Executive Summary
A subscription-rental apparel platform priced post-rental purchases using a simple depreciation curve. Popular items were underpriced, slow-moving items overpriced, sell-through lagged, and margins hovered near 15%. We built a data-driven dynamic pricing engine that predicts each garment’s Expected Remaining Subscription Revenue (ERSR) / Expected Value of Allocation (EVA) and applies guardrails tied to willingness-to-pay and MSRP. With real-time API pricing and integrated data pipelines (catalog, web analytics, transactions, feedback), the client captured true market value, accelerated sell-through, and lifted profitability.
Tech Stack
Python
AWS
Google Analytics
Google BigQuery
Elastic search
Executive Summary
A subscription-rental apparel platform priced post-rental purchases using a simple depreciation curve. Popular items were underpriced, slow-moving items overpriced, sell-through lagged, and margins hovered near 15%. We built a data-driven dynamic pricing engine that predicts each garment’s Expected Remaining Subscription Revenue (ERSR) / Expected Value of Allocation (EVA) and applies guardrails tied to willingness-to-pay and MSRP. With real-time API pricing and integrated data pipelines (catalog, web analytics, transactions, feedback), the client captured true market value, accelerated sell-through, and lifted profitability.
Tech Stack
Google Analytics
Google BigQuery
Elastic search
AWS
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
Apparel rental platforms often price post-rental sales with blunt depreciation curves, undervaluing high-demand items and overpricing slow movers. Fragmented signals across catalog, usage, and feedback stall sell-through and cap margins. With demand-aware, guardrailed pricing tied to willingness-to-pay, teams capture true value, move inventory faster, and improve profitability at scale.
Static, depreciation-only pricing
Undervalued high-demand items and overpriced low-demand ones.
Low sell-through & liquidation losses
From aging inventory and guesswork pricing.
Thin profit margins (~15%)
With no responsiveness to demand, usage, or feedback.
What Shorthills AI Did
We replaced one-size, depreciation-only pricing with a dynamic engine. It pulls catalog, browsing, purchase/return, and feedback data into one view, predicts each garment’s remaining value (ERSR/EVA), and sets a fair price with simple guardrails (willingness-to-pay floors, ≤90% MSRP caps). Prices are served in real time via API and were A/B-tested before rollout—so high-demand items aren’t underpriced, slow movers aren’t overpriced, and sell-through accelerates.
We built ETL on AWS EMR to consolidate product catalogs, GA4/BigQuery interactions, payments (Stripe/Braintree), returns, and customer feedback into a clean pricing dataset.
Unified the data foundation for pricing
We trained a Beta Regression model to estimate EVA (0–1), combined it with mean slot revenue and remaining life to compute ERSR per garment.
Modeled ERSR/EVA with Beta Regression
We enforced price floors (customer willingness-to-pay / minimum threshold) and ceilings (e.g., ≤90% of MSRP or ERSR-based caps) to keep prices competitive and profitable.
Applied intelligent guardrails
We exposed pricing via API for on-site calls, served model weights with Elasticsearch for fast lookups, and ran A/B tests on a pilot segment before full rollout.
Deployed real-time pricing API & validated
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 post-rental pricing for a subscription apparel platform—dynamic ERSR/EVA engine lifts margins to ~20–25% and speeds sell-through via real-time API.
Industry
E-commerce
Region
North America
Technology
AWS
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
A subscription-rental apparel platform was leaving money on the table with static pricing: popular items sold too cheap, slow items lingered, and margins hovered near ~15%. With Shorthills AI’s dynamic engine, prices now reflect true demand and remaining value. Profit margin lifts to ~20–25%, and “Try-Then-Buy” adds ~$1.5M annually. Faster sell-through and lower liquidation losses improve inventory velocity and cash recovery. Because guardrails tie prices to willingness-to-pay and MSRP, offers stay competitive and defensible. Real-time APIs keep storefront prices current without manual tuning, and A/B validation builds confidence for ongoing optimization. Net result: smarter pricing, higher margins, and healthier inventory turn—at scale.
Margin Uplift to 20–25%
ncrease profit margins from a ~15% baseline through optimized pricing and mix improvements.
+$1.5M Try-Then-Buy Gain
Boost annual revenue by ~$1.5M driven by smarter pricing for Try-Then-Buy programs.
Higher Sell-Through, Lower Losses
Improve inventory velocity and reduce liquidation losses for stronger overall profitability.
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