The Client
The client was a global quick commerce and food delivery platform operating across the USA, UK, Ireland, Australia, and Canada. Ahead of Halloween 2025, they aimed to analyze how regional delivery apps and restaurant partners were structuring festive food offers — from percentage discounts and combo packs to add-ons and time-bound flash deals. Their goal: Identify pricing and promotional trends across these five countries, understand which cuisines and categories (fast food, desserts, beverages, bakery, etc.) saw the highest offer engagement, and benchmark competitor offer strategies across platforms to maximize promotional ROI and real-time campaign optimization. To achieve this, they collaborated with Food Data Scrape to build a comprehensive Halloween offer scraping pipeline spanning five key markets.
Key Challenges
- High Frequency of Offer Updates: Platforms like Uber Eats and DoorDash modify offer banners and coupons multiple times daily. Manual tracking couldn’t keep up with such real-time pricing fluctuations.
- Regional Discrepancies in Offer Visibility: Discounts and add-on menus differed between countries, even for the same restaurant chain (e.g., McDonald’s Halloween Sundae in the USA vs. UK-exclusive Pumpkin Shake).
- Limited Access to Dynamic Web Data: Offer details were often embedded in dynamic JavaScript or JSON layers, making food offer data extraction complex without advanced web scraping.
- Inconsistent Offer Classifications: “Buy 1 Get 1,” “Combo Meals,” “Free Add-on,” or “Limited-Time Discounts” were labeled differently across platforms — requiring data normalization and tagging.
Key Solutions
- Dynamic Offer Scraping Engine: Automated scripts extracted live offers from top food delivery platforms’ category and banner sections across five countries. Each record included restaurant name, cuisine, offer type, discount %, duration, and add-on items.
- Menu and Add-on Data Integration: Leveraged menu scraping APIs to correlate offers with actual dishes. This enabled the system to identify whether “Free Dessert Add-ons” or “Combo Upgrades” were linked to premium products.
- Geo-Specific Offer Mapping: Captured region-specific Halloween promotions, allowing direct comparison between local and global chains in each country.
- Discount Analytics Dashboard: Developed an internal visual dashboard for the client showing discount distribution by category, platform, and location — enabling instant pricing intelligence.
- Competitor Offer Tracking: Using real-time scraping algorithms, the platform continuously monitored changes in competitor campaigns, identifying top-performing price patterns and engagement surges.
Methodologies Used
- Targeted Offer Data Collection: Using automated food offer scraping tools, Food Data Scrape gathered structured records from over 35,000 listings: Restaurant name & platform, Offer type (flat %, BOGO, combo, add-on, or free delivery), Discount value, start-end time, Cuisine type & city, Customer ratings (where available).
- Data Cleaning & Standardization: All offer data was normalized into consistent tags: Flat Discount, Combo Offer, Add-on Deal, Flash Sale, Limited-Time Offer, etc. Duplicate and expired listings were automatically filtered out.
- Multi-Region Scraping Logic: Country-specific scraping nodes were deployed to handle timezone differences and local platform URLs — ensuring accurate Halloween deal tracking without data overlap.
- Offer Classification Engine: Using NLP and machine learning tagging, Food Data Scrape automatically categorized unstructured promotional text like “Get a Free Spooky Shake” or “Midnight Feast Offer” into predefined offer types.
- Interactive Visualization: All outputs were visualized in a Power BI-styled dashboard, enabling city-wise filtering and platform comparison. Data refresh frequency: every 4 hours.
Sample Data Snapshot
Advantages of Collecting Data Using Food Data Scrape
- Comprehensive Multi-Country Visibility: A unified dataset allowed real-time comparison of Halloween discounts across 5 geographies — revealing pricing gaps and untapped markets.
- Dynamic Offer Intelligence: The automated scraping engine provided hourly updates, ensuring marketing teams never missed time-sensitive flash deals.
- Competitor Benchmarking: The client could track how rival platforms structured their Halloween offers — by category, cuisine, and city — gaining a decisive competitive advantage.
- Revenue and Engagement Forecasting: Offer frequency and average discount value correlated directly with delivery surge data, enabling data-driven demand forecasting for upcoming festive seasons.
- Ad-Tech Integration: Scraped offer metadata was integrated with the client’s ad-platform to automatically trigger promotional bids during high-engagement hours.
Client’s Testimonial
“Food Data Scrape’s Halloween offer analytics completely transformed our approach to festive discount planning. The multi-country scraping dashboard gave us a real-time pulse of the market, helping us refine pricing across our partner restaurants. It’s like having eyes on every competitor’s festive campaign — live.”
Head of Growth Strategy, Quick Commerce Partner – Europe
Final Outcome
The project empowered the client to make data-driven marketing and pricing decisions during the busiest festive season of the year. By leveraging discount and combo scraping, real-time analytics, and multi-region normalization, the client achieved: 31% higher conversion rates on time-bound Halloween deals, 22% faster decision-making for pricing and promotions, and 100% automated visibility into regional competitor campaigns. Food Data Scrape’s advanced offer scraping infrastructure enabled real-time insights for dynamic campaign management across continents — bridging the gap between raw data and actionable marketing intelligence.
