PROJECT REQUIREMENTS

Scraping Listing Data from a Business Directory for Market Research

The client needed SunTec Data’s website data scraping expertise to systematically harvest comprehensive business listing information for approximately 150 leading global brands across various metropolitan areas. This data (extracted from a prominent online directory) would enable the client to develop a comprehensive market intelligence database, enhancing their research, competitive analysis, and consulting capabilities.

The required dataset involved the following critical attributes:

Business names, physical addresses, and precise geo-coordinates
Contact details, including phone numbers and email addresses
Official website URLs
Current operating hours
Customer feedback metrics: ratings, reviews, and detailed service offerings

PROJECT CHALLENGES

Overcoming Security, Complexity & Scalability Hurdles

During the execution of this massive data extraction assignment, our team encountered several technical and structural obstacles specific to the target business directory platform:

Advanced Anti-Scraping Mechanisms- The directory employed sophisticated defenses (dynamic response generation, request monitoring, and CAPTCHA implementation) designed to thwart automated large-scale data collection. Our solution required mimicking authentic human browsing behavior to ensure every data request was accepted.
Dynamic Content Loading- Critical data points, particularly detailed customer ratings and reviews, were rendered using JavaScript (JS) after the initial page load. Standard HTML parsing was insufficient, necessitating the use of advanced browser automation techniques.
Encoded and Obfuscated Contact Details- Sensitive contact information, such as phone numbers, was often masked using CSS class-based encoding. These details were intentionally hidden from direct extraction, demanding a custom decoding logic to retrieve accurate and usable contact numbers.
Data Diversity and Format Inconsistency- We had to collect data for over 150 brands across different geographies (ex- a Starbucks listing in London and Berlin). Discrepancies arose because local branches presented information differently or included more details (like hours, services, or reviews) than others, or used different formatting conventions (e.g., “5th Ave” vs “Fifth Avenue”). Even for the same brand, listings across regions were categorized differently (e.g., “Café,” “Coffee Shop,” “Restaurant,” etc.). These variations made data cleansing and standardization necessary.
Need for Scalability and Stability- Processing thousands of brand-location combinations demanded a solution that could scale horizontally. The system required resource optimization and advanced configurations to maintain speed, accuracy, and operational stability without interruptions.

OUR SOLUTION

Automated Web Scraping with Built-In Anti-Bot Defenses

To overcome the security limitations and ensure smooth data extraction at scale, our team engineered a customized, end-to-end website data scraping pipeline tailored for the target directory's complex environment.

Hybrid Extraction of Static & JS-Rendered Content

We deployed a unified stack that combines Scrapy for rapid, high-volume crawling of static fields (such as names and addresses) and Selenium, operating in headless mode, for pages that require rendering to capture JS-loaded content (such as reviews and ratings).

Adaptive Anti-Bot & CAPTCHA Evasion

We successfully bypassed anti-scraping countermeasures through:

Rotation of residential proxies and randomization of request headers.
Adaptive crawling speeds and intelligent retry logic to simulate natural user patterns.
Implementation of CAPTCHA detection and automatic re-queuing for uninterrupted data collection.

Data Normalization, Enrichment & Validation

We established a unified data schema to standardize all extracted information. Inconsistent formats—such as address abbreviations, varying phone number styles, and dissimilar rating scales—were systematically cleaned, enriched, validated, and normalized to ensure a consistent output ready for the client’s analysis tools.

Contact Reconstruction & Pagination Handling

A custom Python dictionary mapping system was developed to accurately translate the coded CSS classes back into actual phone numbers, thereby reconstructing the complete contact numbers.

We built adaptive logic to detect whether search results spanned a single page or multiple pages, ensuring the scraper systematically navigated and captured all listings via intelligent URL parameter analysis.

Error Management and Hybrid QA Validation

We implemented robust error handling, including retry logic with exponential backoff to mitigate temporary site restrictions. Crucially, we adopted a hybrid QA approach, supplementing automated real-time data validation with a team of data specialists who performed manual verification and refined scraping parameters to ensure 99% data accuracy.

Cloud-Based, Scalable Deployment

The entire solution was hosted on a secure Virtual Private Server (VPS), designed for executing parallel scraping across various combinations of brand and location queries. The process was fully automated via scheduled tasks, and the system also tracked each scraping cycle, providing detailed logs and reports, thus ensuring complete transparency.

Project Outcomes

50,000+ Verified Listings & Reduced Time-to-Insight

Our team successfully and securely delivered over 50,000 verified business listing records. This ready-to-use dataset empowered the client's global strategy, resulting in measurable business growth:

50,000+ business listing records harvested from a protected directory platform.

99% data accuracy achieved through automated website data scraping and hybrid human data validation.

45% reduced Time-to-Insight, directly enabling faster strategic client advisory and market research delivery.

Empowering a Global Consulting Firm through Business Listing Data Extraction

A Global Consulting Firm