Swiggy vs Zomato Restaurant Ratings Report 2026 - Comparing Customer Ratings, Reviews, and Restaurant Performance

Introduction

The Swiggy vs Zomato Restaurant Ratings Report 2026 examines how restaurant ratings, customer feedback, menu information, and platform-level restaurant data can be compared to understand restaurant performance in India's digital food-delivery ecosystem.

For restaurants, ratings are more than a reputation metric. They can influence how customers evaluate unfamiliar restaurants, compare alternatives, and decide where to order. For food brands, aggregators, restaurant chains, market researchers, and investors, rating and review data can therefore become an important source of competitive intelligence.

The scale of the two platforms makes this comparison particularly relevant. In FY2024–25, Zomato reported 20.6 million average monthly transacting customers and 297,000 average monthly active food-delivery restaurant partners. Its food-delivery operations covered more than 800 cities.

Swiggy reported approximately 14.7 million monthly transacting users for its food-delivery segment in FY2024–25, with food-delivery GOV of ₹28,783 crore, up 16.4% year over year. Its food-delivery service operated in more than 700 cities.

These figures demonstrate why structured restaurant intelligence can be valuable. A business that tracks ratings, review volumes, menu prices, restaurant categories, locations, and other publicly available attributes can build a historical view of competitive restaurant performance rather than relying on individual observations.

This report focuses on six areas: customer reviews, restaurant ratings, data collection, food-delivery intelligence, customer trust, and menu-price comparison.

What Can Customer Feedback Reveal About Restaurant Performance?

Swiggy vs Zomato Customer Reviews Analysis can provide qualitative context behind numerical ratings. A restaurant with a high rating may still have recurring complaints about delivery time, packaging, portion size, food temperature, or specific menu items. Conversely, repeated positive comments can reveal strengths that numerical ratings alone cannot explain.

A structured analysis can examine publicly available rating and review information alongside restaurant characteristics such as cuisine, location, price range, menu size, and availability.

Key data points

Businesses should not interpret ratings as a direct measurement of food quality. Ratings reflect customer experiences and can be influenced by service, delivery, expectations, price, and other factors.

2020–2026 evolution

From 2020 to 2026, digital restaurant discovery became increasingly important as consumers became more accustomed to ordering food through mobile applications. During 2020–2021, pandemic restrictions accelerated dependence on delivery platforms and pushed restaurants toward digital menus, online ordering, and delivery-led operations. In 2022, customers increasingly returned to restaurants while delivery remained an established habit. During 2023–2024, restaurants continued competing across multiple digital touchpoints, making online reputation increasingly relevant. By FY2024–25, Zomato reported 20.6 million average monthly transacting customers, while Swiggy reported 14.7 million monthly transacting users for food delivery. In 2025, price sensitivity also became more significant in India's consumer foodservice market as inflation contributed to value growth and consumers increasingly considered affordable options. By 2026, restaurant intelligence therefore extends beyond simply identifying highly rated restaurants. Businesses increasingly need historical data to understand changes in ratings, review activity, pricing, menu assortment, and customer expectations. This makes longitudinal restaurant data particularly useful for chains and platforms that want to compare performance across cities, cuisines, and competitors.

How Can Restaurant Rating Data Be Compared Reliably?

Swiggy & Zomato Restaurant Rating Data Scraping requires consistent collection and normalization. A restaurant may have different ratings on Swiggy and Zomato because the customer populations, order experiences, review activity, menu pricing, or platform interactions differ.

A useful dataset should therefore preserve the platform source and collection timestamp.

The restaurant-matching process is especially important. One restaurant can have different names, addresses, abbreviations, or branch information across platforms.

For example, "ABC Kitchen – MG Road" and "ABC Kitchen MG Rd" may represent the same restaurant. Entity normalization helps avoid treating them as two separate businesses.

Platform scale comparison

Swiggy figures are from its FY2024–25 annual report; Zomato figures are from its FY2024–25 annual report. The different financial terminology—GOV for Swiggy and NOV for Zomato—means these figures should not automatically be treated as identical accounting measures.

2020–2026 evolution

The 2020–2026 period demonstrates why cross-platform restaurant comparison needs historical rather than one-time data. In 2020, restaurants rapidly shifted toward delivery and digital ordering as physical dining was disrupted. In 2021, digital menus, delivery operations, and online restaurant discovery became more deeply embedded in restaurant workflows. In 2022, the reopening of dine-in businesses created a hybrid environment in which restaurants increasingly managed both physical and digital customer experiences. During 2023, competition among delivery platforms continued to make digital visibility and customer feedback important commercial considerations. In 2024, platform scale increased further as restaurants used online channels for customer acquisition and delivery. By FY2024–25, Zomato reported operations across 800+ cities and 297,000 average monthly active food-delivery restaurant partners, while Swiggy reported food delivery across 700+ cities and nearly 2.4 lakh restaurant and brand partners. In 2025–2026, the analytical challenge became more sophisticated: businesses increasingly needed to compare not only ratings but also review volumes, menu pricing, restaurant availability, cuisine, and location. A consistent historical collection framework can help distinguish short-term changes from sustained performance patterns.

