Ai Tyre Pricing Data Scraping Case Study – Actowiz Solutions

By Author: Actowiz Solutions
Total Articles: 174
Comment this article

Introduction: Why Pricing Intelligence Matters in the Tyre Industry
The tyre industry is one of the most competitive segments within the automotive sector. Manufacturers, wholesalers, and distributors handle hundreds of SKUs—across multiple brands, models, and sizes—where pricing shifts weekly based on stock levels, regional demand, and promotional campaigns.
For global tyre brands, visibility into how distributors price products across different regions has become critical. With new entrants offering aggressive discounts online, even a small price deviation can affect dealer loyalty, margins, and market share.
A leading global tyre manufacturer approached Actowiz Solutions to build an AI-powered web-scraping solution capable of collecting and analyzing competitor pricing data from multiple distributor websites across the United States and Europe. The goal was to automate price benchmarking, track stock fluctuations, and deliver actionable insights for pricing strategy and sales planning.
Client Overview

The client is a global player in tyre design, manufacturing, and distribution for passenger ...
... and commercial vehicles. Their sales network spans the US, EU, and Asia, supported by a wide network of distributors and retail partners.
While the company had strong in-house analytics capabilities, the team lacked access to real-time competitor pricing data. Distributor websites often update prices daily, making manual monitoring impossible. The company wanted a solution that could:
Collect competitor product prices automatically from four major distributor sites
Detect price differences between regions and brands
Identify promotional events, discounts, and stockouts
Deliver clean, structured datasets for internal analytics tools
The Business Challenge

Data Fragmentation Across Distributors
Each distributor website had a unique design, search structure, and product taxonomy. While some offered clear SKU-level details, others displayed unstructured data such as “Best Seller” or “Limited Offer” without standardized tags. The client’s team needed to scrape and normalize this data for a consistent pricing benchmark.
Dynamic Content and Anti-Bot Systems
Distributor websites used modern JavaScript frameworks with dynamic content loading. Many pages required client-side rendering or AJAX calls. Additionally, anti-bot firewalls (like Akamai and Cloudflare) made standard scraping tools ineffective.
Regional Price Variation
Prices for the same tyre model varied significantly between the US and EU due to logistics, import duties, and market positioning. Manual monitoring could not track these changes in real time.
Need for Automated Benchmarking
The brand needed not only raw data but also actionable intelligence—for example:
Which distributors offered consistent price undercuts
Which SKUs had the highest margin erosion
Which models frequently went out of stock
The solution had to extract, process, and visualize these trends efficiently.
The Actowiz Solutions Approach
Actowiz Solutions designed a multi-layered, AI-driven scraping framework customized for automotive and tyre data collection.
Discovery and Scoping
Our data engineering team conducted a discovery audit of the four distributor websites. Each was mapped for:
Page structures and HTML layouts
Product listing patterns
Dynamic rendering methods (React, Vue, or server-side)
Pricing and promotion fields
Stock and availability markers
This phase allowed us to design site-specific crawlers to capture every relevant data point.
AI-Driven Crawlers and Scheduling
We deployed AI-assisted crawlers capable of handling:
JavaScript rendering through headless browsers
Intelligent throttling to mimic human browsing
Adaptive scheduling based on traffic intensity and update frequency
Crawlers ran at specific time intervals (daily or twice per week, depending on region) to capture price changes in near real time.
Data Fields Extracted
Each data record captured included:
Brand: Tyre manufacturer name
Model Name: Product line (e.g., SportDrive, EcoGrip)
Size: Tyre dimensions (e.g., 225/45 R17)
Price (Local Currency): Current listed price
Discount (%): Applicable promotional discount
Availability: Product status – In stock, Limited stock, or Out of stock
Distributor Name: Source or retailer site name
Region: Market region such as US or EU
Timestamp: Date and time when data was extracted

Sample Data Extract (Illustrative)

Bridgestone – Turanza T005
Size: 225/45 R17
Price: $138.90
Discount: 10%
Availability: In Stock
Distributor: TireRack
Region: USA

Michelin – Pilot Sport 4
Size: 225/45 R17
Price: €142.00
Discount: 5%
Availability: In Stock
Distributor: Oponeo
Region: EU

Continental – EcoContact 6
Size: 205/55 R16
Price: $125.50
Discount: 0%
Availability: Low Stock
Distributor: Discount Tire
Region: USA

Pirelli – Cinturato P7
Size: 225/50 R18
Price: €161.30
Discount: 8%
Availability: In Stock
Distributor: MyTyres
Region: EU

From these records, Actowiz generated cross-region comparison dashboards, allowing analysts to visualize price differences at brand and SKU levels.
Data Cleaning and Normalization
Data normalization was crucial to ensure consistent analysis. Actowiz’s AI pipelines automatically handled:
Currency Conversion: All EU prices converted to USD using daily exchange rates.
Unit Standardization: Metric and imperial measurements aligned for uniform comparison.
Duplicate Detection: Removal of similar SKUs listed across multiple distributors.
Error Correction: Automated tagging of anomalies such as missing values or outdated listings.
This produced a clean, analytics-ready dataset delivered in CSV, Excel, or via API integration.
Analytics and Visualization
To make insights actionable, the final step involved creating a Tyre Pricing Benchmark Dashboard using Power BI and Tableau integration.
Key Metrics Displayed:
Average Price per Brand & Size Segment: Helps identify market positioning and premium vs. budget gaps.
Price Difference (US vs EU): Highlights regional variations for each model.
Discount Frequency Tracker: Shows which distributors offer regular promotions.
Stock Availability Heatmap: Displays supply bottlenecks or overstock risks.
Historical Price Trends: Weekly changes for top 20 SKUs, allowing forecasting of promotional cycles.
These insights helped marketing and sales teams identify pricing inefficiencies, hidden opportunities, and competitive risks before they affected profitability.
Technology Stack

Scraping Framework: Custom Python scrapers using Requests and Playwright
AI Components: NLP-based field extraction and anomaly detection
Storage Layer: AWS S3 and PostgreSQL for scalable data storage
Data Cleaning: Performed using Pandas and NumPy
Analytics & Visualization: Insights visualized through Power BI and Tableau
Automation: Managed via Apache Airflow scheduler and AWS Lambda
Delivery: Data shared through REST API and a secure client dashboard

Actowiz’s modular design allowed the system to scale up to more distributors or new regions without rewriting core logic.
Overcoming Technical Challenges
Handling Anti-Bot Mechanisms
Distributor websites often employed rate-limiting and CAPTCHA checks. Actowiz’s crawlers used:
Dynamic user-agent rotation
Proxy IP pools by region
Request-interval randomization
AI-based human behavior simulation
This ensured uninterrupted data flow while remaining fully compliant with website policies.
JavaScript-Heavy Pages
By integrating Playwright headless browsers, our system accurately rendered dynamic product pages and extracted data from client-side scripts.
Pricing Format Variations
Different sites displayed prices in formats like “$138.90”, “138.9 USD”, or “EUR 142,0”. Our AI parser recognized and standardized these across locales automatically.
Continuous Monitoring
Schedulers ensured crawlers ran consistently, while system alerts notified the team of any structural website changes, ensuring 99.6% uptime for data collection.
Results and Impact
The AI-based tyre data scraping solution delivered measurable results within the first month.

Manual effort for price tracking
Before: 18 hours/week
After Actowiz: 1 hour/week

Data freshness
Before: 7–10 days old
After Actowiz: > https://www.actowizsolutions.com/ai-powered-web-scraping-tyre-market-intelligence.php

Originally published at https://www.actowizsolutions.com