Harnessing Web Scraping to Drive Dynamic Pricing Decisions
Client Overview
The client is a leading global online food ordering and delivery platform with operations spanning over 6,000 cities worldwide. The company offers diverse delivery methods and serves millions of customers through its highly scalable digital platform. Headquartered in the USA, the organization has become a household name, connecting restaurants and consumers with seamless, efficient, fast service. The company is committed to providing a superior user experience and improving the efficiency of its internal processes through advanced technological solutions.
Blueprint for a Competitive Edge: Data Depth, Precision, and Performance
Full-Stack Scraping:
Build a comprehensive data collection engine capable of gathering everything from basic store-level info to deep item and modifier-level details, including Store-level data like store_id, address, city, state, zip_code, latitude, longitude, store_page_url, and store_name, Menu item data including item_category, item_id, item_name, item_price, item_description, and item_calories and Modifier data such as modifier_group modifier_calories, min_quantity, max_quantity, and default_quantity.
Track Discrepancies with Precision:
Enable systematic price comparison between scraped competitor/in-store prices and platform-listed rates to help the client quickly surface pricing inconsistencies, mismatches, or opportunities for strategic pricing moves.
Automating the Entire Data Pipeline:
Design a fully automated scraping solution that eliminates the need for manual monitoring or data pulls. From API calls to final output formatting, every step had to run like clockwork, with fail-safes in place.
Adapt to Any Merchant, Any Format:
The system had to work across various merchant websites, each with its own quirks and structures. The scraping framework needed to be modular and adaptable, so adding new merchants didn’t require starting from scratch.
Inside the Engine: How the Solution Was Built and Delivered
Transforming messy merchant webpages into structured, accurate pricing intelligence required more than scraping; it demanded a strategic blend of exploration, engineering, validation, and delivery.
Identifying the Right Data Sources Across Varied Site Structures
The process began by analyzing merchant websites provided by the client. Each site was examined to pinpoint sections that displayed competitor/in-store pricing data, focusing specifically on options labeled “pickup,” “collection,” or “order online.” This helped isolate pricing paths most reflective of in-store rates, essential for accurate benchmarking.
Extracting Hidden Gold Using Developer Tools and Python
Using browser developer tools, network requests were monitored to identify internal APIs that exposed menu and pricing data. These endpoints were reverse-engineered and replicated through Python scripts, bypassing traditional HTML scraping for a more reliable, structured, and resilient approach.
Scripting for Scale with JSON/HTML Parsers
Depending on merchant implementation, custom scripts were built to extract data, either structured JSON or parsed HTML, programmatically. The scripts were modular and flexible, designed to handle format variability across merchant platforms without rewriting core logic for each new website.
Ensuring Data Trust Through Smart Sampling and Cross-Checks
A validation framework was established to ensure accuracy and reliability. It focused on verifying the top 30 best-selling items for pricing and modifier accuracy and performing cross-verification of modifiers and store-level metadata across a sample set of merchants to ensure consistency and completeness.
Transforming Raw Feeds into Usable Pricing Intelligence
Extracted raw data was parsed and normalized into structured CSV formats, ensuring compatibility with downstream systems.
Breaking Barriers: Engineering Around the Web’s Toughest Scraping Shields
Web Scraping, Real Impact: Data That Drove Pricing Precision
Precision Price Benchmarking:
Systematically enabled comparison between competitor/in-store prices and client-listed items, equipping the client with clear visibility into pricing gaps and opportunities.
10x Faster, 100% Smarter:
Replaced manual data collection with an automated, asynchronous scraping engine, accelerating data extraction speed by 10x while reducing operational effort.
Data You Can Decide On:
Delivered clean, structured, and high-accuracy pricing data that directly informed and shaped strategic pricing decisions.
Framework for the Future:
Built a reusable, scalable scraping framework, designed to easily plug into future merchant data pipelines and expand data capabilities beyond the initial scope.
About Indium
Indium is an Al-driven digital engineering company that helps enterprises build, scale, and innovate with cutting-edge technology. We specialize in custom solutions, ensuring every engagement is tailored to business needs with a relentless customer-first approach. Our expertise spans Generative Al, Product Engineering, Intelligent Automation, Data & Al, Quality Engineering, and Gaming, delivering high-impact solutions that drive real business impact.
With 5,000+ associates globally, we partner with Fortune 500, Global 2000, and leading technology firms across Financial Services, Healthcare, Manufacturing, Retail, and Technology-driving impact in North America, India, the UK, Singapore, Australia, and Japan to keep businesses ahead in an Al-first world.
