Real-Time M&A Intelligence for 18,000+ Dealerships
Databricks
Python (Django)
React
AWS S3
Gemini
Tech Stack
Client Profile
Industry
Automotive
Region
North America
Technology
Databricks
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 marketing ROI for an e-commerce brand—Python/Databricks MMM (PyMC Marketing) quantifies channel lift, models adstock/saturation, and recommends optimal budgets.
Industry
E-commerce
Region
North America
Technology
Databricks
Executive Summary
An e-commerce business wanted proof that its marketing spend was working—and a way to reallocate budgets for higher ROI. We built a Media Mix Modeling (MMM) solution on Python/Databricks using PyMC Marketing to quantify channel contribution, measure adstock decay and saturation, and simulate “what-if” scenarios. The system delivers ROI by channel, optimal mix recommendations, and forecasts—turning fragmented analytics into a unified, transparent framework for continuous optimization.
Tech Stack
Python
PyMC Marketing
Databricks
Executive Summary
An e-commerce business wanted proof that its marketing spend was working—and a way to reallocate budgets for higher ROI. We built a Media Mix Modeling (MMM) solution on Python/Databricks using PyMC Marketing to quantify channel contribution, measure adstock decay and saturation, and simulate “what-if” scenarios. The system delivers ROI by channel, optimal mix recommendations, and forecasts—turning fragmented analytics into a unified, transparent framework for continuous optimization.
Tech Stack
PyMC Marketing
Python
Databricks
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
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
E-commerce marketers juggle fragmented channels and noisy attribution, making it hard to prove impact or spot overspend. Without a unified MMM that captures carry-over and diminishing returns, budgets drift, ROI lags, and growth stalls. With transparent, simulation-ready modeling, teams reallocate spend confidently for higher returns.
Validate spend effectiveness
Across channels and surface inefficiencies.
Optimize ROI
Data-driven budget reallocation.
Understand channel mix impact
The role of external factors (seasonality/holidays).
Lack of a unified framework
Separate true marketing impact from noise.
What Shorthills AI Did
We pulled your marketing and sales history into one clean dataset, then used Media Mix Modeling to measure what each channel really contributes. The model captures carry-over (adstock) and saturation effects, so it knows when extra spend stops paying back. A simple simulator shows “what if we shift +30% to Search and −40% from Display?” and returns ROI, revenue, and the recommended mix. Outputs are clear tables/graphs for analysts and plain summaries for marketers—so budgets move with confidence.
We aggregated historical spends, conversions/revenue, and control variables (seasonality, holidays, market events) into a clean modeling dataset on Databricks.
Unified the data foundation
We evaluated options (e.g., Robyn/Meridian) and implemented PyMC Marketing for flexibility and transparency, tailoring priors and features to the client’s context.
Selected and customized PyMC Marketing
We quantified carry-over (half-life) and diminishing returns, enabling precise ROI by channel and detection of overspend thresholds.
Modeled adstock & saturation for each channel
We built a simulation tool to recommend optimal budget mixes and forecast outcomes under “what-if” changes (e.g., +30% on A, −40% on B). Outputs include graphs/tables for analysts and business summaries for marketers.
Delivered optimization & scenario simulation
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.
Outcomes
Unify all your disparate sources into a governed data lakehouse, resolve duplicates to a single “golden record,” and standardize key signals so analysts can trust the data. That’s how we built JumpIQ for a leading U.S. automotive M&A firm: we consolidated decades of data across 18,000+ dealerships, cut refresh time from 7+ days to ~8 hours, and engineered 150+ metrics per store. On top, we added explainable valuation and forecasting models so you can run what-ifs on brand, geography, and macro factors. The result: faster, defensible diligence with scenario planning directly from your historical data.
Drastic Speed Improvement
Full data ingestion and refresh cycles reduced from over a week to 8 hours.
Enhanced Predictive Accuracy
Unified, clean database for 18,000+ dealerships, each with ~150 data points.
Comprehensive & Accurate Data
More reliable forecasts for Key Performance Indicators, sales, and valuations.
Outcomes
An e-commerce brand needed proof its marketing spend was working—and a way to reallocate budgets for higher ROI. With Shorthills AI’s MMM, the team now sees true ROI by channel, including where spend is saturated and where it’s underfunded. Planners run quick scenarios to test new mixes and forecast impact, then shift budgets toward the highest-return blend. Decisions are no longer based on last-click reports or hunches; a transparent model explains the lift, adstock half-lives, and diminishing returns. As budgets move, profitability improves and reporting is unified across teams. In short: clear attribution, optimal mix guidance, and a repeatable framework for continuous optimization.
Data-driven budget allocation
With clear ROI by channel and optimal mix guidance.
Improved marketing ROI and profitability
by shifting spend away from saturated channels.
Transparent, future-proof framework
That unifies analytics and supports ongoing planning and forecasting.
Frequently Asked Questions
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