Growing-Moss-Data/automotive-service-intelligence-sample

🚗 Automotive Service Intelligence Sample Dataset

Connected • Longitudinal • Feature-Engineered • Commercially Available

This repository contains a fully anonymized sample of the Growing-Moss Data Automotive Service Intelligence Dataset, a production-derived dataset built for analytics, forecasting, AI/ML, benchmarking, and commercial product development.

Unlike transactional datasets that provide isolated records, the Growing-Moss dataset delivers connected intelligence through validated relationships across customers, vehicles, invoices, service history, and retail locations.

The sample preserves the structure, relationships, and analytical value of the full commercial dataset while protecting all sensitive information.

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🌿 About Growing-Moss Data

Growing-Moss Data develops production-derived, commercial-grade datasets for analytics, forecasting, AI/ML, benchmarking, and commercial product development.

🌐 https://www.growing-moss.com

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✨ Why This Dataset Is Different

Nearly 9 years of connected automotive service history spanning more than 7.9 million customers, 7.9 million vehicles, 25.4 million invoices, and 121.2 million service records.

Most automotive datasets provide transactional records.

Growing-Moss Data provides connected, longitudinal intelligence enriched with analytical features that would otherwise require significant engineering effort to create.

✅ Key Differentiators

🔗 Connected Data Model

Validated relationships across customers, vehicles, invoices, service history, and retail locations.

🧠 Feature-Engineered Intelligence

Customer, vehicle, revenue, and operational metrics designed for analytics and machine learning.

🏭 Production-Derived Data

Derived from real-world automotive service operations rather than surveys or synthetic data.

✅ Commercially Ready

Fully anonymized, documented, analytics-ready data available for commercial licensing.

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🤝 Ready to Evaluate?

Request:

• Sample Data
• Pricing
• Schema Documentation
• Custom Extracts
• Historical Archives
• Enterprise Licensing

📧 sales@growing-moss.com

🌐 https://www.growing-moss.com

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📊 Full Dataset Scale

Entity	Records
Retail Locations	707
Customers	7,910,004
Vehicles	7,910,004
Invoices	25,421,299
Service Records	121,221,359

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🕒 Temporal Coverage

Production Dataset

August 2017 → June 2026

Coverage Span

8.8 Years

Update Frequency

Daily Refreshes

Source transaction systems update throughout the day. The production dataset is refreshed through a validated daily build process.

Historical Archive (Optional)

May 2008 → December 2015

A service-history gap exists between December 2015 and February 2018 due to platform transitions.

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✅ Data Quality Controls

The production dataset is generated through an automated validation pipeline.

Validation includes:

• Referential Integrity Testing
• Duplicate Detection
• Taxonomy Validation
• Sample Validation
• Multi-Database Consolidation Validation

Latest Build Results

Customer Integrity Violations: 0
Vehicle Integrity Violations: 0
Invoice Integrity Violations: 0
Service Integrity Violations: 0
Post-Dedup Duplicate Records: 0
Taxonomy Validation: PASSED
Sample Validation: PASSED

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🔗 Connected Data Model

The dataset is built around validated relationships between five core entities:

Customers
    ↓
Vehicles
    ↓
Invoices
    ↓
Service History
    ↓
Retail Locations

This structure supports longitudinal analysis across the entire automotive service lifecycle.

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📦 Included Sample Files

The sample dataset includes:

• sample_customers.csv
• sample_vehicles.csv
• sample_invoices.csv
• sample_service_history.csv
• sample_retail_locations.csv
• sample_service_business_types.csv
• sample_service_domains.csv
• sample_invoice_line_categories.csv

All entity relationships are preserved.

