Executive Summary
A leading indigenous automotive and farm equipment manufacturer received market sales data on a three-month lag — February numbers arrived in May — forcing every strategic call to rely on outdated analytics. With only two years of history, sales skewed across states, HP categories, and competitors, standard time-series methods weren't viable. Shorthills AI engineered a native, end-to-end predictive forecasting workflow inside the client's existing Qlik Cloud ecosystem: a load-script data pipeline feeding a regression-model suite auto-deployed via Qlik AutoML, wrapped in a CXO-facing dashboard with a 68-market "Action Matrix" and an interactive what-if simulator. The result is 2.2% Mean Absolute Error on market share predictions and a proven shift from reactive reporting to proactive market penetration decisions.
Tech Stack
Qlik Cloud
Qlik Load Scripts
Qlik AutoML
XGBoost
LightGBM
CatBoost
Python
Executive Summary
A leading indigenous automotive and farm equipment manufacturer received market sales data on a three-month lag — February numbers arrived in May — forcing every strategic call to rely on outdated analytics. With only two years of history, sales skewed across states, HP categories, and competitors, standard time-series methods weren't viable. Shorthills AI engineered a native, end-to-end predictive forecasting workflow inside the client's existing Qlik Cloud ecosystem: a load-script data pipeline feeding a regression-model suite auto-deployed via Qlik AutoML, wrapped in a CXO-facing dashboard with a 68-market "Action Matrix" and an interactive what-if simulator. The result is 2.2% Mean Absolute Error on market share predictions and a proven shift from reactive reporting to proactive market penetration decisions.
Tech Stack
Qlik Cloud
Qlik Load Scripts
Qlik AutoML
XGBoost
LightGBM
CatBoost
Python
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
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
Delayed data, sparse history, and a dataset that broke every standard forecasting playbook.
No Predictive Capability
The business had no automated way to forecast future demand, Total Industry Volume (TIV), or competitor market share across Horsepower (HP) segments and regions — leaving whitespace opportunities invisible.
The Data Lag Dilemma
Market sales data was inherently delayed by three months — February data landed in May — forcing leadership to plan and allocate capital against rearview-mirror analytics that were already stale on arrival.
Data Constraints Ruled Out Standard Forecasting
Only two years of data, unevenly split across states, HP categories, and competitors, made the dataset too imbalanced and too short for traditional time-series models. A different architectural approach was needed.
What Shorthills AI Did
We built a native predictive forecasting workflow inside the client's existing Qlik Cloud — a load-script pipeline feeding regression models auto-deployed via Qlik AutoML, surfaced through a CXO dashboard whose "Action Matrix" classifies 68 markets into Attack, Defend, Fix, or Deprioritize each cycle. An integrated what-if simulator lets executives flex variables like Industry Volume and see the impact on future market share live — all owned and maintained by the client's own Qlik teams.
To forecast demand four months into the future and neutralize the three-month data lag, we engineered a data pipeline using Qlik load scripts to prep and standardize inputs that seamlessly feed a suite of regression models — built, benchmarked, and auto-deployed via Qlik AutoML with the best-fit algorithm (XGBoost, LightGBM, CatBoost, or LightGBM Regression) selected per forecast target.
Native ML Pipeline & Future-Proof Forecasting
We translated complex ML outputs into a CXO-focused visual dashboard featuring an "Action Matrix" that automatically categorizes 68 distinct markets into four actionable KPIs — Attack, Defend, Fix, and Deprioritize — giving leadership a clear read on exactly how to prioritize regions and allocate capital.
Actionable Strategic Dashboard
We integrated a dynamic scenario-planning tool that lets executives adjust sliders for variables like Industry Volume and instantly visualize the downstream impact on future market share — moving strategy conversations from static reports to live, testable hypotheses.
Interactive "What-If" Simulator
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.
Forecasting market share 4 months ahead at 2.2% MAE for a leading automotive and farm equipment manufacturer.
Industry
Automotive & Farm Equipment
Region
APAC
Technology
Qlik Cloud + AutoML
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.
Frequently Asked Questions
Outcomes
Leadership no longer waits three months to react to market movement — they now plan four months ahead. The forecasting engine delivers market share predictions at 2.2% Mean Absolute Error, precise enough to underpin real capital allocation, while the Action Matrix classifies all 68 markets as Attack, Defend, Fix, or Deprioritize each cycle so commercial teams know exactly where to press and where to pull back. Because everything runs inside the client's own Qlik Cloud, adoption was immediate and the workflow is fully owned by internal teams — moving market strategy from reactive, rearview reporting to proactive, data-driven decisions at scale.
2.2% MAE on Market Share Predictions
High-precision forecasts that leadership can act on with confidence.
4 months Forward Visibility
Forecasting four months ahead neutralizes the three-month reporting lag entirely.
68 Markets on the Action Matrix
Every market automatically classified as Attack, Defend, Fix, or Deprioritize.
Frequently asked questions
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