The Client Story
A leading fiber infrastructure company aimed to expand its high-speed internet network across millions of households in major urban centers. Their success depended heavily on identifying underserved regions—areas where local monopolies could be secured without overlapping competitors. Every month, the company commits significant investment decisions, managing a €7 billion infrastructure budget.
However, manual methods of researching competitor coverage proved impractical on a large scale. Analyzing millions of addresses individually across national telecom providers demanded a level of data extraction automation and reliability that their internal resources couldn’t support. Without precise, up-to-date insights into competitor footprints, strategic investment decisions risked becoming inefficient or misguided.
| Industry: | Telecom |
|---|---|
| Cooperation: | Since 2024 |
| Location: | Europe |
We needed a solution that could validate millions of addresses quickly, accurately, and without manual overhead. Delays or inaccuracies weren’t acceptable—our investment roadmap depended on it.
Automated scraping delivered clean, structured coverage data directly into our systems, giving us strategic clarity we never had before.
The Challenge of Scaling Data Collection Across Tens of Millions of Addresses for Telecom Market Research
In today’s telecom landscape, marketing research demands granular, real-time data, not periodic reports or assumptions. Operators must forecast infrastructure ROI with greater precision, detect underserved regions early, and optimize marketing campaigns based on actual local availability.
To meet these needs, web scraping emerged as the only scalable, cost-efficient solution to validate network coverage across 22 million residential addresses. Manual checking was infeasible, and third-party datasets were outdated or incomplete.
Two major obstacles surfaced:
- Data Access Complexity: Validation required navigating form submissions and returning statuses such as ‘available’, ‘unavailable’, or ‘future rollout’.
- Technical Constraints: Sites employed POST requests, CSRF tokens, and unique address encoding, requiring custom-engineered scrapers.
Automation had to be accurate, resilient, scalable, and ethically designed.
Engineering a Scalable, Resilient Web Scraping System
To meet the demands of the large-scale telecom market research, the client required a fully automated, modular, and resilient data extraction architecture. The final solution was engineered around three key pillars:
Intelligent Scraping Architecture
- Dynamic Address Handling:
Engineered logic reconstructed address IDs by combining postal codes, city abbreviations, and house numbers.
- Adaptive Request Management:
Systems handled both simple GET and complex POST requests, dynamically extracting CSRF tokens and managing session authentication.
- Standardized Data Structuring:
Responses across platforms were normalized into three categories—Available, Unavailable, Planned—creating a unified, analyzable dataset.
The ability to trigger targeted analyses ‘on demand’ before each investment decision fundamentally changed how we evaluate expansion opportunities.
Automation, Proxy Infrastructure, and Traffic Optimization
- Scalable Scrapy-Based Automation:
Millions of address queries were processed using robust, error-tolerant spiders.
- Rotating Proxy Network:
Geo-distributed IP rotation ensured consistent access without triggering anti-bot defenses.
- Elastic Load Scaling:
Concurrent thread architecture enabled flexible scaling based on data volume, preventing bottlenecks and maintaining cost efficiency.
Continuous Monitoring, Adaptation, and Resilience
- Live Performance Dashboards:
Real-time visibility into scraper health, task success rates, and system status enabled proactive management.
- Change Detection and Rapid Reconfiguration:
Structural shifts on target sites automatically triggered scraper updates, ensuring uninterrupted, reliable extraction.
- Traffic and Cost Calibration:
Data payloads were measured and optimized (~50–70 KB or ~3 KB per record, depending on source), enabling accurate cost forecasting and budgeting.
Market Intelligence at Scale
Extracting millions of data points was only the foundation.
Ensuring their accuracy, speed, and resilience in the face of constant market shifts defined the true success of this system.
- Complete Process Automation:
Manual address validation was eliminated, freeing internal teams to focus on strategic initiatives instead of repetitive checks.
- 99%+ Data Reliability:
Continuous monitoring ensured that coverage information for millions of addresses remained accurate, clean, and ready for decision-making.
- Strategic Investment Precision:
Expansion efforts were prioritized with real-time, address-level intelligence, which minimized overlap and maximized local market advantages.
- Cost-Efficient Scaling:
The modular scraping infrastructure allowed seamless scaling from 1 million to over 22 million addresses, with only marginal increases in operational costs.
- Acceleration of Market Response:
Investment planning cycles were shortened by up to 50%, enabling the client to move ahead of competitors in newly uncovered markets.
In modern telecom infrastructure strategies, real-time coverage data isn’t optional—it’s survival critical.
With a resilient, scalable data extraction system engineered for agility and accuracy, this company now operates with unprecedented speed, foresight, and market control.
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