Truecar, Inc.

Zilliant Incorporated

Home Depot International, Inc.

Xingchu Liu

Michael D Swinson

Mikhail Semeniuk

Isaac Lemon Laughlin

Meghashyam Grama Ramanuja

Param Pash Kaur Dhillon

Thomas J Sullivan

Shi Zhong

Loren Williams

Reetabrata Mookherjee

Lukas Bouvrie

Disclosed are embodiments for the aggregation and analysis of vehicle prices via a geo-specific model. Data may be collected at various geo-specific levels such as a ZIP-Code level to provide greater data resolution. Data sets taken into account may include demarcation point data sets and data sets based on vehicle transactions. A demarcation point data set may be based on consumer market factors that influence car-buying behavior. Vehicle transactions may be classified into data sets for other vehicles having similar characteristics to the vehicle. A geo-specific statistical pricing model may then be applied to the data sets based on similar characteristics to a particular vehicle to produce a price estimation for the vehicle.

System, method and computer program product for geo-specific vehicle pricing

To increase the efficiency of a used vehicle data processing process while still tailoring to an individual user's unique specifications on a used vehicle, at least some pre-calculations are performed by a backend process before a request for data for a specified vehicle is received through a web site or a web server on the Internet. A user can be presented with an interface where the user can make a variety of determinations. After the user requests data on a specific vehicle configuration, a frontend process handles user-provided data in conjunction with the data calculated in the backend process to ensure that the results are better tailored to the user's specific vehicle attributes. The results can include pricing data such as a trade-in price, a list price, an expected sale price or range of sale prices, market low sale price, market average sale price, market high sale price, etc.

System and method for analysis and presentation of used vehicle pricing data

System, method and computer program product for geo specific vehicle pricing

Systems, methods and computer program products that determine wholesale prices for durable goods such as used vehicles. In one embodiment, a system includes a computer processor, a network interface, a data storage device, and an output device. The system collects and stores, via the network interface, historical sales information from external data sources. A retail price for a target vehicle is determined based on the collected information. Condition information for the vehicle is collected and used to adjust the retail price to arrive at a wholesale price, which is output to a user. The system may also adjust the price based on repair costs associated with identified vehicle conditions. The system may collect cost information associated with a wholesale sale transaction and deduct these costs and a profit margin from the wholesale price to produce a trade-in price.

Wholesale/trade-in pricing system, method and computer program product therefor

Systems and methods for the aggregation, analysis, and display of data for used vehicles are disclosed. Historical transaction data for used vehicles may be obtained and processed to determine pricing data, where this determined pricing data may be associated with a particular configuration of a vehicle. The user can then be presented with an interface pertinent to the vehicle configuration utilizing the aggregated data set or the associated determined data where the user can make a variety of determinations. This interface may, for example, be configured to present the historical transaction data visually, with the pricing data such as a trade-in price, a list price, an expected sale price or range of sale prices, market low sale price, market average sale price, market high sale price, etc. presented relative to the historical transaction data.

Natural language processing (NLP) approaches may be utilized to map two strings. The strings may come from sources utilizing different naming conventions. One example may be a data aggregator that collects used car transaction information. Another example may be a comprehensive database listing all possible manufacturer-defined vehicle options. A NLP system may operate to determine whether a source string is present in a target string and outputting a match containing the source string and the target string if the source string is present in the target string or computing a similarity factor if the source string is not present in the target string. The similarity factor representing a measure of similarity between two strings may be computed based on a plurality of parameters, including a Levenshtein edit distance parameter. The computed similarity can be used to find pricing information, including trade-in, sale, and list prices, across disparate naming conventions.

Natural language processing system, method and computer program product useful for automotive data mapping

In response to a user request for information on the best/worst days in an upcoming time period to buy a commodity, a vehicle data system may determine anticipated daily discounts applicable to the commodity. An example commodity may be a vehicle of a specific configuration. In one embodiment, characteristics of month, day of week, and day of month may be gathered and fed into a Best Day to Buy model to determine, for each day of the time period, a projected daily discount relative to a set price for the commodity. Additional input variables such as incentives and seasonal discounts may be included. From the computed daily discounts, the vehicle data system may determine the best day and/or the worst day to buy and report same to the user.

System, method and program product for predicting best/worst time to buy

To determine pricing recommendations for goods and service products in a business-to-business environment, a set of transaction data corresponding to a set of products are processed to generate a set of pricing recommendations optimized according to an objective. Furthermore, a set of product segments may be determined, transaction data may be associated with one or more of the product segments and a demand model and associated price elasticity may be formulated for one or more of the product segments based upon the transaction data associated with the segment. Using these formulated price elasticities, pricing recommendations for each product may be determined for each of a set of customers. Using an optimization process, price elasticities are used to determine price dependent entity goals for any combination of products, customers and sets of prices, using a mathematical objective function.

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Austin, TX, United States of America

Xingchu Liu