An artificial intelligence (AI) and dealer management (DMS) predictive computer-implemented method implemented on one or more computing devices comprising non-transitory memory and at least one processor, the method comprising:
- retrieving, with the one or more computing devices over a network, 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 and (ii) historical behavior of the consumer, wherein the individual consumer data is retrieved from an interview including a plurality of interview questions being electronically administered to the consumer using a computing device accessible by the consumer, the individual consumer data including response point values associated with each interview question;
- retrieving, with the one or more computing devices, dealer management system (DMS) data from a DMS of an automotive dealership, the DMS data indicative of transactional data relating to a plurality of automotive commodities being finance or insurance (F&I) products or services associated with the vehicle of interest;
- utilizing, with the one or more computing devices, one or more application programming interfaces (APIs) to acquire, over the network, consumer resource data from an online consumer resource provided on a website hosted by a web server, the one or more APIs being utilized to provide accessibility to various datasets provided by the online consumer resource relating to at least one of the automotive commodities and the vehicle of interest;
- retrieving, with the one or more computing devices, from the DMS, 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;
- predictively analyzing, with the one or more computing devices, the individual consumer data, DMS data, consumer resource data, and deal data by utilizing a computer-implemented algorithm and the predictive analysis model by the one or more computing devices to:
- compute a needs value associated with each of the automotive commodities based on the individual consumer data retrieved from the interview being electronically administered to the consumer, the needs value includes a point value from +10 to −10 scale×100 returned from a combination of point values assigned to the interview questions;
- compute a likelihood value associated with each of the automotive commodities based on the retrieved DMS data, the retrieved consumer resource data, and the retrieved deal data, the likelihood value determined as a percentage;
- compute a predictive analytics score associated with each of the automotive commodities based on a corresponding needs value multiplied by a first weighted value of the predictive analysis model and a corresponding likelihood value multiplied by a second weighted value of the predictive analysis model, each predictive analytics score being determined as a percentage indicative of a probability of sale of a corresponding automotive commodity;
- and
- filtering the plurality of automotive commodities to a subset of automotive commodities based on the predictive analytics score associated with each of the automotive commodities;
- continuously and periodically updating the predictive analysis model by further utilizing a computer-implemented algorithm to:
- output, with the one or more computing devices, an electronic presentation as dynamically generated for the consumer including an analytics page displayable on a display of a computing device accessible by the consumer, the analytics page displaying a set of one or more interactive visual icons corresponding to each of the subset of automotive commodities including a respective predictive analytics scores, the corresponding needs value, and the corresponding likelihood value associated with each automotive commodity, wherein the set of one or more interactive visual icons are sized on the display, placed on the display, or are displayed in an order based on one or more predicted outcomes of an individual consumer selecting a respective one of the set of one or more interactive visual icons from the display;
- receive a selection of the individual consumer indicating an actual selection of a selected interactive visual icon from the display screen;
- update the predictive analysis model for, wherein updating the model comprises tuning one or more of the weights of the predictive analysis model based on comparison of the actual selection of the selected interactive visual icon from the display with the one or more predicted outcomes to improve the accuracy of the predictive analysis model;
- and
- output an updated electronic presentation as dynamically generated for the individual consumer and including an updated set of one or more interactive visual icons corresponding to an updated set of automotive commodities comprising respective updated predictive analytics scores determined by the predictive analysis model as updated,
- wherein the updated set of one or more interactive visual icons are sized on the display, placed on the display, or displayed in an order that is different from the set of one or more interactive visual icons as originally displayed to visually indicate the individual consumer's actual interaction with the selected interactive visual icon.