SYSTEM, METHOD, AND SOFTWARE FOR PREDICTING THE LIKELIHOOD OF SELLING AUTOMOTIVE COMMODITIES

• US 20160180358A1
• Filed: 12/22/2015
• Published: 06/23/2016
• Est. Priority Date: 12/22/2014
• Status: Active Grant

• Current Assignee: Superior Integrated Solutions, Inc.
• Original Assignee: Superior Integrated Solutions, Inc.
• Inventors: Battista, Phillip
• Granted Patent: US 11,443,332 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 2219/23258 GUI graphical user interface, icon, function bloc editor, labview
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0629 for generating comparisons
• CPC Class Codes: G06Q 30/0643 Graphical representation of items or shoppers
• CPC Class Codes: G06Q 40/08 Insurance

Abstract

Provided are systems, methods, and software for determining a probability of selling to a consumer an automotive commodity associated with a vehicle of interest to the consumer. The software retrieves individual consumer data indicating at least one of present or future needs of the consumer, historical behavior of the consumer, and demographics of the consumer. The software retrieves dealer management system (DMS) data indicating transactional data relating to automotive commodities associated with the vehicle. Deal data indicating transactional terms relating to at least one of current acquisition of the vehicle of interest by the consumer, and past acquisition of the vehicle of interest or vehicles similar to the vehicle of interest by other consumers, is retrieved. The software predicatively analyzes the individual consumer data, DMS data, and deal data and computes a probability of selling at least one of the automotive commodities to the consumer based on the predictive analysis.

Claims

1. A system, comprising:

at least one processor;

a non-transitory memory; and

instructions stored on the non-transitory memory, wherein the instructions when executed by the processor cause the processor to;

retrieve individual consumer data about a consumer interested in acquiring a vehicle, the individual consumer data indicative of at least one of (i) present or future needs of the consumer, (ii) historical behavior of the consumer, and (iii) demographics of the consumer;

retrieve dealer management system (DMS) data from a DMS, the DMS data indicative of transactional data relating to a plurality of automotive commodities associated with the vehicle of interest;

retrieve deal data indicative of transactional terms relating to at least one of (i) current acquisition of the vehicle of interest by the consumer, and (ii) past acquisition of the vehicle of interest or vehicles similar to the vehicle of interest by other consumers;

predicatively analyze at least the individual consumer data, the DMS data, and the deal data; and

compute a probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of at least the individual consumer data, the DMS data, and the deal data.

2. The system of claim 1 wherein the instructions when executed by the processor further cause the processor to compute the probability of selling each one of the plurality of automotive commodities and filter the plurality of automotive commodities to a subset of automotive commodities based on the computed probabilities.

3. The system of claim 2 wherein the instructions when executed by the processor further cause the processor to rank the subset of automotive commodities based on the computed probability of each automotive commodity of the subset.

4. The system of claim 1 wherein the instructions when executed by the processor further cause the processor to generate a presentation being displayable on a display of a computing device and being customized for the consumer by having integrated into the presentation the individual consumer data and suggestions for purchasing the at least one automotive commodity for which probability of selling was computed.

5. The system of claim 1 wherein the instructions when executed by the processor further cause the processor to generate a visual indicator that is displayable on a display of a computing device, wherein the visual indicator relates to the computed probability of selling the at least one of the automotive commodities.

6. The system of claim 1 wherein the instructions when executed by the processor further cause the processor to retrieve group consumer data about other consumers who have previously purchased the vehicle of interest, vehicles similar to the vehicle of interest, or automotive commodities associated with the vehicle of interest, the group consumer data indicative of at least one of (i) past needs of the other consumers, (ii) historical behavior of the other consumers, and (iii) demographics of the other consumers, and the processor further predicatively analyzing the group consumer data and further computing the probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of the group consumer data.

7. The system of claim 1 wherein the instructions when executed by the processor further cause the processor to retrieve consumer resource data about at least one of the automotive commodities or the vehicle of interest from an online consumer resource, and the processor further predicatively analyzing the consumer resource data and further computing the probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of the consumer resource data.

8. The system of claim 1 wherein the individual consumer data is retrieved at least in part from an interview being electronically administered to the consumer using a computing device.

