Propensity-driven search results

1. A computing system, comprising:

a network interface configured to receive a request from a user device; and

a processor configured to

train a predictive model to determine a propensity of a user to accept an employment opportunity based on patterns between spending activity and employment opportunities accepted by a plurality of users,

retrieve spending data of a user associated with the user device, the spending data comprising spending activity of the user captured over time, and

predict one or more employment opportunities for that the user will accept from among a pool of employment opportunities via execution of the predictive model, wherein the predictive model identifies a pattern between a product type included in the spending activity of the user included in the spending data and the geographical location data the one or more employment opportunities,

wherein the processor further controls the network interface to transmit information about the one or more employment opportunities to the user device via a content channel between the server and the user device.

2. The computing system of claim 1 , wherein the one or more predicted employment opportunities comprise one or more employment positions listed on at least one of an employment-based website and an employment-based mobile application.

3. The computing system of claim 1 , wherein the processor is further configured to train the predictive model based on one or more of mobile device usage behavior and Internet usage behavior, which are retrieved by the processor via one or more application programming interfaces (APIs).

4. The computing system of claim 1 , wherein the processor is configured to predict an optimal employment opportunity from among multiple employment opportunities based on a neural network which receives the spending data as input, and outputs the predicted optimal employment opportunity.

5. The computing system of claim 4 , wherein the predictive model comprises an algorithm with variables for responsibility, activity creation, value creation, and people involved, and each of the variables comprises a respective weight.

6. The computing system of claim 1 , wherein the processor is configured to iteratively modify the predictive model for the user based on additional spending data of the user retrieved over time.

7. The computing system of claim 1 , wherein the network interface is configured to transmit information about the one or more employment opportunities to the user device via at least one of a webpage, an email, a text message, a page of a mobile application on the user device, and a phone call.

8. The computing system of claim 1 , wherein the processor is further configured to determine an optimal period of time to transmit the information about the one or more employment opportunities to the user device based on time-based attributes within the spending data.

9. A method, comprising:

receiving a request from a user device;

training a predictive model to determine a propensity of a user to response to accept an employment opportunity based on patterns between spending activities and employment opportunities accepted by a plurality of users;

retrieving spending data of a user associated with the user device, the spending data comprising spending activity of the user captured over time;

predicting one or more employment opportunities for that the user will accept from among a pool of employment opportunities via execution of the predictive model, wherein the predictive model identifies a pattern between a product type included in the spending activity of the user included in the spending data and the one or more employment opportunities; and

transmitting information about the one or more employment opportunities to the user device via a content channel between the server and the user device.

10. The method of claim 9 , wherein the one or more employment opportunities comprise one or more employment positions listed on at least one of an employment-based website and an employment-based mobile application.

11. The method of claim 9 , wherein the training further comprises training the predictive model based on one or more of mobile device usage behavior and Internet usage behavior, which are retrieved via one or more application programming interfaces (APIs).

12. The method of claim 9 , wherein the predicting comprises predicting an optimal employment opportunity from among multiple employment opportunities based on a neural network which receives the spending data as input, and outputs the predicted optimal employment opportunity.

13. The method of claim 12 , wherein the predictive model comprises an algorithm with variables for responsibility, activity creation, value creation, and people involved, and each of the variables comprise respective weights.

14. The method of claim 9 , wherein the method further comprises iteratively modifying the predictive model for the user based on additional spending data of the user retrieved over time.

15. The method of claim 9 , wherein the transmitting comprises transmitting information about the one or more employment opportunities to the user device via at least one of a webpage, an email, a text message, a page of a mobile application on the user device, and a phone call.

16. The method of claim 9 , further comprising determining an optimal period of time to transmit the information about the one or more employment opportunities to the user device based on time-based attributes within the spending data.

17. A non-transitory computer-readable medium storing instructions which when executed cause a computer to perform a method comprising:

receiving a request from a user device;

training a predictive model to determine a propensity of a user to response to accept an employment opportunity based on patterns between spending activities and employment opportunities accepted by a plurality of users;

retrieving spending data of a user associated with the user device, the spending data comprising spending activity of the user captured over time;

predicting one or more employment opportunities that the user will accept from among a pool of employment opportunities via execution of the predictive model, wherein the predictive model identifies a pattern between a product type included in the spending activity of the user included in the spending data and the one or more employment opportunities; and

transmitting information about the one or more employment opportunities to the user device via a content channel between the server and the user device.

18. The non-transitory computer-readable medium of claim 17 , wherein the one or more employment opportunities comprise one or more employment positions listed on at least one of an employment-based website and an employment-based mobile application.

19. The non-transitory computer-readable medium of claim 17 , wherein the training further comprises training the predictive model based on one or more of mobile device usage behavior and Internet usage behavior, which are retrieved via one or more application programming interfaces (APIs).

20. The non-transitory computer-readable medium of claim 17 , wherein the predictive model comprises an algorithm with variables for responsibility, activity creation, value creation, and people involved, and each of the variables comprise respective weights.