Scrape restaurant ratings and reviews for actionable business insights
                     How We Helped Client Scrape Restaurant Ratings and Reviews for Menu Changes in 
Canada vs the USA
This case study explores how restaurant brands used data-driven insights to refine menus across 
North America. By leveraging tools to scrape restaurant ratings and reviews, brands identified 
recurring customer complaints, popular flavors, and unmet expectations. Canadian customers 
consistently emphasized ingredient quality and dietary transparency, while U.S. diners focused more 
on portion sizes and value for money. These insights helped brands prioritize localized menu 
improvements instead of applying one-size-fits-all changes.
Through restaurant review scraping Canada and USA, clear regional differences emerged. Canadian 
outlets introduced healthier sides, reduced sodium options, and clearer allergen labeling. In contrast, 
U.S. locations experimented with bold flavors, limited-time offers, and upsized meal combos. Ratings 
showed noticeable improvement within months, proving that regional feedback directly influenced 
customer satisfaction and repeat visits.
Finally, scraped food reviews analysis enabled continuous menu optimization. Brands tracked 
sentiment shifts after changes, validated successful launches, and quickly removed underperforming 
items. This data-backed approach turned customer voices into actionable strategy across both 
markets.
The Client
A Well-known Market Player in the Restaurant Industry
iWeb Data Scraping Offerings: Leverage our data crawling services to scrape restaurant menu data.
Population 
State / Territory Number of Served Store Type Growth Rate Stores Dominant (2023–2025)
(Approx.)
New South Wales 88 7.8 million Urban & Drive- +11%
thru
Victoria 70 6.6 million Mall & CBD +9%
Outlets
Client’s Challenges
Queensland 55 5.5 million Suburban Cafes +13%
The cliWenets testrnru Agugslteradl iato u3n4derstand why id2e.8n timcilalilo nmenu itemStsa npdearlfoonrem Setodr edsiff+er1e0n%tly across regions. 
ManagSinogu tchr Aoussst-rcaoliuantry 2re2staurant review 1s.c9r ampiillniogn was compMlaelxl  Cdaufees to varied +p7la%tforms, inconsistent 
reviewT afsomrmanaiats, and lan8guage nuances. 5M41a,0n0u0al tracking Rmegaiodnea l iStt odreiffis cult+ 6to% capture real-time 
customAeurs tsreanliatinm Ceanptit,a lle ading to delayed decisions and missed improvement opportunities.
Territory 9 462,000 CBD Cafes +5%Another major hurdle was conducting a reliable Canada vs USA restaurant review scraping analysis. 
ConsumNoerrt heexrpn eTcetrraititooryns, 5rating behaviors, a2n47d, 0fe00edback deptAhir pdoiffrte Oruetdle stsignific+an4t%ly between the two 
countries. Without structured comparison, insights remained fragmented, making it hard to identify 
whether issues stemmed from taste preferences, pricing sensitivity, or service standards.
Additionally, the client lacked scalable restaurant data extraction services. Existing tools failed to 
handle large review volumes, frequent updates, and sentiment classification. This resulted in 
incomplete datasets, biased insights, and limited confidence in using reviews to guide strategic menu 
changes across markets.
Our Solutions: Restaurant Data Scraping
We implemented a centralized data framework that unified reviews, ratings, and menu performance 
across platforms. By building structured food delivery app menu datasets, the client gained visibility 
into item-level feedback, pricing variations, and demand patterns for both Canadian and U.S. markets. 
This eliminated guesswork and enabled faster, evidence-based decisions.
To convert raw data into strategy, we deployed advanced restaurant data intelligence services. Our 
solution applied sentiment analysis, trend detection, and regional benchmarking, allowing teams to 
identify which menu changes would resonate locally. Dashboards highlighted emerging preferences 
and declining items in real time.
Finally, we enabled secure pipelines to extract online food delivery website API data at scale. 
Automated updates ensured fresh insights without manual effort, helping the client continuously 
refine menus, test new concepts, and improve customer satisfaction across regions.
