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Navratri Mega Sale Price Tracking

About the Client

Location: Lafayette, United States

Industry: Automotive Data & Analytics

Objective: Automate the collection of carrying capacity specifications—including GVWR, payload, curb weight, length, and wheelbase—for over 4,500 vehicle trims across model years 2016, 2018, and 2020.

The client provided an Excel workbook listing all vehicles and trims but with many missing data points. Most values could be found on Edmunds, Cars.com, CarMax, and manufacturer sites—but doing this manually would take weeks. They needed an automated web scraping solution capable of extracting and normalizing this data efficiently and accurately.

Project Goals

Actowiz Solutions was tasked to:

  • Scrape missing vehicle data for all trims listed in the Excel workbook (2016, 2018, 2020).
  • Collect the following parameters:
    • Gross Vehicle Weight Rating (GVWR)
    • Payload
    • Curb Weight
    • Vehicle Length
    • Wheelbase
    • Data Source (URL)
  • Merge results with the client's existing dataset and ensure clean, structured outputs in .csv format.
  • Complete the project within 20–30 hours with validation, deduplication, and compliance controls.

Key Challenges

Variation in Terminology

Websites use different labels like Gross Weight, GVWR, or Gross Vehicle Weight Rating — often meaning the same value but presented differently.

Trim-Level Complexity

Over 4,500 entries included multiple trims per model. Many trims share identical specifications, but trucks and vans vary significantly by configuration.

Multi-Source Requirement

While Edmunds covers most data, smaller cars or discontinued trims required lookups from Cars.com, CarMax, or OEM (manufacturer) websites.

Consistency & Validation

The scraper needed to ensure that:

GVWR = Curb Weight + Payload

where possible, and flag mismatches or missing pairs for review.

Actowiz Solutions' Approach

1. Data Discovery & Source Mapping

We began by mapping all major automotive data sources:

Source Coverage Format Scraping Tool
Edmunds.com 2010–2024 models HTML / JSON API BeautifulSoup + Scrapy
Cars.com Dealer listings + specs Dynamic (JS) Selenium
CarMax.com Used inventory + trim specs JS-heavy Puppeteer (Node.js)
Manufacturer Sites Missing trims Static pages Requests + XPath
2. Automation Framework

We deployed a Python-based modular web scraping framework with the following stack:

  • Scrapy + Selenium hybrid for structured crawling.
  • BeautifulSoup for static HTML parsing.
  • Pandas for data normalization and deduplication.
  • Regex rules to identify variants of weight/size terms.
  • Headless browser rotation via ChromeDriver for dynamic sites.
3. Extraction Logic

Each record in the Excel sheet contained:

  • Make
  • Model
  • Trim
  • Year

The scraper performed a targeted search (example: "2018 Ford F-150 XLT site:edmunds.com") and parsed tables containing:

Gross Vehicle Weight Rating: 6,850 lbs
Curb Weight: 4,780 lbs
Payload: 2,070 lbs
Wheelbase: 145 inches
Vehicle Length: 231 inches

When any value was missing, fallback logic fetched data from secondary sources.

4. Data Normalization Rules

To ensure accuracy:

  • Numeric standardization: All weights converted to pounds (lbs); lengths and wheelbases converted to inches.
  • Text parsing: Extracted numeric values using regex patterns ([0-9,]+).
  • Deduplication: Identical trims' data reused where specifications matched 100%.
  • Derived values: If two of three (GVWR, Curb Weight, Payload) were found, the missing one was calculated automatically.
5. Data Validation & Cross-Verification

Actowiz Solutions implemented a multi-step validation:

  • Cross-source check: Compare Edmunds vs Cars.com data within ±1% tolerance.
  • Formula validation: GVWR ≈ Curb + PayloadGVWR \approx Curb + PayloadGVWR≈Curb+Payload
  • Manual QA sample: Random 100-record check for unit consistency.
  • Completeness audit: Ensure every row had at least two weight values and dimensions.
6. Output & Delivery
Navratri Mega Sale Price Tracking

Final data was exported in .csv format with the following schema:

Year Make Model Trim GVWR (lbs) Payload (lbs) Curb Weight (lbs) Length (in) Wheelbase (in) Source URL
2018 Ford F-150 XLT 4x4 6,850 2,070 4,780 231 145 www.edmunds.com
2020 Toyota Tacoma TRD Off-Road 5,600 1,175 4,425 212 127 www.cars.com
2016 Chevrolet Silverado 1500 LT 7,100 2,030 5,070 230 143.5 www.carmax.com
2018 Ram 2500 Tradesman 4x2 9,000 3,060 5,940 237 149 www.edmunds.com
2020 Honda Civic EX Sedan 3,900 930 2,970 182 107 www.edmunds.com

Additionally:

  • Unique vehicles processed: 4,593
  • Data completeness: 97.2%
  • Duplicates removed: 312
  • Missing-only entries flagged: 128 for manual follow-up
Chart: GVWR Distribution by Vehicle Type (Sample Visualization)
Navratri Mega Sale Price Tracking
Vehicle Type Avg GVWR (lbs)
Sedan 4,000
SUV 5,500
Pickup Truck 7,800
Van 8,600
Compact 3,200

(Insert bar chart visualizing these averages — color-coded by vehicle category.)

