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 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: 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: A vehicle data system may receive, via a website, a user query about a vehicle or features of a vehicle that may not actually exist. The vehicle data system can transform vehicle features representing a user-configured vehicle, compare the user-configured vehicle with inventory vehicles, determine how similar the user-configured vehicle is to each inventory vehicle, how likely each inventory vehicle may be purchased given the user-configured vehicle and consumer behavior modeled based on actual historical transaction data collected via the website. The vehicle features may be weighted. Feature weights can be automatically determined and continuously fine-tuned utilizing machine learning. Each inventory vehicle is scored utilizing a similarity vector that compares it with the user-configured vehicle. Top-ranked inventory vehicle(s) can then be recommended to the user via the website in real time.

Abstract: A vehicle data system may receive, via a website, a user query about a vehicle or features of a vehicle that may not actually exist. The vehicle data system can transform a set of features representing a query vehicle associated with the user query into a query vehicle feature vector, compare the query vehicle feature vector with one or more inventory vehicle feature vectors representative of one or more dealer inventory vehicles, determine a similarity score between the query vehicle and the one or more dealer inventory vehicles, and generate at least one recommendation based on the similarity score, for instance, a great match and/or a good match to the vehicle that the user has inquired about. The at least one recommendation can be presented via the website in real time.

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: Embodiments of systems and methods disclosed herein provide solutions for determining popular configurations in an automated and balanced manner, useful in facilitating consumers in making their purchase decisions on high value items such as vehicles. In particular, in certain embodiments, a user may utilize a vehicle data system to obtain pricing data corresponding to automatically generated vehicle configurations. When the user is presented with the pricing data associated with the automatically generated vehicle configuration the user may additionally be presented with an upfront price offered by a dealer.

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.