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Automotive Payment-focused Data

The Gold Standard in data analytics in the automotive industry

“Big Data
without Science
is Just Noise”

Rusty West, President
& CEO of Market Scan

Market Scan’s automotive industry database is the Gold Standard by which all others are measured. It is the most timely, accurate, and comprehensive repository of payment-centric data in the world.

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Market Scan employs agile development techniques to electronically aggregate Manufacturer Data, Lender Data, Municipal Market Data, and Dealership Data, and accurately applies all this data to any conceivable consumer scenario.

OEM Data

All auto manufacturer rebates, incentives, and stackability rules are electronically aggregated. Every new and used vehicle for the past two decades is accurately represented (year, make, model, trim, cost, MSRP, shipping, gas guzzler, bore, stroke, number of cylinders, EPA mileage, laden weight, GVWR, etc.).

Lender Data

Every parameter, lender policy & factor that can influence a lease, finance, or balloon payment note from every lender that has a published program is electronically aggregated (rate, residual value, bank fee, low mileage enhancements, high mileage deductions, zero-standard-multiple security deposit policies, max advance, max rate, rate participation, hard/soft adds, etc.)

Municipal Data

Every law, rule, regulation, calculation methodology, tax percentage rate, and applicable registration fee is included.

Dealership Data

All terms and conditions under which every participating dealer is willing to transact are housed for each respective rooftop (inventory, selling price rules by vehicle, available lenders, dealer-defined fees, DMS calculation settings, quoting preferences, etc.).

Consumer Rebates and Incentives

Automatically apply all loyalty and conquest incentives based on trade-in or current vehicle. Accurately evaluate all available consumer credit criteria and apply all published lender buying policies and practices to predict “call-backs”.

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Contact us so we can assess your needs and demonstrate the power of our data.

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