Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Overview
A leading automotive advisory firm that provides M&A and investment insights for the U.S. car dealership market struggled to leverage its raw data, coming from over 18,000 dealerships spanning decades. Each record had roughly 150 fields drawn from Polk, Helix, demographic and population datasets and other open sources and APIs. This had issues of inconsistent formats, missing common identifiers that prevented easy merging, and large gaps. These problems slowed extraction of actionable insights: full data refreshes took more than a week and blocked timely, strategic decisions such as dealership valuations.
To resolve the client's data challenges, Shorthills AI developed JumpIQ, an AI-powered platform that ingests and processes raw data from Polk, Helix, and other open APIs directly into Databricks. A robust data engineering pipeline was built for intelligent merging (using techniques like fuzzy matching and address normalization), cleaning, mapping, and formatting to create a unified “golden record” for each dealership. On this refined data foundation, advanced AI/ML models were deployed for predictive analytics, including revenue forecasting, sales efficiency, dealership valuation, and performance scoring—all accessible through a web-based dashboard offering detailed analytical reports and visual insights.
As a result, the client reduced data processing time from over a week to just 8 hours, gained a single clean and accurate database, and obtained significantly stronger predictive insights that enable faster, more confident strategic decisions.
Modernizing enhancements for a global automotive marketplace—implementing a real-time, demand-driven engine to cut processing time to ~15 minutes and lift revenue.
Industry
Automotive Marketplace
Region
North America
Technology
AWS
Real-Time M&A Intelligence for 18,000+ Dealerships
Executive Summary
A global automotive marketplace needed to modernize paid ad enhancements that boost listing rank and visibility. The legacy flow was manual and batch-driven: dealers hand-picked listings and a once-daily job took another 1.5 hours to make the selection live, often missing real demand. Shorthills implemented a real-time, demand-driven engine that ranks listings by user search interest (by Designated Market Area- DMA and vehicle attributes) and auto-applies the most relevant enhancement bundles. Integrated with the dealer portal, it executes instantly and removes guesswork. Result: The overall selection and processing time cut to ~15 minutes and a measurable increase in revenue from ad enhancement services.
Tech Stack
MySQL
Nodejs
Vue.js
Elasticsearch
Nestjs
App Config
AWS (Lambda, SQS, ECS, API Gateway, DynamoDB)
Redis
New Relic
PHP (Laravel)
Modernizing enhancements for a global automotive marketplace—real-time, demand-driven engine cuts processing to ~15 minutes and lifts revenue.
Industry
Automotive Marketplace
Region
North America
Technology
AWS
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Modernizing Leading U.S. Automotive M&A with Databricks—unifying data from 18,000+ dealerships into golden records to deliver explainable valuations, standardized forecasts, and 8-hour refreshes
Industry
Automotive
Region
North America
Technology
Databricks
Tech Stack
Databricks | Python (Django) | React | AWS S3 | Gemini
Executive Summary
A leading U.S. automotive advisory firm struggled to turn decades of raw data from 18,000+ dealerships—spread across Polk, Helix, demographic datasets, and multiple APIs—into actionable insights. The fragmented and inconsistent data made full refreshes take over a week, delaying critical decisions like dealership valuations. Shorthills AI developed JumpIQ, an AI-powered platform that ingests this data into Databricks, creating unified “golden records” through intelligent cleaning, mapping, and merging. Advanced AI/ML models then deliver predictive analytics via a web dashboard with detailed reports and visual insights. The result: data processing dropped from over a week to 8 hours, the client gained a single accurate database, and predictive insights now support faster, more confident decisions.
Challenges
Automotive marketplaces depend on timely, data-driven boosts to surface the right listings to active shoppers. Manual, once-daily batches and dealer guesswork miss real demand shifts—slowing execution, diluting visibility, and capping monetization. With live search signals and automated application, upgrades hit at the right moment, improving performance and revenue at scale.
Manual daily batch processing
Dealers selected listings by hand; the batch took >1.5 hours post-selection.
Ineffective
enhancements
Without real-time search data, upgrades often missed what shoppers wanted—losing revenue and market moments.
What Shorthills Did
We replaced the manual, once-a-day batch with a real-time, demand-driven engine. It ranks listings by live shopper searches (by DMA and vehicle attributes) and auto-applies the best enhancement bundle, or lets dealers trigger it from the portal. Changes go live in minutes—so upgrades match real demand instead of guesses derived from historical data.
Real-Time Architecture
We replaced the manual, once-daily batch with a demand-driven flow that lets dealers select from enhancement bundles (e.g., premium select, premium, featured) and apply them automatically.
Listing Ranking & Selection
We built an auto-selection system that analyzes user search data to rank listings by geography (DMA) and vehicle type—identifying the best enhancement opportunities.
Automated Enhancements via Dealer Portal
We seamlessly integrated the dealer-portal so that it automates application of purchased bundles, ensuring visibility with minimal manual effort.
Built on AWS (Lambda, SQS, ECS, API Gateway, DynamoDB) with PHP (Laravel), Vue.js, MySQL, Redis, ElasticSearch, App Config, New Relic, Node.js, NestJS—optimized for speed and reliability.
