Systems and methods for determining and leveraging geography-dependent relative desirability of products

1 . A computer-implemented method for regulating vehicle stock, the method comprising:

receiving, by one or more processors, query data including one or more queries indicative of one or more characteristics of one or more vehicles;

receiving, by the one or more processors, from a database, transaction data including a quantity of the one or more vehicles that were purchased or attempted to be purchased;

monitoring, by the one or more processors, vehicle inventory of a merchant;

determining, by the one or more processors, using at least one trained machine learning model, a desirability value for each vehicle of the vehicle inventory of the merchant based on the query data and the transaction data;

assigning, by the one or more processors, a likelihood value for each vehicle of the vehicle inventory of the merchant, wherein the likelihood value is a likelihood that each vehicle of the vehicle inventory of the merchant will be purchased based on one or more purchaser preferences and the desirability value in comparison to a threshold desirability value;

transmitting, by the one or more processors, a recommendation to a graphical user interface of the merchant, the recommendation based on the likelihood value and the desirability value of each vehicle of the vehicle inventory of the merchant;

receiving, by the one or more processors, additional transaction data from a purchaser info database, the additional transaction data including whether a vehicle purchaser purchased or failed to purchase a vehicle of the vehicle inventory of the merchant; and

transmitting, by the one or more processors, a second recommendation to the graphical user interface of the merchant, the second recommendation dynamically determined based on the at least one trained machine learning model determining an updated desirability value for an updated vehicle inventory based on the additional transaction data.

2 . The computer-implemented method of claim 1 , wherein the query data includes data indicating vehicle preferences, including at least one of a vehicle year, a vehicle make, a vehicle model, a vehicle color, a vehicle type, a vehicle transmission, a vehicle door count, or a vehicle condition.

3 . The computer-implemented method of claim 1 , wherein the recommendation is based on at least one of a financial status of a purchaser including a credit profile and at least one of a credit score, a debt amount, a credit segmentation, or a pre-approved loan amount.

4 . The computer-implemented method of claim 1 , wherein the transaction data including the quantity of the one or more vehicles that were purchased or attempted to be purchased comprises vehicles within a predetermined distance of a location of the merchant or within a predetermined time period.

5 . The computer-implemented method of claim 1 , wherein the transaction data includes data indicating one or more loan instruments associated with the quantity of the one or more vehicles that were purchased or attempted to be purchased.

6 . The computer-implemented method of claim 1 , wherein the transaction data includes data indicating an available inventory of merchants in a predetermined area associated with the quantity of the one or more vehicles that were purchased or attempted to be purchased.

7 . The computer-implemented method of claim 1 , wherein determining the desirability value for each vehicle of the vehicle inventory of the merchant is based on a number of vehicles purchased in a predetermined historical period.

8 . The computer-implemented method of claim 1 , wherein determining the desirability value for each vehicle of the vehicle inventory of the merchant further includes learning relationships between demographics of purchasers by the at least one trained machine learning model.

9 . The computer-implemented method of claim 1 , wherein the query data includes at least one of a mileage, a total price, a monthly payment, a vehicle category, a body style, a condition, a feature, a fuel economy, a drive type, a specification, a zip code, or a selected one or more merchants of a plurality of merchants.

10 . The computer-implemented method of claim 1 , wherein the recommendation includes adjusting the vehicle inventory of the merchant based on the desirability value for each vehicle of the vehicle inventory of the merchant.

11 . A computer-implemented method for regulating vehicle stock, the method comprising:

receiving, by one or more processors, query data including one or more queries indicative of one or more characteristics of one or more vehicles;

receiving, by the one or more processors, from a database, transaction data including a quantity of the one or more vehicles that were purchased or attempted to be purchased;

monitoring, by the one or more processors, vehicle inventory of a merchant;

determining, by the one or more processors, using at least one trained machine learning model, a desirability value for each vehicle of the vehicle inventory of the merchant based on the query data;

assigning, by the one or more processors, a likelihood value for each vehicle of the vehicle inventory of the merchant, wherein the likelihood value is a likelihood that each vehicle of the vehicle inventory of the merchant will be purchased based on a financial status of a user and the desirability value in comparison to a threshold desirability value;

