Normalizing the Waves of Data
Creators: name: sprague, sean
Publisher: name: Zenodo
Year: 2024

description: Description: Data Modeling and Normalizing Industry Flows in Retail Automotive Analysis

Summary:This document explores the transformative potential of integrating granular data models, machine learning, and industry knowledge into the Retail Automotive sector. By utilizing innovative tools like web scraping, clustering algorithms, and AI-driven forecasting, the paper outlines a paradigm shift from macro-level market analysis to dealership-focused insights. This approach seeks to normalize data flows across the industry, creating actionable intelligence while leveraging publicly available data and advanced computational models.



Core Themes:





Granular Proxy Data Models:



By collecting publicly accessible data from online listings and dealership APIs, the paper identifies key variables (e.g., price changes, vehicle saves, time to market) that act as proxies for dealership performance, consumer interest, and market behavior.

These proxies form the foundation of highly detailed, dealership-specific analytics.





Industry Knowledge Integration:



Building on industry-standard practices like “Day Supply” and “Turn Rate,” the document highlights limitations in current statistical models and introduces AI-driven approaches for improved forecasting.

The methodology emphasizes understanding the nuances of individual dealerships rather than broad regional trends.





Normalizing the Flow of Data:



Through web scraping and clustering, the paper proposes the normalization of dealership-specific data into structured and analyzable formats. This enables cross-dealership comparisons and provides insights into performance benchmarks.

Data normalization also facilitates deeper integration of disparate data sources, creating a cohesive and scalable analytical framework.





Machine Learning for Dynamic Forecasting:



AI models are introduced as replacements for traditional statistical methods, offering predictive accuracy for key questions like:



"What will this vehicle sell for?"

"How long will it take to sell this vehicle?"



These forecasts are shown to provide a competitive edge in inventory management and market positioning.






Applications:





Dealership Performance Insights:



The clustering of dealerships based on granular metrics allows underperforming dealerships to identify actionable strategies to improve.

The framework enables dealerships to better understand their competitors and adjust their operations accordingly.





Investment Opportunities:



The proposed methodology creates opportunities for investors and dealership owners to evaluate market potential and identify undervalued assets.

By tracking performance metrics, early adopters can gain foresight into market shifts, such as the collapse of Carvana, enabling informed investment decisions.





Creating a Competitive Advantage:



The document emphasizes the inevitability of AI and granular analytics becoming a requirement to compete in the industry, much like the adoption of dealership websites or VAuto's early market analysis software.






Conclusion:

By integrating granular proxy data models, normalizing data flows, and leveraging machine learning, this paper presents a roadmap for redefining competitive intelligence in the Retail Automotive industry. It highlights the potential for early adopters to capitalize on these innovations and outlines a clear path for transforming raw data into actionable insights.



This description aligns with the document's themes of normalizing data flows, industry knowledge integration, and granular modeling. If you'd like further refinements or additional emphasis on specific applications, let me know!