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📋 Sample Dataset Statistics

Entity	Records
Customers	100
Vehicles	100
Invoices	1,029
Service Records	4,417
Retail Locations	69

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✅ Sample Data Quality & Validation

The sample maintains:

• 100% Referential Integrity
• Zero Duplicate Customers
• Zero Duplicate Vehicles
• Zero Duplicate Invoices
• Zero Duplicate Service Records
• Zero Duplicate Retail Locations

Validation Results

Referential Integrity Violations: 0
Duplicate Keys: 0
Join Accuracy: 100%

All relationships are validated across:

Customers
→ Vehicles
→ Invoices
→ Service History
→ Retail Locations

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🧠 Analytics-Ready Intelligence

Beyond transactional records, the dataset includes feature-engineered metrics designed to accelerate analytics and machine learning workflows.

👤 Customer Intelligence

• Customer Tenure
• Visit Frequency
• Lifetime Value
• Loyalty Indicators
• Churn Indicators
• Fleet vs Retail Segmentation

🚘 Vehicle Intelligence

• Vehicle Year
• Vehicle Make
• Vehicle Model
• Vehicle Type
• Drive Type
• Transmission Type
• Vehicle Age
• Mileage Progression
• Annualized Mileage
• Lifecycle Metrics
• Fleet Classification
• Visit Counts

💰 Revenue Intelligence

• Gross Revenue
• Net Revenue
• Parts Revenue
• Labor Revenue
• Promotion Impact
• Average Ticket Value

🏪 Operational Intelligence

• Retail Location Revenue
• Vehicle Volume
• Invoice Volume
• Fleet Mix Analysis
• Retail Location Benchmarking
• Performance Metrics

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🔬 Example Analytical Applications

The dataset supports a wide range of analytical use cases.

👥 Customer Analytics

• Churn Prediction
• Loyalty Segmentation
• Customer Lifetime Value Modeling
• Retention Optimization

🔧 Predictive Maintenance

• Maintenance Forecasting
• Service Interval Analysis
• Vehicle Lifecycle Modeling
• Demand Planning

📈 Revenue Analytics

• Revenue Forecasting
• Promotion Effectiveness Analysis
• Ticket Value Modeling
• Trend Analysis

🏆 Operational Benchmarking

• Retail Location Performance
• Fleet Mix Analysis
• Throughput Analysis
• Volume Benchmarking

🤖 AI & Machine Learning

• Feature Engineering
• Predictive Modeling
• Customer Scoring
• Demand Forecasting

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🔒 Privacy & Security

Protecting customer and operational confidentiality is a foundational design principle.

The dataset includes:

• SHA-256 Anonymization
• No Personally Identifiable Information (PII)
• No VINs
• No Addresses
• No Phone Numbers
• No Email Addresses
• No Proprietary Identifiers

Hashed identifiers remain stable across entities while preventing re-identification.

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🏭 Data Provenance

The Growing-Moss Automotive Service Intelligence Dataset is:

• Production-Derived
• Multi-Year
• Multi-Location
• Longitudinal
• Commercially Licensed
• Fully Anonymized

No synthetic data is included.

All relationships are validated and preserved across the complete data model.

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📂 Extended Historical Archive (Optional)

Additional historical coverage is available from:

May 2008 → December 2015

Ideal for:

• Long-Horizon Forecasting
• Parts Demand Modeling
• Vehicle Lifecycle Analysis
• Macroeconomic Studies
• Longitudinal Trend Analysis

A service-history gap exists between December 2015 and February 2018 due to platform transitions.

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📜 Licensing

This sample dataset is provided for evaluation and research purposes.

Commercial use, redistribution, and re-identification attempts are prohibited.

The full production dataset is available under separate commercial licensing terms.

For licensing inquiries:

📧 sales@growing-moss.com

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🤝 Ready to Evaluate?

Request:

• Pricing
• Sample Data
• Schema Documentation
• Custom Extracts
• Historical Archives
• Enterprise Licensing

📧 sales@growing-moss.com

🌐 https://www.growing-moss.com

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📖 Citation

Growing-Moss Data (2026)

Automotive Service Intelligence Dataset

Production-derived automotive service, vehicle, customer, invoice, service history, and retail location intelligence connected through validated relationships and built for analytics, forecasting, AI/ML, benchmarking, and commercial product development.