Systems and methods for generating dynamic models based on trigger events
Embodiments of a system may comprise databases and a processor that accesses a first database and verifies that each of the plurality of client records conforms to a set of formatting guidelines and filters the records in the first database into a vehicle data subfile and a client data subfile, monitors the records in the vehicle and client data subfiles for a trigger event, generates one or more dynamic models for determining a likelihood that a client will perform an action based on the trigger event, applies the generated dynamic model(s) to generate at least one score value associated with the record including the trigger event, and generates a notification including one or more of the generated at least one score value, the one or more trigger event records, and information associated with the one or more trigger event records for transmission to a user.
1. A system comprising:
a client database comprising records relating to vehicles and clients, wherein each record includes information relating to at least one vehicle associated with a respective client;
a hardware processor configured to execute computer-executable instructions to:
apply a first data filter to each of the records to generate a vehicle data subfile comprising records for each vehicle associated with one of the clients having a record in the client database;
apply a second data filter to the records to generate a client data subfile comprising information for each of the clients having a record in the client database;
monitor the vehicle data subfile and the client data subfile for one or more trigger event records, wherein each of the one or more trigger event records identifies one or more of: a vehicle event or a client event;
generate a first dynamic model for determining one or more score values that indicate a predicted likelihood that a client associated with the one or more trigger event records will perform a particular action, based at least in part on the one or more trigger event records;
apply the first dynamic model to generate a first score value associated with a first trigger event record; and
generate a notification including one or more of: the first score value, the first trigger event record, or information associated with the first trigger event record; and
an electronic input/output communication interface configured to receive the records from a user device and transmit instructions for rendering a user interface providing the notification on the user device.
2. The system of claim 1 , wherein the hardware processor is configured to execute further computer-executable instructions to:
verify that each record conforms to a set of formatting guidelines by analyzing the records to identify that each record includes one or more expected fields or an expected identifier; and
update or remove any record that does not include the one or more expected fields or the expected identifier.
3. The system of claim 1 , wherein the information in the client data subfile comprises one or more of: client contact information, client life events information, client purchase behavior information, or client loan information.
4. The system of claim 1 , wherein each of the records in the vehicle data subfile comprises one or more of: vehicle information, garage loyalty information, vehicle equity information, or vehicle service information.
5. The system of claim 1 , wherein the vehicle event comprises one or more of: an accident, a total loss of a vehicle, a theft of a vehicle, a vehicle title change, a vehicle equity change, a garage loyalty change, or a vehicle service event.
6. The system of claim 1 , wherein the client event comprises one or more of: a change in a client shopping model, a particular client purchase event, or a life event.
7. The system of claim 1 , wherein performing the particular action comprises acquiring a vehicle with which a client is not currently associated.
8. The system of claim 1 , wherein the one or more score values comprises one or more of: a loyalty propensity value that predicts a likelihood of the client being loyal to a make of vehicle with which the client is already associated, or a buy propensity value that predicts a likelihood of the client acquiring a vehicle with which the client is not already associated.
9. A computer-implemented method comprising:
accessing a client database comprising records relating to vehicles and clients, wherein each record includes information relating to at least one vehicle associated with a respective client;
applying a first data filter to each of the records to generate a vehicle data subfile comprising records for each vehicle associated with one of the clients having a record in the client database;
applying a second data filter to the records to generate a client data subfile comprising information for each of the clients having a record in the client database;
monitoring the vehicle data subfile and the client data subfile for one or more trigger event records, wherein each of the one or more trigger event records identifies one or more of: a vehicle event or a client event;
generating a first dynamic model for determining one or more score values that indicate a predicted likelihood that a client associated with the one or more trigger event records will perform a particular action, based at least in part on the one or more trigger event records;
applying the first dynamic model to generate a first score value associated with a first trigger event record;
generating a notification including one or more of: the first score value, the first trigger event record, or information associated with the first trigger event record; and
transmitting the notification to a user device, the notification including instructions for rendering a user interface providing the notification on the user device.
10. The computer-implemented method of claim 9 , further comprising:
verifying that each record conforms to a set of formatting guidelines by analyzing the records to identify that each record includes one or more expected fields or an expected identifier; and
updating or removing any record that does not include the one or more expected fields or the expected identifier.
11. The computer-implemented method of claim 9 , wherein the information in the client data subfile comprises one or more of: client contact information, client life events information, client purchase behavior information, or client loan information, and wherein each of the records in the vehicle data subfile comprises one or more of: vehicle information, garage loyalty information, vehicle equity information, vehicle service information.
12. The computer-implemented method of claim 9 , wherein the vehicle event comprises one or more of: an accident, a total loss of a vehicle, a theft of a vehicle, a vehicle title change, a vehicle equity change, a garage loyalty change, or a vehicle service event.
13. The computer-implemented method of claim 9 , wherein the client event comprises one or more of: a change in a client shopping model, a particular client purchase event, or a life event.
14. The computer-implemented method of claim 9 , wherein performing the particular action comprises acquiring a vehicle with which the client is not currently associated.
15. The computer-implemented method of claim 9 , wherein the one or more score values comprise one or more of: a loyalty propensity value that predicts a likelihood of the client being loyal to a make of vehicle with which the client is already associated, or a buy propensity value that predicts a likelihood of the client acquiring a vehicle with which the client is not already associated.
16. A non-transitory computer storage medium storing computer-executable instructions that, when executed by a processor, cause the processor to at least:
access a client database comprising records relating to vehicles and clients, wherein each record includes information relating to at least one vehicle associated with a respective client;
apply a first data filter to each of the records to generate a vehicle data subfile comprising records for each vehicle associated with one of the clients having a record in the client database;
apply a second data filter to the records to generate a client data subfile comprising information for each of the clients having a record in the client database;
monitor the vehicle data subfile and the client data subfile for one or more trigger event records, wherein each of the one or more trigger event records identifies one or more of: a vehicle event or a client event;
generate a first dynamic model for determining one or more score values that indicate a predicted likelihood that a client associated with the one or more trigger event records will perform a particular action, based at least in part on the one or more trigger event records;
apply the first dynamic model to generate a first score value associated with a first trigger event record;
generate a notification including one or more of: the first score value, the first trigger event record, or information associated with the first trigger event record; and
transmit the notification to a user device, the notification including instructions for rendering a user interface providing the notification on the user device.
17. The non-transitory computer storage medium of claim 16 , storing further computer-executable instructions that, when executed by the processor, cause the processor to at least:
verify that each record conforms to a set of formatting guidelines by analyzing the records to identify that each record includes one or more expected fields or an expected identifier; and
update or remove any record that does not include the one or more expected fields or the expected identifier.
18. The non-transitory computer storage medium of claim 16 , wherein the information in the client data subfile comprises one or more of: client contact information, client life events information, client purchase behavior information, or client loan information, and wherein each of the records in the vehicle data subfile comprises one or more of: vehicle information, garage loyalty information, vehicle equity information, or vehicle service information.
19. The non-transitory computer storage medium of claim 16 , wherein the vehicle event comprises one or more of: an accident, a total loss of a vehicle, a theft of a vehicle, a vehicle title change, a vehicle equity change, a garage loyalty change, or a vehicle service event, and wherein the client event comprises one or more of: a change in a client shopping model, a particular client purchase event, or a life event.
20. The non-transitory computer storage medium of claim 16 , wherein the one or more score values comprises a loyalty propensity value that predicts a likelihood of the client being loyal to a make of vehicle with which the client is already associated, or a buy propensity value that predicts a likelihood of the client acquiring a vehicle with which the client is not already associated.