Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments disclosed herein can produce and present sustainable price information to help dealers to price vehicles for sustainability and facilitate consumers in making purchase decisions. In one approach, a sustainable price range for a specific vehicle configuration may be based on an average profit margin (APM) determined utilizing historical sale prices and an estimated actual dealer cost. Other approaches may utilize some or all of the following steps: determine APM and build a model of distribution of profit margins by APM, build a model to adjust APM by certain variables, identify sustainable levels for the inventory, production, and incentives variables feeding into the APM model, plug those in to get the averages for those sustainable levels, identify sustainable percentile cutoffs for a given profit margin, then use this relationship with the now identified sustainable levels as inputs to find the overall sustainable profit margin.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: 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.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: 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.

Abstract: Embodiments disclosed herein provide systems and methods for the filtering, selection and presentation of vendors accounting for both user characteristics and vendor characteristics, such that the systems and methods may be used by both customer and vendor alike to better match customer needs with the resource-constrained vendors with whom a successful sale has a higher probability of occurring. Embodiments may include filtering, selecting and/or presenting vendors to a user sorted by the probability that the particular vendor will possess the characteristics that appeal to a particular customer and therefore result in a large probability of sale and suppress presentation of those vendors that are unlikely to be selected by the customer since their characteristics are less consistent with those needed by the customer and, therefore, are unlikely to result in a sale.

Abstract: Embodiments disclosed herein can provide consumers with an effective tool for evaluating the negotiability of prices for vehicles in the marketplace. The tool may include a Price Flexibility Score which measures the elasticity of transaction prices by vehicle model. Specifically, a method may dynamically incorporate factors that affect price variance, convert those factors into variables, generate order statistics for each of the variables, apply a weighting factor to the variables to generate a price flexibility score for each make-model, and determine a negotiability index utilizing the price flexibility score. In one embodiment, the process of determining the negotiability index may be fully driven by a price flexibility model that incorporates a plurality of factors.

Abstract: 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.

Abstract: Embodiments of systems and methods for the aggregation, analysis, display and monetization of pricing data for commodities in general, and which may be particularly useful applied to vehicles are disclosed. In certain embodiments, one or more models may be applied over a set of historical transaction data associated with a vehicle configuration to determine pricing data. Some models may leverage incremental data in various conditions, including cases where fewer than a desired number of historical transactions are present in the bin of a specified vehicle, where fewer than, equal to, or more than a certain number of list prices for the specified vehicle available, and where no historical transaction data for new models is available.

Abstract: Embodiments disclosed provide a system, method, and computer program product for identifying consumer items more likely to be bought by an individual user. In some embodiments, a collaborative filter may be used to rank items based on the degree to which they match user preferences. The collaborative filter may be hierarchical and may take various factors into consideration. Example factors may include the similarity among items based on observable features, a summary of aggregate online search behavior across multiple users, the item features determined to be most important to the individual user, and a baseline item against which a conditional probability of another item being selected is measured.

Abstract: A vehicle identification number (VIN) decoder (VDC) implementing a unique VIN decoding method may, for a given VIN, shorten the VIN and form a stem and a leaf therefrom. Utilizing the stem, the VDC may operate to find matching leaf values, if any, from a set of look up tables. Depending upon a match outcome, one or more trim identification code (TIC) values can be assigned to the VIN and a candidate list can be constructed utilizing the assigned TIC value(s). The candidate list, which can be optimized, may contain one or more candidate trims for the VIN. For each candidate trim, a confidence score and match probability can be generated. The VDC may provide decoded information containing trim data associated with at least one of the one or more candidate trims for the VIN to a client device over a network connection.

Abstract: 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.

Abstract: In embodiments disclosed herein, a computer may operate to determine, for each member in a set and each non-member under consideration for the set, an expected revenue to be passed to a parent organization. The expected revenue may be determined utilizing a plurality of weighted features. Members in the set may be ranked according to their expected revenues. Top performing members in the set may be protected to ensure their expected revenues. Bottom performing members may be removed from the set and/or replaced with new members outside of geographical protective areas of the top performing members. The optimization process can be repeated until the set of members can satisfy an existing demand while maximizing revenue for the parent organization.