Why Does Longitudinal Restaurant Data Matter?

One-time restaurant research provides only a snapshot. Restaurant Ratings & Reviews Data Collection creates a historical dataset that can show how restaurant reputation and customer engagement change over time.

Consider a restaurant whose rating moves from 4.2 to 4.4 over six months. That improvement is more meaningful when businesses can also observe whether review volume increased, whether menu prices changed, and whether the restaurant expanded its menu.

Example historical dataset

Illustrative example for demonstrating the analytical model; not platform-reported data.

This type of dataset can support trend analysis, restaurant benchmarking, competitor research, and location-level intelligence.

2020–2026 evolution

Between 2020 and 2026, historical restaurant information became increasingly valuable because digital restaurant profiles evolved from simple listings into rich commercial profiles containing ratings, menus, pricing, images, cuisine information, offers, and customer feedback. During 2020–2021, businesses primarily focused on maintaining online visibility and delivery access. In 2022, restaurant operators increasingly sought to understand how digital feedback affected customer acquisition and retention. By 2023, the growing maturity of online food ordering created greater demand for structured restaurant datasets that could compare businesses across cities and categories. In 2024, platform expansion increased the amount of restaurant information available for competitive analysis. By 2025, Zomato reported 297,000 average monthly active food-delivery restaurant partners, while Swiggy reported nearly 2.4 lakh restaurant and brand partners. At the same time, the Indian food-services market continued expanding. Swiggy and Kearney projected that India's food-services market could exceed US$125 billion by 2030. By 2026, the growing size and complexity of the ecosystem makes historical restaurant datasets increasingly useful for identifying changes in reputation, menu positioning, pricing, and competitive activity. The important shift is from collecting isolated restaurant records to maintaining repeatable datasets that can support longitudinal analysis.

How Can Platforms Turn Restaurant Data Into Competitive Intelligence?

Food Delivery Restaurant Rating Intelligence combines ratings, reviews, restaurant information, pricing, menus, and location data to provide a broader understanding of the digital restaurant market.

For a restaurant chain, intelligence can answer questions such as:

• Which branches have the strongest ratings?
• Which locations receive the most customer feedback?
• Which competitors have higher ratings?
• Which cuisines have stronger customer engagement?
• Are price increases associated with changes in customer perception?
• Which restaurants are gaining or losing digital visibility?

Restaurant intelligence framework

A restaurant chain could calculate a rating gap between its branches and competitors.

Example:

• Chain A average rating: 4.3
• Chain B average rating: 4.5
• Difference: 0.2 rating points

The difference alone does not explain performance. Analysts should also consider review volume, cuisine, location, pricing, and restaurant maturity.

2020–2026 evolution

The role of digital restaurant intelligence changed substantially between 2020 and 2026. In 2020, restaurants primarily used delivery applications to remain operational and accessible during unprecedented disruptions. In 2021, online visibility became an essential component of customer acquisition as consumers relied heavily on digital ordering. During 2022, the reopening of restaurants increased competition between dine-in and delivery experiences. In 2023, businesses increasingly viewed digital profiles as an extension of their brand identity, making ratings, reviews, menus, and pricing important competitive variables. During 2024, restaurant aggregators continued expanding their networks, creating larger datasets for market analysis. By FY2024–25, Swiggy reported that its food-delivery GOV reached ₹28,783 crore, while Zomato reported food-delivery NOV of ₹32,862 crore. In 2025, food delivery continued to be supported by rising digital adoption and changing consumer habits, while India's organized food-services segment was projected to grow faster than the unorganized segment. By 2026, restaurant intelligence can therefore be used not only for reputation tracking but also for competitive benchmarking, pricing analysis, menu research, geographic expansion, and customer-experience strategy.

What Influences Customer Trust in Digital Restaurant Listings?

Customer trust is influenced by multiple signals rather than ratings alone. Swiggy vs Zomato Customer Trust Analysis can examine how rating levels, review volumes, restaurant information, pricing, menu transparency, and availability contribute to the customer's perception of a restaurant.

A high rating accompanied by thousands of reviews can communicate a different level of confidence than a similar rating supported by very few reviews.

Trust indicators

However, rating data should always be interpreted carefully. Platform ratings are not perfectly equivalent across services, and a restaurant's customer mix can vary by platform.

The report should therefore avoid claiming that one platform has "better restaurants" solely because of aggregate ratings. A more reliable approach is to compare matched restaurants, cities, cuisines, and observation periods.

