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GeoIp2\Model\City Object
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            [validAttributes:protected] => Array
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    [postal:protected] => GeoIp2\Record\Postal Object
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                            [iso_code] => OH
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 country : United States
 city : Columbus
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    [continent] => North America
    [continent_code] => NA
    [country] => United States
    [country_code] => US
)
Navratri Mega Sale Price Tracking

Introduction

In today’s digital-first dining ecosystem, restaurant discovery is no longer driven solely by proximity or cuisine type—it is powered by data. Online reviews, ratings, and customer sentiment have become the most influential factors shaping consumer dining decisions. However, relying on star ratings alone often leads to misleading conclusions due to review bias, outdated feedback, or inconsistent rating behavior across platforms.

To address this challenge, Actowiz Solutions partnered with a leading food discovery platform to build a comprehensive Restaurant Popularity Index Using Reviews & Ratings (Google + Zomato). The objective was to create a data-driven ranking system that accurately reflects real-world restaurant popularity by analyzing review volume, sentiment, rating consistency, and recency.

By transforming unstructured customer feedback into a standardized popularity score, the client was able to improve restaurant discovery, highlight trending venues, and deliver more reliable recommendations to users across multiple cities.

About the Client

Navratri Mega Sale Price Tracking

The client is a fast-growing food discovery brand focused on helping consumers find the best dining experiences across metropolitan and tier-2 cities. Operating in the food-tech and hospitality intelligence space, the platform aggregates restaurant information, menus, reviews, and ratings to support dining decisions for millions of users.

Their core audience includes urban professionals, frequent diners, tourists, and food enthusiasts who rely on digital platforms to explore restaurants based on trust and authenticity. The brand wanted to go beyond basic star ratings and introduce a smarter ranking model to Analyze restaurant popularity using Reviews & Ratings Data.

By doing so, the client aimed to differentiate itself from competitors, improve user engagement, and offer restaurant partners deeper visibility into their online reputation and performance trends.

Challenges & Objectives

Challenges
  • Fragmented reviews spread across Google and Zomato with no unified structure
  • Difficulty identifying genuinely popular restaurants versus artificially inflated ratings
  • High volume of unstructured text data requiring sentiment normalization
  • Lack of a scalable popularity metric usable across cities and cuisines

To overcome these challenges, the client needed a robust framework to Scrape Google and Zomato restaurant Reviews & Ratings Data and convert it into a standardized index.

Objectives
  • Build a unified popularity score using multi-platform review data
  • Incorporate sentiment, recency, and review velocity into rankings
  • Enable city-level and cuisine-level popularity comparisons
  • Support dynamic restaurant discovery and trend identification

Our Strategic Approach

Unified Review Intelligence Framework

Actowiz designed a structured data pipeline to aggregate reviews and ratings from Google and Zomato into a single normalized dataset. This enabled cross-platform consistency and removed discrepancies caused by platform-specific rating behavior. Using weighted scoring models, we generated Restaurant Popularity Data Insights Using Google & Zomato Data that reflected real consumer engagement rather than surface-level ratings.

Advanced Popularity Scoring Model

Our model combined quantitative and qualitative metrics, including average rating, total review count, review velocity, sentiment polarity, and recency weighting. Restaurants with consistent positive sentiment and recent engagement ranked higher than those with outdated or volatile feedback. This ensured the popularity index remained dynamic and responsive to real-time consumer behavior.

Technical Roadblocks

1. Review Volume & Velocity Management

Popular restaurants generated thousands of reviews across platforms, making data ingestion complex. We implemented distributed crawling and batching mechanisms to manage scale while maintaining accuracy when we Scrape Restaurant Visibility & Popularity Data From Google & Zomato.

2. Sentiment Noise & Language Variations

Reviews included slang, emojis, multilingual text, and mixed sentiments. Our NLP pipelines filtered noise, normalized sentiment scores, and supported multiple languages to ensure fair popularity assessment.

3. Platform Anti-Scraping Controls

Both Google and Zomato deploy dynamic rendering and bot-detection measures. Actowiz addressed this with adaptive request rotation, headless browser automation, and intelligent throttling to maintain uninterrupted data flow.

Our Solutions

Actowiz delivered a scalable, automated popularity intelligence system powered by a custom-built Restaurant Popularity Trends Scraper. The solution continuously collected reviews, ratings, timestamps, and metadata from Google and Zomato, transforming raw data into structured popularity signals.

Our pipeline processed millions of reviews, applying sentiment analysis, temporal weighting, and engagement scoring to generate a single popularity index per restaurant. The output was delivered through dashboards and APIs, enabling the client to display dynamic rankings, trending restaurants, and city-specific leaderboards.

The system was designed to scale across geographies and update rankings in near real time, ensuring the food discovery platform always reflected the latest consumer sentiment.

Results & Key Metrics

  • Processed over 12 million reviews across Google and Zomato
  • Improved restaurant ranking accuracy by 38%
  • Reduced bias from one-off negative or positive reviews
  • Enabled real-time popularity updates across 25+ cities
  • Increased user engagement with ranked listings by 27%

By leveraging Restaurant Data Scraping, the client successfully launched a transparent, explainable ranking system. The Restaurant Popularity Index Using Reviews & Ratings (Google + Zomato) became a core feature of the platform, driving higher trust and repeat usage among consumers.

Client Feedback

“Actowiz Solutions helped us completely redefine how we rank restaurants. The popularity index they built goes far beyond star ratings and truly reflects customer sentiment and engagement.”

— Product Head, Food Discovery Platform

The client emphasized how the Restaurant Popularity Index Using Reviews & Ratings (Google + Zomato) strengthened platform credibility and enhanced the dining discovery experience.

Why Partner with Actowiz Solutions?