Data Category Canada – Insights USA – Insights Data Source Action Taken Business Impact
Identified Identified
Reformulated 
Delivery apps & ingredients in +18% rating uplift 
Average Rating Trend 4.2/5 3.8/5 review platforms Canada; upsized overall
meals in the USA
Health-focused 
Common Review “Fresh” (32%), “Value” (35%), Customer reviews & variants in Canada; Improved regional 
“healthy” (28%), “filling” (30%), 
Keywords ratings bold flavors in the relevance
“low sodium” (20%) “spicy” (25%) USA
Excess salt (25%), Reduced sodium, 
Menu Item unclear allergens Small portions Scraped reviews clearer labels; 15% drop in 
Complaints (30%), pricing (22%) negative reviews
(18%) combo pricing
Promotions (38%), Highlighted sourcing 
Positive Sentiment Transparency (40%), Sentiment analysis 20% increase in 
Drivers sustainability (35%) limited-time offers engine details; frequent repeat orders
(32%) offers
Replaced with 
Plain menu items 
Underperforming Fried sides (22%), Menu performance healthier sides; 
(25%), unseasoned 12% sales growth
Items sugary drinks (18%) meals (20%) data added flavored 
variants
Weekday evenings Weekends & late Order behavior Adjusted promotions +10% conversion 
Peak Order Times (6 PM – 9 PM, 45% nights (7 PM – 11 
datasets timing rate
orders) PM, 55% orders)
Minimal price 
Price Sensitivity Moderate (35% price High (50% price Pricing & review changes; value Reduced churn by 
complaints) complaints) correlation 8%
bundles
Review Volume Automated data Smarter campaign Better demand 
2,500 reviews/month 5,800 reviews/month
Growth extraction planning forecasting
Web Scraping Advantages
Gain Real-Time Insights – Access up-to-date restaurant ratings, reviews, and menu performance to 
make informed business decisions.
Regional Analysis Made Easy – Compare trends across markets like Canada vs USA to tailor offerings 
for local customer preferences.
Automated Data Collection – Reduce manual effort by continuously extracting large volumes of 
reviews, ratings, and menu information.
Identify Opportunities & Risks – Detect underperforming items, negative feedback patterns, and 
emerging customer preferences quickly.
Support Strategic Menu Optimization – Use actionable insights from scraped data to improve menus, 
promotions, and customer satisfaction efficiently.
Final Outcome
The final outcome of our engagement delivered measurable improvements in menu performance and 
customer satisfaction. By leveraging customer sentiment analysis for restaurants, the client was able 
to pinpoint region-specific preferences and address recurring complaints efficiently. This enabled 
more informed decisions for menu redesigns and promotional strategies, tailored to both Canadian 
and U.S. markets.
Through food delivery reviews data extraction, the client gained access to structured, real-time 
feedback across multiple platforms, eliminating the need for manual tracking. Insights from this data 
guided product innovation, optimized pricing strategies, and improved overall dining experiences. 
Ultimately, the client reported higher ratings, increased repeat orders, and stronger customer loyalty, 
demonstrating the tangible business value of leveraging actionable restaurant review data.
Client’s Testimonial
"Working with this team has completely transformed how we understand our customers across 
Canada and the USA. Their data scraping services provided us with real-time insights into restaurant 
ratings, reviews, and menu performance, helping us identify trends and optimize our offerings 
effectively. The automated processes saved us countless hours of manual work, and the detailed 
analysis allowed us to make data-driven decisions with confidence. Thanks to their expertise, our 
customer satisfaction scores and repeat orders have improved significantly. I highly recommend their 
services to any brand looking to leverage restaurant data intelligently.“
— Head of Operations
FAQ’s
What is restaurant data scraping and how does it work?
Restaurant data scraping involves extracting reviews, ratings, menus, and pricing information from 
online platforms. This structured data helps brands analyze customer feedback and market trends.
How can scraped reviews improve my menu offerings?
By analyzing customer sentiments, preferences, and complaints, brands can identify popular items, 
underperforming dishes, and regional taste differences, guiding menu adjustments.
Can you scrape data from multiple countries simultaneously?
Yes, our services support cross-country review scraping, enabling insights from markets like Canada 
and the USA for comparative analysis.
Is the data collected in real-time?
Absolutely. Automated pipelines ensure real-time data extraction, keeping insights current for timely 
decision-making.
How can this data impact customer satisfaction?
Analyzing reviews helps optimize menus, promotions, and service strategies, leading to higher ratings, 
repeat orders, and stronger customer loyalty.