9. The system of claim 1 wherein the automotive commodity is a finance or insurance product or service.

10. The system of claim 1 wherein the at least one processor and the non-transitory memory are implemented on a computing device that is located at an automotive dealership.

11. A computer-implemented method comprising the steps of:

retrieving individual consumer data about a consumer interested in acquiring a vehicle, the individual consumer data indicative of at least one of (i) present or future needs of the consumer, (ii) historical behavior of the consumer, and (iii) demographics of the consumer;

retrieving dealer management system (DMS) data from a DMS, the DMS data indicative of transactional data relating to a plurality of automotive commodities associated with the vehicle of interest;

retrieving deal data indicative of transactional terms relating to at least one of (i) current acquisition of the vehicle of interest by the consumer, and (ii) past acquisition of the vehicle of interest or vehicles similar to the vehicle of interest by other consumers;

predicatively analyzing at least the individual consumer data, the DMS data, and the deal data; and

computing a probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of at least the individual consumer data, the DMS data, and the deal data.

12. The computer-implemented method of claim 11 further comprising the steps of computing the probability of selling each one of the plurality of automotive commodities and filtering the plurality of automotive commodities to a subset of automotive commodities based on the computed probabilities.

13. The computer-implemented method of claim 12 further comprising the step of ranking the subset of automotive commodities based on the computed probability of each automotive commodity of the subset.

14. The computer-implemented method of claim 11 further comprising the step of generating a presentation being displayable on a display of a computing device and being customized for the consumer by having integrated into the presentation the individual consumer data and suggestions for purchasing the at least one automotive commodity for which probability of selling was computed.

15. The computer-implemented method of claim 11 further comprising the step of generating a visual indicator that is displayable on a display of a computing device, wherein the visual indicator relates to the computed probability of selling the at least one of the automotive commodities.

16. The computer-implemented method of claim 11 further comprising the steps of retrieving group consumer data about other consumers who have previously purchased the vehicle of interest, vehicles similar to the vehicle of interest, or automotive commodities associated with the vehicle of interest, the group consumer data indicative of at least one of (i) past needs of the other consumers, (ii) historical behavior of the other consumers, and (iii) demographics of the other consumers, further predicatively analyzing the group consumer data and further computing the probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of the group consumer data.

17. The computer-implemented method of claim 11 further comprising the steps of retrieving consumer resource data about at least one of the automotive commodities or the vehicle of interest from an online consumer resource, further predicatively analyzing the consumer resource data and further computing the probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of the consumer resource data.

18. The computer-implemented method of claim 11 further comprising the steps of retrieving the individual consumer data at least in part from an interview being electronically administered to the consumer using a computing device.

19. The computer-implemented method of claim 11 wherein the automotive commodity is a finance or insurance product or service.

20. A software product comprising instructions storable on a non-transitory computer-readable medium wherein the instructions when executed by at least one processor, are configured to:

retrieve individual consumer data about a consumer interested in acquiring a vehicle, the individual consumer data indicative of at least one of (i) present or future needs of the consumer, (ii) historical behavior of the consumer, and (iii) demographics of the consumer;

retrieve dealer management system (DMS) data indicative of transactional data relating to a plurality of automotive commodities associated with the vehicle of interest;

retrieve deal data indicative of transactional terms relating to at least one of (i) current acquisition of the vehicle of interest by the consumer, and (ii) past acquisition of the vehicle of interest or vehicles similar to the vehicle of interest by other consumers;

retrieve group consumer data about other consumers who have previously purchased the vehicle of interest, vehicles similar to the vehicle of interest, or automotive commodities associated with the vehicle of interest, the group consumer data indicative of at least one of (i) past needs of the other consumers, (ii) historical behavior of the other consumers, and (iii) demographics of the other consumers;

retrieve consumer resource data about at least one of the automotive commodities or the vehicle of interest from an online consumer resource;

predicatively analyze at least two of the individual consumer data, DMS data, deal data, group consumer data, and consumer resource data; and

compute a probability of selling at least one of the automotive commodities to the consumer based on predictive analysis of at least two of the individual consumer data, DMS data, deal data, group consumer data, and consumer resource data.