Cloud Stack & Operations
Challenges
Manual daily batch processing
Dealers selected listings by hand; the batch took >1.5 hours post-selection.
Ineffective enhancements
Without real-time search data, upgrades often missed what shoppers wanted—losing revenue and market moments.
Automotive marketplaces depend on timely, data-driven boosts to surface the right listings to active shoppers. Manual, once-daily batches and dealer guesswork miss real demand shifts—slowing execution, diluting visibility, and capping monetization. With live search signals and automated application, upgrades hit at the right moment, improving performance and revenue at scale.
Our Solutions
Data Foundation: Lakehouse & Entity Resolution
We stood up a Databricks-powered lakehouse with medallion layers (bronze → silver → gold) and survivorship rules to reconcile conflicts. Fuzzy matching plus brand/state heuristics created a durable golden dealer record across renames, mergers, and closures—an analytics-ready backbone with end-to-end lineage.
Signals & Feature Engineering
On unified records, we built a reusable catalog of 150+ signals per dealership spanning performance, market, and macro indicators. Features are standardized across brands/states and versioned over time, so valuations, forecasts, and benchmarks stay fair and reproducible.
Valuation & Forecasting Engines
A model suite blends store performance with market signals to produce explainable valuations and forward-looking forecasts. Scenario/sensitivity views test brand, geography, and macro assumptions—accelerating buy/no-buy calls with consistent methodology.
Delivery Experience: Analyst App for M&A Workflows
A secure analytics app streamlines real M&A tasks: search/filter/compare, geospatial views, and exportable diligence summaries. Built on governed tables and shared definitions, it keeps every stakeholder aligned—from board decks to deep dives.
What Shorthills AI Did
We replaced the manual, once-a-day batch with a real-time, demand-driven engine. It ranks listings by live shopper searches (by DMA and vehicle attributes) and auto-applies the best enhancement bundle, or lets dealers trigger it from the portal. Changes go live in minutes—so upgrades match real demand instead of guesses derived from historical data.
Real-Time Architecture
We replaced the manual, once-daily batch with a demand-driven flow that lets dealers select from enhancement bundles (e.g., premium select, premium, featured) and apply them automatically.
Listing Ranking & Selection
We built an auto-selection system that analyzes user search data to rank listings by geography (DMA) and vehicle type—identifying the best enhancement opportunities.
Automated Enhancements via Dealer Portal
We seamlessly integrated the dealer-portal so that it automates application of purchased bundles, ensuring visibility with minimal manual effort.
Cloud Stack & Operations
Built on AWS (Lambda, SQS, ECS, API Gateway, DynamoDB) with PHP (Laravel), Vue.js, MySQL,
Redis, ElasticSearch, App Config, New Relic,
Node.js, NestJS—optimized for speed and reliability.
Executive Summary
A global automotive marketplace needed to modernize paid ad enhancements that boost listing rank and visibility. The legacy flow was manual and batch-driven: dealers hand-picked listings and a once-daily job took another 1.5 hours to make the selection live, often missing real demand. Shorthills implemented a real-time, demand-driven engine that ranks listings by user search interest (by Designated Market Area- DMA and vehicle attributes) and auto-applies the most relevant enhancement bundles. Integrated with the dealer portal, it executes instantly and removes guesswork. Result: The overall selection and processing time cut to ~15 minutes and a measurable increase in revenue from ad enhancement services.
Tech Stack
AWS (Lambda, SQS, ECS, API Gateway, DynamoDB)
PHP (Laravel)
Vue.js
MySQL
Redis
ElasticSearch
App Config
New Relic
Nodejs
Nestjs
Outcomes
A global automotive marketplace was missing revenue because enhancement picks were manual and batches took hours to go live. With Shorthills AI’s real-time engine, selection and processing drop to ~15 minutes, so high-intent listings get boosted while demand is hot. Visibility improves where shoppers are actually searching, driving more effective upgrades and a measurable lift in enhancement revenue. Dealers spend less time guessing and more time seeing results, thanks to instant application and clear tracking. In short: faster execution, demand-aligned boosts, and higher monetization—at scale.
Processing runtime → ~15 minutes
Down from hours, enabling timely, demand-aligned boosts.
Revenue uplift from enhancements
Improved alignment to real shopper demand increased revenue.
Outcomes
A global automotive marketplace was missing revenue because enhancement picks were manual and batches took hours to go live. With Shorthills AI’s real-time engine, selection and processing drop to ~15 minutes, so high-intent listings get boosted while demand is hot. Visibility improves where shoppers are actually searching, driving more effective upgrades and a measurable lift in enhancement revenue. Dealers spend less time guessing and more time seeing results, thanks to instant application and clear tracking. In short: faster execution, demand-aligned boosts, and higher monetization—at scale.
Processing runtime → ~15 minutes
Down from hours, enabling timely, demand-aligned boosts.
Revenue uplift from enhancements
Improved alignment to real shopper demand increased revenue.
Frequently Asked Questions
Also Read