  • Deep expertise in large-scale web scraping and sentiment analytics
  • Proven experience in hospitality and food-tech intelligence
  • Scalable, compliant, and secure data extraction frameworks
  • Custom scoring models tailored to business objectives
  • Reliable support and ongoing optimization

Actowiz’s strength lies in transforming raw data into actionable Restaurant Data Intelligence, enabling brands to move from descriptive insights to predictive decision-making with confidence.

Conclusion

This case study demonstrates how data-driven innovation can transform restaurant discovery. By combining advanced scraping, NLP, and analytics, Actowiz delivered a robust popularity framework rooted in Customer Ratings & Reviews Analytics.

With flexible delivery options including Web scraping API, Custom Datasets, and an instant data scraper, Actowiz Solutions empowers food-tech brands to build trust, transparency, and smarter discovery experiences.

FAQs

1. What is a Restaurant Popularity Index?

It is a composite score that ranks restaurants based on multiple factors such as ratings, review volume, sentiment, and recency rather than relying only on star ratings.

2. Why use both Google and Zomato reviews?

Each platform attracts different user behaviors. Combining both ensures balanced insights and reduces platform bias.

3. How often is the popularity index updated?

The system supports near real-time updates, allowing rankings to change as new reviews and ratings are posted.

4. Can the index be customized by city or cuisine?

Yes. The model supports geographic, cuisine-level, and category-based segmentation.

5. Is the solution scalable for global markets?

Absolutely. Actowiz’s architecture is designed to scale across regions, platforms, and languages with ease.

From Raw Data to Real-Time Decisions

All in One Pipeline

Scrape Structure Analyze Visualize

Look Back Analyze historical data to discover patterns, anomalies, and shifts in customer behavior.

Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

Move Forward Predict demand, price shifts, and future opportunities across geographies.

Industry:

Coffee / Beverage / D2C

Result

2x Faster

Smarter product targeting

★★★★★

“Actowiz Solutions has been instrumental in optimizing our data scraping processes. Their services have provided us with valuable insights into our customer preferences, helping us stay ahead of the competition.”

Operations Manager, Beanly Coffee

✓ Competitive insights from multiple platforms

Industry:

Real Estate

Result

2x Faster

Real-time RERA insights for 20+ states

★★★★★

“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

Data Analyst, Aditya Birla Group

✓ Boosted data acquisition speed by 3×

Industry:

Organic Grocery / FMCG

Result

Improved

competitive benchmarking

★★★★★

“With Actowiz Solutions' data scraping, we’ve gained a clear edge in tracking product availability and pricing across various platforms. Their service has been a key to improving our market intelligence.”

Product Manager, 24Mantra Organic

✓ Real-time SKU-level tracking

Industry:

Quick Commerce

Result

2x Faster

Inventory Decisions

★★★★★

“Actowiz Solutions has greatly helped us monitor product availability from top three Quick Commerce brands. Their real-time data and accurate insights have streamlined our inventory management and decision-making process. Highly recommended!”

Aarav Shah, Senior Data Analyst, Mensa Brands

✓ 28% product availability accuracy

✓ Reduced OOS by 34% in 3 weeks

Industry:

Quick Commerce

Result

3x Faster

improvement in operational efficiency

★★★★★

“Actowiz Solutions' data scraping services have helped streamline our processes and improve our operational efficiency. Their expertise has provided us with actionable data to enhance our market positioning.”

Business Development Lead,Organic Tattva

✓ Weekly competitor pricing feeds

Industry:

Beverage / D2C

Result

Faster

Trend Detection

★★★★★

“The data scraping services offered by Actowiz Solutions have been crucial in refining our strategies. They have significantly improved our ability to analyze and respond to market trends quickly.”

Marketing Director, Sleepyowl Coffee

Boosted marketing responsiveness

Industry:

Quick Commerce

Result

Enhanced

stock tracking across SKUs

★★★★★

“Actowiz Solutions provided accurate Product Availability and Ranking Data Collection from 3 Quick Commerce Applications, improving our product visibility and stock management.”

Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

Trusted by Industry Leaders Worldwide

Real results from real businesses using Actowiz Solutions

★★★★★
'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
Thomas Gallao
Thomas Galido
Co-Founder / Head of Product at Upright Data Inc.
Product Image
2 min
★★★★★
“I strongly recommend Actowiz Solutions for their outstanding web scraping services. Their team delivered impeccable results with a nice price, ensuring data on time.”
Thomas Gallao
Iulen Ibanez
CEO / Datacy.es
Product Image
1 min
★★★★★
“Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing highly recommended!”
Thomas Gallao
Febbin Chacko
-Fin, Small Business Owner
Product Image
1 min

See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

Blinkit (Delhi NCR)

In Stock
₹524

Amazon USA

Price Drop + 12 min
in 6 hrs across Lel.6

Appzon AirPdos Pro

Price
Drop −12 thr

Zepto (Mumbai)

Improved inventory
visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

Actowiz's real-time scraping dashboard helps you monitor stock levels, delivery times, and price drops across Blinkit, Amazon: Zepto & more.

✔ Scraped Data: Price Insights Top-selling SKUs

Our Data Drives Impact - Real Client Stories

Blinkit | India (Retail Partner)

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

US Electronics Seller (Amazon - Walmart)

With hourly price monitoring, we aligned promotions with competitors, drove 17%

✔ Scraped Data, SKU availability, delivery time

Zepto Q Commerce Brand

"Actowiz's helped us reduce out of stock incidents by 23% within 6 weeks"

✔ Scraped Data, SKU availability, delivery time

Actowiz Insights Hub

Actionable Blogs, Real Case Studies, and Visual Data Stories -All in One Place

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How We Helped a Hospitality Brand Track 700+ Properties by Scraping Booking.com Hotel Prices in France

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