is it Difficult to Manage Internally?

Teams attempting to manage Google Maps reputations for stores in multiple countries face a common barrier.

Google Maps lacks features like 'view only unanswered reviews,' 'how many days have passed since a review was neglected,' or 'how much has it dropped compared to last week.' With multiple stores, each page must be opened manually, and collecting data from competing brand stores using the same criteria is unmanageable on a global scale.

Additionally, the operational burden unique to web data collection adds to the challenge.

• Maintenance Complexity: The structure of map and review screens changes frequently, causing collection to quietly stop. With targets spanning dozens of countries and hundreds of stores, this response becomes an ongoing task.
• Continuous Monitoring: Without someone noticing when collection in a country drops to zero or only half comes in, there will be gaps in the data. A system to detect failures and omissions and conduct re-collection is necessary.
• Establishing and Managing Collection Infrastructure: Mass collection across multiple countries requires dynamic IPs, proxies, browser automation, schedulers, and large storage to work together. Setting this up and maintaining it requires dedicated manpower and costs.

Hashscraper takes on this burden entirely. It collects data from multiple country stores regularly, adapts to screen changes and collection gaps, and provides the infrastructure, allowing clients to focus solely on making judgments based on the organized data.

Other Use Cases

Measuring Response Effectiveness — By stacking responses to reviews and ratings over time, compare how store ratings and new review sentiments changed 'before and after responses.' This proves whether stores with high response rates are actually advantageous in defending ratings.

Detecting Review Bombings or Manipulation Signals — Distinguish between a genuine decline in ratings or organized review bombardment. Separate patterns like short-term concentration, similar wording, and extreme rating clustering from gradual genuine declines to focus response resources on real problem locations.

Adjusting Ratings Inflation by Country — Cultural differences in giving generous or harsh ratings vary by country, distorting comparisons between a 4.3 and a 4.1. By adjusting distributions by country, identify the truly top-performing markets.

Formal Response Check — Responses that are merely copy-paste without substance are still counted as 'responded.' By checking response content, ensure that KPIs don't degrade into mere number filling.

Summary

Store reputations are influenced not by a single HQ but by hundreds of locations simultaneously. To understand which locations are deteriorating, which reviews are being neglected, and where we are falling behind compared to competing brands, data must be collected to manage based on evidence rather than intuition or self-reporting.

Hashscraper handles crawler operations, maintenance, multilingual collection, and analysis. If map screens change or collections are missed, there is no need for clients to respond directly. Once set up, store reputations from multiple countries are organized into a single format.

Get Started Now

With Hashscraper, you can collect Google Maps reviews and response data for stores in various countries based on our own and competitors' standards, and expand the analysis and visualization capabilities.

Explore Review & VOC Analysis

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