Web Scraping for Competitive Market Insights: Powering $3 Billion in EBITDA Through Data-Driven Pricing 

Setting prices for products is similar to adjusting the sails on a boat. If you don’t read the wind properly, you’ll either be stuck in place or heading in the wrong direction. Data is the wind that helps you steer a steady course.

In an economy where every dollar counts, businesses can’t afford to guess when it comes to pricing. They need data-driven pricing, the kind of precision that turns market insights into clear, actionable strategies.

This case study is about a consulting firm that used high-volume, accurate market data from Grepr to empower their clients. We’ll explore how the pricing decisions suggested from data-driven insights led to over $3 billion in EBITDA improvements. 

And, how the right data, when handled correctly, can make all the difference in staying competitive and profitable in a price-sensitive world.

About the client

A pricing and analytics consulting firm based in the USA approached Grepsr for a large-scale home improvement sites data extraction project. 

They specialize in helping manufacturers, distributors, and retailers optimize pricing strategies with data-driven pricing insights. 

As of today, the consultant has collectively helped its clients generate billions.

Requirements

The consultant needed a dependable source of high-volume, accurate, and consistent market data from major home improvement and retail websites across multiple product categories and store locations. 

By gathering insights from the high-volume and accurate data, they would be able to support data-driven pricing decisions for their clients.

To achieve that, they needed a data partner who could:

1. Deliver High-Volume Market Data

Collect large volumes of product and pricing data across multiple retailers, categories, and store locations to support comprehensive pricing analysis.

2. Ensure Accuracy and Consistency

Provide clean and structured datasets with consistent field coverage so analysts can confidently use the data in pricing models and recommendations.

3. Support Recurring Data Needs

Maintain consistent data delivery on scheduled runs so pricing decisions can be based on current market conditions.

4. Maintain Reliable Coverage at Scale

Ensure stable extraction across multiple sites and locations so the datasets remain dependable over time.

Complex site challenges and unforeseen obstacles

Even though the requirements were clear, the real battle was extracting data from sites fortified with heavy anti-scraping defenses. 

Retailers like Home Depot made it a high-stakes game, where every step forward felt like overcoming a new roadblock.

1. Battling Site-Level Restrictions

Advanced anti-scraping measures like IP blocks, CAPTCHAs, and dynamic content loading made data retrieval inconsistent. Critical product listings were either missing or incomplete.

2. Managing High-Volume Extraction

The need to extract massive volumes of data across 18 categories and 5 store locations created immense scale challenges. Traditional scraping methods became slow and inefficient.

3. Dynamic Content and Data Complexity

Pages were loading dynamically, often with AJAX (Asynchronous JavaScript and XML), making it difficult to extract all necessary product details. 

4. Prolonged Run Times and Instability

Scaling the extraction process resulted in long run times (up to 150 hours) and system instability. Any interruption meant a restart, further stretching deadlines and risking the reliability of the data.

Solution: Trial and error to find the silver bullet

To tackle each problem, we put our 12+ years of expertise to work. We tested different workarounds to figure out what worked best in collecting accurate data. 

Phase 1: HTML Parsing 

The first approach involved scraping visible HTML content directly from retailer websites. While this seemed to be a quick fix, it quickly became clear that many pages did not load all product information in the HTML source. 

As a result, a large portion of the data was missing, requiring additional API calls. Even the API calls were often blocked by site restrictions, limiting the ability to collect comprehensive data.

Challenges faced:

• Partial data extraction from HTML source.
• Site restrictions causing data retrieval issues.

Phase 2: Proxy Solutions and Request Optimization

To circumvent IP blocks, various proxy solutions were tested. A provider’s proxy service was used to mask requests, but the solution required multiple retries per listing. 

These were leading to extended run times (sometimes 120–150 hours) and inconsistent data retrieval. So, this phase demonstrated the limitations of using proxies and prompted further optimization efforts.

Challenges faced:

• High request failure rates.
• Long processing times, making large-scale scraping impractical.

Phase 3: API Integration with Limited Returns

The next step involved integrating an API service to pull data directly. 

The API provided a structured way to retrieve data, but it had major limitations:

• Data return restrictions: The API returned a limited number of pages per request (e.g., only 15 pages per call), causing incomplete data extraction.
• Inconsistent store selection: The API sometimes returned data from random stores instead of the client’s targeted store locations, making the data unreliable for location-based insights.

Despite these issues, the team saw the potential in this approach and started adjusting it for more targeted data retrieval.

Challenges faced:

• Limited number of pages per API call.
• Random store data returned, complicating analysis.

Phase 4: Optimized API Usage with Store-Level Control

The breakthrough came when the team fine-tuned the API service to fully control store-level targeting. By passing specific store identifiers and category URLs directly into the API, we regained full control over data accuracy.

This adjustment allowed us to ensure data was pulled only from the specified stores.

The API’s now return fully rendered HTML, solving the issue of missing fields from partial data.

By ensuring that each request was processed efficiently within 7-10 seconds, the scraping solution became scalable again.

This final adjustment restored multiprocessing across all 90 store-category combinations, making the entire process more reliable and faster.

Outcome:

• Full control over store-level targeting, providing the expected data.
• Reduced runtime, ensuring faster and more reliable data extraction.

Impact on End Clients: Business Results from Data-Driven Pricing at scale 

In the end, Grepsr successfully built a reliable market intelligence engine for the client. 

This included large-scale web data extraction, anti-blocking strategies, and structured data delivery. 

With this solution, we could provide consistent and accurate competitive market data across retailers, product categories, and store locations.

This enabled:

Better Pricing Decisions

Businesses set prices using real competitive market data instead of assumptions improving both competitiveness and revenue capture.

Higher Profit Margins

Clear visibility into competitor pricing helped identify opportunities for margin improvements across large product portfolios leading to meaningful EBITDA gains.

Faster Market Response

Continuous market monitoring enabled faster reactions to competitor price changes helping protect both sales and profitability.

Smarter Category Strategies

Store and category-level insights allowed businesses to adjust pricing more precisely instead of relying on broad pricing rules.

Confident Decision-Making

Reliable and consistent datasets gave pricing teams and leadership confidence to make strategic pricing and promotion decisions.

Measurable Financial Impact

These data-driven pricing improvements helped support over $3 billion in EBITDA gains across the consultant’s client base.

Strengthening the Relationship and Earning Client Trust

In the end, the success of this project went beyond just delivering great results. Our relationship with the client also got strengthened. By consistently providing high-quality data extraction, we earned their trust and demonstrated our capability to handle complex challenges.

As a result, our collaboration expanded, leading to additional data extraction projects and a deeper, long-term partnership. This trust has become the foundation for ongoing success and continued growth together.

Need accurate market data for smarter pricing? Grepsr’s data extraction services deliver the insights you need to stay competitive. Get started today. 

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