Abstract: Embodiments of vehicle data systems for use in distributed computer network are disclosed. Particular embodiments may determine and enhance vehicle data from various data sources distributed across the computer network, and utilize the enhanced vehicle data in the determination of normalization metrics that account for geography and population density or spatial behavioral patterns. Embodiments may utilize these normalization metrics to determine or predict one or more metrics about participants in a network.

Abstract: Embodiments of vehicle data systems for use in distributed computer network are disclosed. Particular embodiments may determine and enhance vehicle data from various data sources distributed across the computer network, and utilize the enhanced vehicle data in the determination of normalization metrics that account for geography and population density or spatial behavioral patterns. Embodiments may utilize these normalization metrics to assign zone labels to geographic areas and present representations of the geographic areas based on the normalization metrics across the distributed computer network.

Abstract: Endogenous and exogenous variables associated with an item for sale by an affiliate may be displayed to a user of an affiliate decision-making tool. In response to the user selecting one of the endogenous variables, the affiliate decision-making tool may compute a number of introductions, a number of leads, and a number of sales for each of a plurality of possible values of the endogenous variable. The computation may be done utilizing a display position algorithm. A visualization of effects of setting the endogenous variable at different levels may be presented. The user may interact with the display position algorithm to vary one or more of the plurality of possible values of the endogenous variable such that the affiliate is eligible or disqualified to be displayed by an intermediary in response to a search for the item by a visitor of a network site owned and operated by the intermediary.

Abstract: Systems and method herein provide for sophisticated and efficient matching between users of a vehicle data system and data on sales that occur, where that data was obtained from distributed sources across a computer network. In particular, embodiments may obtain data from a variety of data sources across a distributed network and enhance data records by correlating the data obtained from these distributed sources. Data on sales thus obtain can be correlated with data on online users of the vehicle data system using a scoring engine to provide confidence scores to potential user-sale matches.

Abstract: 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: 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. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Abstract: Lead to recognition time delay data, leads and sales data, and lead distance data for leads arriving at a system within a time range can be used to determine a time variable close rate and a distance adjustment thereof which, in turn, can be used to generate a predicted close rate for each lead. Using the predicted close rate for each lead, an address drive distance for each lead, and dealer-level discount information, a logistic regression model can be trained to generate a score for each combination of a dealer, make, and zip code. Each score indicates a probability of closing a sale between a lead in the zip code and the dealer in the combination. Scores thus generated can be used by a computing facility in recommending dealers to a consumer in the zip code who has expressed an interest in purchasing a vehicle of the same make.

Abstract: A vehicle sales matching system may include a vehicle sales lead data database, a vehicle sales information database, and a sales matching system embodied on a non-transitory computer-readable medium and communicatively connected to the vehicle sales lead data database and the vehicle sales information database. The vehicle sales lead data may include validated customer data and third-party customer data. The vehicle sales information may include sales data from vehicle dealers, data extract services, and sales data sources. The sales matching system may be configured for applying one or more matching rules for matching a vehicle sales lead from the vehicle sales lead database to a vehicle sale from the vehicle sales information database.

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: 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.

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. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

Abstract: Systems and method herein provide for sophisticated and efficient matching between users of a vehicle data system and data on sales that occur, where that data was obtained from distributed sources across a computer network. In particular, embodiments may obtain data from a variety of data sources across a distributed network and enhance data records by correlating the data obtained from these distributed sources. Data on sales thus obtain can be correlated with data on online users of the vehicle data system using a scoring engine to provide confidence scores to potential user-sale matches.

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. Specifically, in certain embodiments, historical transaction data associated with a particular vehicle configuration may be obtained and processed to determine pricing data associated with the vehicle configuration. The historical transaction data or determined pricing data may then be presented in an intuitive manner.

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: Endogenous and exogenous variables associated with an item for sale by an affiliate may be displayed to a user of an affiliate decision-making tool. In response to the user selecting one of the endogenous variables, the affiliate decision-making tool may compute a number of introductions, a number of leads, and a number of sales for each of a plurality of possible values of the endogenous variable. The computation may be done utilizing a display position algorithm. A visualization of effects of setting the endogenous variable at different levels may be presented. The user may interact with the display position algorithm to vary one or more of the plurality of possible values of the endogenous variable such that the affiliate is eligible or disqualified to be displayed by an intermediary in response to a search for the item by a visitor of a network site owned and operated by the intermediary.

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: Systems, methods and computer program products for selecting dealers based on characteristics of the dealers and the user. A vehicle data system collects dealer location and historical transaction data from external data sources and generates and stores an eligibility table that identifies a set of eligible dealers for each combination of user location and vehicle make. Eligible dealers are determined from the eligibility table using a location and vehicle make identified from a user request. Scores are determined for each eligible dealer based on a dealer scoring model using a binary choice model in the form of a logistic regression of market share, inventory, close rate, price and distance, and dealers are ranked by these scores. A presentation of dealers selected by rank and by closest location to the user is generated and provided to the user via an interface running on a computing device.

Abstract: Systems, method and computer program products for presenting to a user a visualization of a vehicle dealer performance assessment based on dealer location and vehicle sales transaction data, where the assessment is based on geographically normalized metrics. Dealer location data and historical vehicle sales transaction data is collected by a in a vehicle data system from external data sources. Distances from dealers to geographical regions of interest are determined, and differences between these distances are normalized to produce competition zone indices for the geographical regions. The competition zone indices are then used to aggregate the geographical regions into different competition zones in which dealers of interest have corresponding levels of competitive advantage or disadvantage, normalized according to typical distances associated with transactions in the respective geographical regions.

Abstract: Embodiments of systems and methods disclosed herein provide solutions for predicting vehicle invoice pricing, trim, and options by starting from scratch (raw data), transforming the raw data into actionable data, and comprehensively exploring all possible option combinations that can match a vehicle's Manufacturer Suggested Retail Price (MSRP). Embodiments implement algorithms that determine the trim of a vehicle and intelligently analyze all possible option combinations that would result in a target MSRP while avoid searching unnecessary vehicle configurations. The algorithms can be used to predict the trim, invoice, and options of a vehicle based on the vehicle's VIN and MSRP.

Abstract: 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.