Systems and methods for contextual communication between devices
An artificial intelligence (AI) model, using logistic regression and gradient boosting tree, may determine a priority score for a user. The priority score may be associated with a likelihood of redemption of investment funds of the user by the user. Based on the priority score for the user, a server may prioritize transmission of a communication message to the user via one of a plurality of communication channels. The communication message may describe why the user should continue with their investment funds without redemption.
1. A computer-implemented method comprising:
executing, by a computer, an artificial intelligence model to output a priority score representative of likelihood that a user will redeem an asset within a time period at an amount higher than a previous time period by inputting user data for the user, the artificial intelligence model classifying the user into a first group when the priority score satisfies a threshold value or into a second group when the priority score does not satisfy the threshold value,
wherein the artificial intelligence model is re-trained to generate an updated dataset for a plurality of user records to detect a time-period for appropriate interventions;
when the executing step classifies the user into the first group, initiating, by the computer, an outgoing call to a user; and
when the executing step classifies the user into the second group, transmitting, by the computer, an electronic message to a user computer.
2. The computer-implemented method according to claim 1 , further comprising assigning, by the computer, an analyst from a database to the outgoing call.
3. The computer-implemented method according to claim 2 , wherein the database comprises a group of analysts qualified to execute the outgoing call based upon at least one of information related to operation of an automated call delivery system and information regarding capabilities of respective analysts.
4. The computer-implemented method according to claim 3 , wherein the information related to capabilities of the selected analyst is determined based at least in part on an availability of each analyst to execute the outgoing call.
5. The computer-implemented method according to claim 2 , further comprising presenting, by the computer, instructions for the outgoing call on a user interface of an analyst computer of the selected analyst.
6. The computer-implemented method according to claim 1 , further comprising querying, by the computer in the database, a set of prior communication messages associated with the user.
7. The computer-implemented method according to claim 6 , further comprising querying, by the computer in a database, a set of electronic messages associated with a set of users associated with a first event.
8. The computer-implemented method according to claim 7 , further comprising generating, by the computer, an electronic message content that comprises text based on processing of the set of prior communication messages and the set of electronic messages, the text comprising language indicative of a context of a portion of the set of prior communication messages and the set of electronic messages, wherein the transmitted electronic message comprises the electronic message content.
9. The computer-implemented method according to claim 1 , wherein the artificial intelligence model applies logistic regression and gradient boosting decision tree to output the priority score and to classify the user into the high likelihood group or into the low likelihood group.
10. The computer-implemented method according to claim 1 , wherein the artificial intelligence model applies weighted linear regression, gradient boosting regression, and gradient boosting classification to output the priority score and to classify the user into the high likelihood group or into the low likelihood group.
11. A system comprising:
a server comprising instructions stored on a non-transitory computer-readable medium that when executed by a processor:
execute an artificial intelligence model to output a priority score representative of likelihood that a user will redeem an asset within a time period at an amount higher than a previous time period by inputting user data for the user, the artificial intelligence model classifying the user into a first group when the priority score satisfies a threshold value or into a second group when the priority score does not satisfy the threshold value,
wherein the artificial intelligence model is re-trained to generate an updated dataset for a plurality of user records representing effects of specific interventions on a redemption rate to detect a time-period for appropriate interventions;
when the executing step classifies the user into the first group, initiate an outgoing call to a user; and
when the executing step classifies the user into the second group, transmit an electronic message to a user computer.
12. The system according to claim 11 , wherein the processor is further configured to assign an analyst from a database to the outgoing call.
13. The system according to claim 12 , wherein the database comprises a group of analysts qualified to execute the outgoing call based upon at least one of information related to operation of an automated call delivery system and information regarding capabilities of respective analysts.
14. The system according to claim 13 , wherein the analyst is selected from the group of analysts qualified to execute the outgoing call based at least in part on the priority score, information related to the user, attributes of a selected analyst, and the operation of the automated call delivery system.
15. The system according to claim 12 , wherein the processor is further configured to present instructions for the outgoing call on a user interface of an analyst computer of the selected analyst.
16. The system according to claim 13 , wherein the information related to capabilities of the selected analyst is determined based at least in part on an availability of each analyst to execute the outgoing call.
17. The system according to claim 11 , wherein the processor is further configured to query, in a database, a set of prior communication messages associated with the user.
18. The system according to claim 17 , further comprising querying, by the computer in the database, a set of electronic messages associated with a set of users associated with a first event.
19. The system according to claim 18 , wherein the processor is further configured to generate an electronic message content that comprises text based on processing of the set of prior communication messages and the set of electronic messages, the text comprising language indicative of a context of a portion of the set of prior communication messages and the set of electronic messages, wherein the transmitted electronic message comprises the electronic message content.
20. A computer-implemented method comprising:
executing, by a computer, an artificial intelligence model using logistic regression and gradient boosting decision tree to output a priority score representative of likelihood that a user will redeem an asset within a time period at an amount higher than a previous time period by inputting user data for the user, the artificial intelligence model classifying the user into a first group when the priority score satisfies a threshold value or into a second group when the priority score does not satisfy the threshold value,
wherein the artificial intelligence model is re-trained to generate an updated dataset for a plurality of user records to detect a time-period for appropriate interventions;
when the executing step classifies the user into the first group, initiating, by the computer, an outgoing call to a user; and
when the executing step classifies the user into the second group, transmitting, by the computer, an electronic message to a user computer.