System and method for dealer evaluation and dealer network optimization using spatial and geographic analysis in a network of distributed computer systems
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.
1. A vehicle data system for determining and utilizing spatial or geography based metrics in a distributed computing environment based on enhanced data obtained from distributed sources, comprising:
a plurality of computing devices coupled to one another, one or more user computer devices, and a plurality of distributed data sources over a network, wherein:
a first computer device of the vehicle data system performs a process including:
obtaining a set of historical transaction data associated with a vehicle make from a first distributed data source, where the set of historical transaction data comprises data on transactions associated with vehicles of the vehicle make;
applying one or more transformations to the set of historical transaction data to create a modified set of historical transaction data that includes additional vehicle data collected from a second distributed data sources by VIN by correlating the additional vehicle data collected from the second distributed data sources with data on transactions of the set of historical transaction data;
determining a competition zone index for a first dealer, a geographic area and a make of vehicle, the competition zone index quantifying the competitiveness of the first dealer in the geographic area, wherein determining a competition zone index comprises determining a distance between the geographic area and the first dealer, a distance between the geographic area and a closest second dealer, and a typical distance traveled from the zip code to purchase a vehicle of the vehicle make;
creating a first training set of historical transaction data, the first training set comprising historical transaction data and modified historical transaction data;
training a universal sales model at a first time using the first training set based on the competition zone;
creating a second training set of historical transaction data, the second training set comprising modified historical transaction datal;
training the universal sales model at a second time using the second training set based on the competition zone;
receiving a request, the request associated with the first dealer and specifying the make;
identifying a set of geographic areas within a distance of the first dealer;
determining a predicted number of sales for the first dealer in a geographic area of the set of geographic areas based on the competition zone index for the first dealer and the universal sales model;
generating an interface providing a visual representation of the geographic area and the predicted number of sales of the geographic area associated with the first dealer and the vehicle make; and
responding to the request by distributing the generated interface over the network.
2. The system of claim 1 , wherein a zone label is assigned to the geographic area for the first dealer.
3. The system of claim 1 , wherein the set of historical transaction data is determined based on the first dealer.
4. The system of claim 1 , wherein the competition zone index is a dealer competition zone index or a Customer Competition Zone (CCZ) index.
5. The system of claim 1 , wherein the predicted number of sales is based on a dealer network associated with the first dealer.
6. The system of claim 5 , wherein the predicted number of sales is based on a removal of the first dealer from the dealer network or an addition of the first dealer to the dealer network.
7. A method, comprising:
obtaining a set of historical transaction data associated with a vehicle make from a first distributed data source, where the set of historical transaction data comprises data on transactions associated with vehicles of the vehicle make;
applying one or more transformations to the set of historical transaction data to create a modified set of historical transaction data that includes additional vehicle data collected from a second distributed data sources by VIN by correlating the additional vehicle data collected from the second distributed data sources with data on transactions of the set of historical transaction data;
determining a competition zone index for a first dealer, a geographic area and a make of vehicle, the competition zone index quantifying the competitiveness of the first dealer in the geographic area, wherein determining a competition zone index comprises determining a distance between the geographic area and the first dealer, a distance between the geographic area and a closest second dealer, and a typical distance traveled from the zip code to purchase a vehicle of the vehicle make;
creating a first training set of historical transaction data, the first training set comprising historical transaction data and modified historical transaction data;
training a universal sales model at a first time using the first training set based on the competition zone;
creating a second training set of historical transaction data, the second training set comprising modified historical transaction datal;
training the universal sales model at a second time using the second training set based on the competition zone;
receiving a request, the request associated with the first dealer and specifying the make;
identifying a set of geographic areas within a distance of the first dealer;
determining a predicted number of sales for the first dealer in a geographic area of the set of geographic areas based on the competition zone index for the first dealer and the universal sales model;
generating an interface providing a visual representation of the geographic area and the predicted number of sales of the geographic area associated with the first dealer and the vehicle make; and
responding to the request by distributing the generated interface over the network.
8. The method of claim 7 , wherein a zone label is assigned to the geographic area for the first dealer.
9. The method of claim 7 , wherein the set of historical transaction data is determined based on the first dealer.
10. The method of claim 7 , wherein the competition zone index is a dealer competition zone index or a Customer Competition Zone (CCZ) index.
11. The method of claim 7 , wherein the predicted number of sales is based on a dealer network associated with the first dealer.
12. The method of claim 11 , wherein the predicted number of sales is based on a removal of the first dealer from the dealer network or an addition of the first dealer to the dealer network.
13. A non-transitory computer readable medium, comprising instructions for:
obtaining a set of historical transaction data associated with a vehicle make from a first distributed data source, where the set of historical transaction data comprises data on transactions associated with vehicles of the vehicle make;
applying one or more transformations to the set of historical transaction data to create a modified set of historical transaction data that includes additional vehicle data collected from a second distributed data sources by VIN by correlating the additional vehicle data collected from the second distributed data sources with data on transactions of the set of historical transaction data;
determining a competition zone index for a first dealer, a geographic area and a make of vehicle, the competition zone index quantifying the competitiveness of the first dealer in the geographic area, wherein determining a competition zone index comprises determining a distance between the geographic area and the first dealer, a distance between the geographic area and a closest second dealer, and a typical distance traveled from the zip code to purchase a vehicle of the vehicle make;
creating a first training set of historical transaction data, the first training set comprising historical transaction data and modified historical transaction data;
training a universal sales model at a first time using the first training set based on the competition zone;
creating a second training set of historical transaction data, the second training set comprising modified historical transaction datal;
training the universal sales model at a second time using the second training set based on the competition zone;
receiving a request, the request associated with the first dealer and specifying the make;
identifying a set of geographic areas within a distance of the first dealer;
determining a predicted number of sales for the first dealer in a geographic area of the set of geographic areas based on the competition zone index for the first dealer and the universal sales model;
generating an interface providing a visual representation of the geographic area and the predicted number of sales of the geographic area associated with the first dealer and the vehicle make; and
responding to the request by distributing the generated interface over the network.
14. The non-transitory computer readable medium of claim 13 , wherein a zone label is assigned to the geographic area for the first dealer.
15. The non-transitory computer readable medium of claim 13 , wherein the set of historical transaction data is determined based on the first dealer.
16. The non-transitory computer readable medium of claim 13 , wherein the competition zone index is a dealer competition zone index or a Customer Competition Zone (CCZ) index.
17. The non-transitory computer readable medium of claim 13 , wherein the predicted number of sales is based on a dealer network associated with the first dealer.
18. The non-transitory computer readable medium of claim 17 , wherein the predicted number of sales is based on a removal of the first dealer from the dealer network or an addition of the first dealer to the dealer network.