Observation:

Pickups and Vans dominate the upper GVWR spectrum (7,500–9,000 lbs), while sedans and compacts cluster between 3,000–4,500 lbs.

Infographic

Navratri Mega Sale Price Tracking

Results

Metric Outcome
Vehicles Processed 4,593
Data Points Extracted ~25,000
Accuracy 98.4% verified
Project Duration 22 hours
Automation Efficiency 10× faster than manual
Delivery Format CSV + Quality Report

Impact on the Client's Operations

Time Saved:

Reduced a multi-week manual data entry task (80–100 hrs) to under 24 hours.

Accuracy Improved:

Validations ensured <2% error margin, meeting engineering data standards.

Reusable Framework:

The scraper can now be reused annually for updated model years (2022–2024).

Insight Generation:

The client's analysts built pivot dashboards showing:

  • Payload ranges by brand and trim.
  • Correlation between vehicle length and GVWR.
  • Segment-level distribution of curb weight.
Insights Generated (Illustrative Analytics)
Metric Observation
Payload vs GVWR Ratio Trucks had 27–32% payload-to-GVWR ratio, while sedans averaged 20%.
Wheelbase Variations Vans showed largest range (110–150 in.), consistent with trim extensions.
Brand Consistency Toyota and Honda exhibited <2% year-over-year deviation in curb weight.
Data Completeness 97% of Edmunds data validated directly without external lookup.

Technical Stack

Layer Tool / Language
Core Scraper Python (Scrapy, Selenium, BeautifulSoup)
JavaScript Handling Puppeteer (Node.js)
Data Processing Pandas, NumPy
Validation Regex + Statistical Checks
Storage CSV / PostgreSQL
Visualization Power BI / Matplotlib
Cloud Hosting AWS EC2 with rotating proxies

Compliance & Ethics

Public Data Only: No authentication or private endpoints accessed.

Respect robots.txt: Crawl-delay and polite requests.

Attribution: Each row includes source URL.

Data Use: Strictly for research and engineering analysis.

Actowiz Solutions maintains ethical scraping standards, ensuring clients stay compliant with local data regulations (US and EU).

Business Outcome

Delivered a complete, high-integrity vehicle dataset covering three model years.

Enabled faster product benchmarking for aftermarket suppliers.

Laid the groundwork for future AI-based vehicle specification prediction models.

Client Testimonial

"The team at Actowiz Solutions turned a complex manual task into a seamless automated process. Their attention to accuracy and validation saved us countless hours."

— Automotive Data Lead, Lafayette, USA

Why Choose Actowiz Solutions

Deep expertise in automotive web scraping and technical specifications mining.

Proven track record in multi-source data aggregation (Edmunds, Cars.com, OEM portals).

Highly scalable and compliant framework for engineering-grade datasets.

End-to-end delivery: from design → scraping → cleaning → analytics.

Future Enhancements

Expand to 2022–2024 models with live updates.

Integrate vehicle image scraping for dataset enrichment.

Add API feed for real-time spec queries.

Include towing capacity and fuel economy metrics for broader trend analysis.

Conclusion

This case study highlights how Actowiz Solutions engineered an automated vehicle carrying capacity scraping system to complete missing specification data for thousands of trims across three years.

By leveraging a hybrid Scrapy + Selenium framework, applying intelligent parsing, and automating validations, Actowiz Solutions delivered a high-quality dataset within days—something that would otherwise take weeks manually.

The project demonstrates our expertise in automotive data scraping, data normalization, and technical compliance, helping clients unlock structured insights at scale.

From Raw Data to Real-Time Decisions

All in One Pipeline

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Find Insights Use AI to connect data points and uncover market changes. Meanwhile.

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Industry:

Coffee / Beverage / D2C

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Operations Manager, Beanly Coffee

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Real-time RERA insights for 20+ states

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“Actowiz Solutions provided exceptional RERA Website Data Scraping Solution Service across PAN India, ensuring we received accurate and up-to-date real estate data for our analysis.”

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Organic Grocery / FMCG

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Improved

competitive benchmarking

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Boosted marketing responsiveness

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Growth Analyst, TheBakersDozen.in

✓ Improved rank visibility of top products

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Real results from real businesses using Actowiz Solutions

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'Great value for the money. The expertise you get vs. what you pay makes this a no brainer"
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2 min
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See Actowiz in Action – Real-Time Scraping Dashboard + Success Insights

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Price Drop + 12 min
in 6 hrs across Lel.6

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Price
Drop −12 thr

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visibility & planning

Monitor Prices, Availability & Trends -Live Across Regions

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✔ Scraped Data: Price Insights Top-selling SKUs

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With hourly price monitoring, we aligned promotions with competitors, drove 17%

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