India Retail Brand Reputation Index 2026: Google Maps Ratings by City from Store Review Data Scraping

Executive summary

India's organised retail has expanded from metros into hundreds of towns, and shoppers increasingly check Google Maps before visiting a store. A store's rating, its review count and the tone of recent reviews now shape footfall as much as signage does. Yet most brands still look at reviews one store at a time.

This report uses Store Review Data Scraping across Google Maps to build an India Retail Brand Reputation Index. It compares store ratings, review volume, complaint themes and owner response rates across eight retail sectors and ten cities, based on an illustrative sample of 12,000 stores and 1.8 million public reviews.

Key illustrative findings:

• The national median store rating is 4.1, but the gap between the best and worst sectors is 0.6 stars.
• Pharmacies and beauty stores rate highest; electronics service and large-format grocery rate lowest.
• Tier-2 cities rate slightly higher than metros for the same brands, with fewer complaints about crowding.
• Billing queues and staff behaviour are the top two complaint themes nationally.
• Only 23% of negative reviews receive an owner response, and stores that respond show faster rating recovery.

Why store reviews deserve an index

Brand-level surveys run once or twice a year. Store reviews arrive every day, from real customers, at every location. Aggregated, they show:

• Which stores, cities and regions are improving or slipping.
• Which operational issues matter most to customers.
• How a brand compares with competitors in the same street or mall.
• Whether management responses are working.

An index makes these signals comparable over time and across sectors.

Methodology

Sectors and stores

The illustrative sample covers eight sectors, with multiple national and regional brands in each:

Cities

Ten cities were included: Mumbai, Delhi NCR, Bengaluru, Hyderabad, Chennai, Kolkata, Pune, Ahmedabad, Jaipur and Lucknow.

Data collection

Store review data scraping collected, for each store's Google Maps listing:

• Overall rating and total review count.
• Rating distribution from one to five stars.
• Public reviews from the last 12 months: star rating, text, date, language and whether the owner responded.

Reviewer identities were not collected.

Theme classification

Each review was classified by an AI model into one or more themes: billing speed, staff behaviour, product availability, pricing, cleanliness, store ambience, returns and exchange, parking and access, service turnaround (for service centres) and food quality (for QSR).

Index construction

The Reputation Index for each sector or city combines four components, each scaled to 0 to 100:

Key findings

1. Sector ranking

Service centres score lowest by a wide margin. Their reviews are dominated by turnaround time and communication complaints, and response rates are low.

2. City ranking

Metros generate more reviews per store but slightly lower ratings, mainly because of crowding and billing queues at peak times. Tier-2 cities rate higher, though complaints about parking and product availability are more common.

3. National complaint themes

4. Responding to reviews pays off

Stores that respond to most negative reviews showed better rating momentum in the sample. Responding does not fix the underlying problem, but it signals care to future shoppers and often leads customers to update their reviews.

5. Review language

About 82% of reviews in the sample were in English, 11% in Hindi (in Devanagari or Latin script) and 7% in other Indian languages. Regional-language reviews were more common in Chennai, Kolkata and Ahmedabad. Brands that analyse only English reviews miss a meaningful share of customer feedback in these cities.

6. Rating spread within brands

Even within one brand, the gap between its best and worst store in a city often exceeds one full star. For multi-location brands, the store-level spread is often a bigger issue than the brand average. A brand with an average of 4.1 might have 15% of stores below 3.7, and those stores pull down search visibility in their neighbourhoods.

Sample data

The record below shows the structure of one store observation.

{
  "sector": "Fashion and apparel",
  "brand_code": "FA-03",
  "store_code": "FA-03-PUN-012",
  "city": "Pune",
  "place_rating": 4.0,
  "total_reviews": 1530,
  "reviews_last_90d": 118,
  "avg_rating_last_90d": 3.8,
  "avg_rating_prev_90d": 4.0,
  "negative_share_last_90d": 0.21,
  "owner_response_rate_negative": 0.12,
  "top_negative_theme": "billing_speed",
  "languages": {"en": 0.86, "hi": 0.09, "mr": 0.05},
  "collected_at": "2026-10-06"
}

7. Seasonal patterns

Review volume rises sharply in festive periods, and so does the share of complaints about queues. Brands that add billing capacity and staff before festive peaks protect their ratings when the most shoppers are looking.

What brands should do with this data

Rank every store by risk. Combine low rating, negative momentum and low response rate into one store risk score.

Fix the top two themes locally. Most stores have one or two dominant issues; staffing at peak times and staff training usually come first.

Set a response target. Aim for a reply to every negative review within 48 hours.

Analyse all languages. Include Hindi and regional-language reviews in theme analysis.

Benchmark locally. Compare each store with competitor stores within the same mall or 2 km radius.

Uses beyond retail operations

• Mall operators use store ratings to understand tenant performance and visitor experience.
• Investors use reputation momentum as an early signal of operational quality.
• Franchise owners use benchmarks to show performance against peers.
• Consultants use the index to prioritise store-improvement programmes.

Limitations

Ratings reflect customers who choose to leave reviews and may not represent all shoppers.

Review dates on some listings are shown as relative times; dates are approximated where needed.

Some stores have duplicate or unofficial listings, which are merged or flagged during collection.

AI theme classification is reviewed by sampling but may misclassify a small share of reviews.

Conclusion

Store reviews are the most direct, continuous signal of customer experience that Indian retailers have. Store review data scraping makes that signal measurable across sectors, cities and stores. The illustrative index shows that ratings vary widely, that a few operational themes drive most complaints, and that responding to reviews is associated with faster recovery.

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