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Our Success Stories – Case Studies from Our Automotive Clients

An ML-powered Schema Matching Solution for Improving Automated Data Extraction and Mapping Efficiencies

This UK Automotive Intelligence Company providing insights and analytics to 100+ markets needed to map inconsistent data formats from varied sources to a standardised format. 

Costs were high due to the labour-intensive and time-consuming nature of data extraction, cleaning, annotation, and quality checks of parsed entries by SMEs.  

AI/NLP was implemented to process varied domain/industry-specific terminology at scale, resulting in a 60% reduction in SME effort, 85% accuracy and 90% increased efficiency.

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Empowering Global Automotive Intelligence with an Enhanced ETL Solution

This Intelligence company handles high volume, variety, and velocity of data to deliver meaningful insights to the automotive industry in over 100 countries. They needed to automate processes for large-scale global data accuracy, completeness, and consistency.  

Merit improved rule implementations of make and market managing data overlaps and conflicts from data extracted from 50 websites and 6000 PDFs and brochures, resulting in 100% data accuracy and a 70% reduction in research time.

Use Cases

Automated Payments Data and Lease Feature Collection Solution

This globally compliant automotive intelligence company needed to scrape data from frequently updated, complex website structures.  

Merit developed custom high-volume scrapers tailored for diverse website structures, automating the collection of monthly payments data with efficient delta differencing. A comprehensive dashboard provided real-time insights and enabled issue management.  

Operational costs were reduced by 30%, decision-making efficiency increased by 25% and revenue increased by 15% in the first year.

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Automotive Data Scraping Dashboard: Enabling Quick Insights for Improved Decision-making

This global automotive intelligence company scraped data on an enormous scale. They experienced a lack of visibility on scraper status, issue management and source monitoring, with a lengthy on boarding process for each new source.

Merit developed a highly visual dashboard that provided quicker insights and reduced source onboarding time from weeks to days. Issue logging and fix time was reduced from 3 days to 12 days.

Automotive Data
Revolutionising New Vehicle Data Management for an Automotive Intelligence Giant

This creator of award-winning automotive intelligence products that connects the global automotive industry, faces the challenges of collecting data from diverse sources, in varied formats, on a massive scale that experience continuous updates, perpetual data discrepancies and constant industry expansion.  

Our team of automotive data analysts work cohesively with custom built tech to handle 400k data points monthly, processing up to 1 million changes during model year transitions at an accuracy rate of 99%.

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A Hybrid Solution for Automotive Data Processing at Scale

This award-winning creator of automotive products needed millions of price points and specification details to be tracked for a large range of vehicles. 

The technical solutions implemented provided insights to help manufacturers and buyers make smarter automotive decisions related to a single vehicle or an entire fleet. 

To read the full case study and the solution to this challenge.

Automotive Data Aggregation Using Cutting Edge Tech Tools

An award-winning automotive client whose product allows the valuation of vehicles anywhere in the world and tracks millions of price points and specification details across a large range of vehicles.

Required help navigating the increasingly complex world of automotive data.

To read the full case study and the solution to this challenge.

A Digital Engineering Solution for High Volume Automotive Data Extraction

This automotive intelligence company tracks car specification data from all European OEMs. Obtaining accurate data from the manual processing of over 200+ sources across 20 different categories with regional differences in metrics and terminology was a drain on time and manpower.  

Our solution reduced manual processing time by 60% and QA time by 40%. Automated population of data points increased by 40%, data accuracy increased by 35% and productivity on models increased by 30%.