transmitting, by the one or more processors, a recommendation to a graphical user interface of the merchant, the recommendation based on the likelihood value and the desirability value of each vehicle of the vehicle inventory of the merchant;

receiving, by the one or more processors, additional transaction data from a purchaser info database, the additional transaction data including whether a vehicle purchaser purchased or failed to purchase a vehicle of the vehicle inventory of the merchant; and

transmitting, by the one or more processors, a second corresponding recommendation to the graphical user interface of the merchant, the second corresponding recommendation dynamically determined based on the at least one trained machine learning model determining an updated desirability value for an updated vehicle inventory based on the additional transaction data.

12 . The computer-implemented method of claim 11 , wherein the likelihood value is further based on purchaser preferences comprising a vehicle year, a vehicle make, a vehicle model, a vehicle color, a vehicle type, a vehicle transmission, a vehicle door count, or a vehicle condition.

13 . The computer-implemented method of claim 12 ,

wherein the likelihood value is further based on one or more purchaser financial statuses associated with one or more purchasers, the one or more purchaser financial statuses including data indicating a credit profile, the credit profile including at least one of a credit score, a debt amount, a credit segmentation, or a pre-approved loan amount.

14 . The computer-implemented method of claim 11 ,

wherein the recommendation to the graphical user interface of the merchant includes reducing the vehicle inventory of the one or more vehicles, adjusting an offer price to purchasers, or trading the one or more vehicles with other one or more vehicles from other merchants.

15 . The computer-implemented method of claim 11 ,

wherein the transaction data includes data indicating an available inventory of merchants in a predetermined area associated with the quantity of the one or more vehicles that were purchased or attempted to be purchased.

16 . The computer-implemented method of claim 11 ,

wherein determining the desirability value for each vehicle of the vehicle inventory of the merchant is further based on a number of purchasable vehicles purchased in a predetermined historical period.

17 . The computer-implemented method of claim 11 ,

wherein determining the desirability value for each vehicle of the vehicle inventory of the merchant further includes learning relationships between demographics of purchasers.

18 . The computer-implemented method of claim 11 ,

wherein the one or more characteristics of the one or more vehicles includes at least one of a mileage, a total price, a monthly payment, a vehicle category, a body style, a condition, a feature, a fuel economy, a drive type, a specification, a zip code, or a selected one or more merchants of a plurality of merchants.

19 . The computer-implemented method of claim 11 , further comprising:

causing to display, via the graphical user interface, a graphical depiction of available vehicles assigned a highest desirability value and below the threshold desirability value, and the desirability value for the available vehicles.

20 . A system for regulating vehicle stock, the system comprising:

a memory having processor-readable instructions stored therein; and

at least one processor configured to access the memory and execute the processor-readable instructions, which when executed by the at least one processor configures the at least one processor to perform a plurality of functions, including functions for:

receiving, by one or more processors, query data including one or more queries indicative of one or more characteristics of one or more vehicles;

receiving, by the one or more processors, from a database, transaction data including a quantity of the one or more vehicles that were purchased or attempted to be purchased;

monitoring, by the one or more processors, vehicle inventory of a merchant;

determining, by the one or more processors, based on the query data and the transaction data, and using at least one trained machine learning model, a desirability value for each vehicle of the vehicle inventory of the merchant;

assigning, by the one or more processors, a likelihood value for each vehicle of the vehicle inventory of the merchant, wherein the likelihood value is a likelihood that each vehicle of the vehicle inventory of the merchant will be purchased based on one or more purchaser preferences and the desirability value in comparison to a threshold desirability value;

transmitting, by the one or more processors, a recommendation to a graphical user interface of the merchant, the recommendation based on a vehicle having a highest likelihood value and a desirability value below the threshold desirability value;

receiving, by the one or more processors, additional transaction data from a purchaser info database, the additional transaction data including whether a vehicle purchaser purchased or failed to purchase a vehicle of the vehicle inventory of the merchant; and

transmitting, by the one or more processors, a second corresponding recommendation to the graphical user interface of the merchant, the second corresponding recommendation dynamically determined based on the at least one trained machine learning model determining an updated desirability value for an updated vehicle inventory based on the additional transaction data.