2020–2026 evolution

Customer trust in online restaurant platforms evolved considerably from 2020 through 2026. During 2020 and 2021, customers increasingly depended on digital platforms not only for convenience but also for information about restaurant availability and service. As ordering volumes normalized, consumers gained more choices and could compare restaurants using ratings, reviews, menus, photographs, prices, and delivery information. In 2022 and 2023, the growing number of restaurants and digital-first food businesses increased the importance of reputation signals. During 2024, platform scale and restaurant choice made comparative decision-making easier for consumers but also increased the importance of trustworthy information. In 2025, changes to detailed review visibility on major food-delivery platforms generated public discussion about how much information consumers should receive when evaluating restaurants. India Today reported in November 2025 that users had noticed changes in access to detailed restaurant reviews on both Zomato and Swiggy. At the same time, authorities began exploring how food-delivery-platform data, including customer ratings, could support restaurant inspection prioritization in some contexts. By 2026, customer trust is therefore best understood as a combination of reputation, information quality, consistency, pricing transparency, and service signals rather than a single rating number.

How Do Menu Prices Differ Across the Two Platforms?

Menu pricing is another important variable when comparing restaurant listings. Menu Price Comparison for Swiggy and Zomato can identify differences in listed prices, item availability, portion descriptions, discounts, and menu assortment.

A price comparison dataset can include:

• Restaurant name
• Menu item
• Category
• Listed price
• Discounted price
• Add-ons
• Portion size
• Availability
• Platform
• Location
• Collection timestamp

Example comparison

Illustrative example; actual prices vary by restaurant, location, time, offers, taxes, and platform.

Price comparison should be performed carefully because a displayed menu price may not represent the final amount paid by the customer. Offers, delivery charges, taxes, packaging charges, membership benefits, and restaurant-specific promotions can affect checkout totals.

2020–2026 evolution

Menu-price intelligence became more important between 2020 and 2026 as digital ordering became deeply embedded in India's food-service ecosystem. In 2020, restaurant menus increasingly moved online as delivery became essential. During 2021, restaurants experimented with delivery-oriented menus, pricing structures, combos, and packaging. In 2022, dine-in recovery occurred alongside continued delivery demand, creating more complex pricing strategies across channels. During 2023 and 2024, restaurants increasingly used promotions, bundles, and platform-specific offers to compete for digital customers. By FY2024–25, Swiggy reported an average order value of ₹514 for food delivery, while Zomato reported a net average order value of ₹385; these are company-reported metrics and are not directly interchangeable because their definitions and business contexts differ. In 2025, rising costs contributed to greater price sensitivity among Indian foodservice consumers, with value-oriented formats gaining attention. By 2026, menu-level comparison can therefore support more sophisticated questions around price positioning, promotional intensity, item availability, and competitive assortment. For restaurant chains, this can help identify locations where pricing differs materially from comparable competitors and where menu optimization may be warranted.

Actowiz Solutions provides scalable restaurant and food-market data solutions designed for businesses that need structured, repeatable, and analytics-ready information.

With Swiggy & Zomato Restaurant Data Scraping, businesses can develop customized datasets around restaurant listings, ratings, reviews, menus, prices, cuisines, locations, availability, and other publicly accessible attributes.

The approach can support:

• Restaurant benchmarking: Compare matched restaurants across locations, cuisines, and platforms.
• Rating monitoring: Track changes in ratings and review volumes over time.
• Menu intelligence: Compare menu items, prices, categories, and assortment.
• Competitive research: Identify differences between restaurant brands and local competitors.
• Historical datasets: Maintain recurring snapshots for trend analysis.
• Data normalization: Standardize restaurant names, locations, categories, menu fields, and other attributes.
• Custom delivery: Provide structured data for databases, dashboards, analytics platforms, or research workflows.

Actowiz Solutions can also design collection schedules based on the volatility of the business requirement. Daily monitoring may be appropriate for fast-changing prices, while weekly or monthly collection may be sufficient for strategic market research.

The objective is to provide decision-ready information rather than simply large volumes of raw records.

Conclusion

The Swiggy vs Zomato Restaurant Ratings Report 2026 demonstrates why restaurant intelligence increasingly requires more than a simple comparison of average ratings. Ratings, reviews, menus, prices, restaurant locations, availability, and historical changes can collectively provide a much stronger understanding of restaurant performance.

Swiggy and Zomato operate at substantial scale. In FY2024–25, Swiggy reported food delivery across 700+ cities, while Zomato reported food delivery across 800+ cities. The scale and complexity of these ecosystems create significant opportunities for structured data analysis.

For restaurants and food businesses, the right dataset can help answer practical questions about reputation, pricing, customer engagement, assortment, and competitive positioning.

Actowiz Solutions can support these requirements through scalable Web Crawling service workflows and structured Web Data Mining solutions designed around specific research and business objectives.

Ready to compare restaurant ratings, reviews, menus, and pricing across India's leading food-delivery platforms? Contact Actowiz Solutions today to build a customized restaurant intelligence dataset for your business!