Utilizing machine learning models to generate interactive digital text threads with personalized agent escalation digital text reply options
The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing machine learning models to determine predicted client intent classifications and/or client-agent escalation classes to generate personalized digital text reply options within an automated interactive digital text thread. For example, disclosed systems utilize the machine learning model to generate predicted client-agent escalation classes and corresponding probabilities. The disclosed systems utilize the predicted client-agent escalation classifications and the escalation class probabilities to generate personalized digital text reply options. Moreover, the disclosed systems can provide personalized digital text reply options to a client device within an automated interactive digital text thread, bypassing the inefficiency of menu options or protocols utilized to guide clients to terminal information.
1. A computer-implemented method comprising:
extracting client features corresponding to a client device participating in an automated interactive digital text thread;
generating, utilizing an agent escalation machine learning model, a plurality of predicted client-agent escalation classes and a plurality of escalation class probabilities from the client features;
selecting a predicted client-agent escalation class from the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities;
providing, for display via the client device, a personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread, wherein the personalized escalation digital text reply option is associated with a client self-service workflow corresponding to the predicted client-agent escalation class; and
executing the client self-service workflow based on receiving an indication of a user interaction with the personalized escalation digital text reply option.
2. The computer-implemented method of claim 1 , wherein extracting the client features comprises extracting historical device activity of a digital account corresponding to the client device, and further comprising generating, utilizing the agent escalation machine learning model, the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities from the historical device activity of the digital account corresponding to the client device.
3. The computer-implemented method of claim 1 , further comprising generating the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing the agent escalation machine learning model trained from a plurality of client-agent escalation class predictions with ground truth client-agent escalation classes.
4. The computer-implemented method of claim 1 , further comprising generating the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing one or more of a neural network, a random forest model, or a gradient boosted decision tree model.
5. The computer-implemented method of claim 1 , wherein providing the personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread comprises:
generating an escalation ranking of the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities; and
selecting the personalized escalation digital text reply option according to the escalation ranking.
6. The computer-implemented method of claim 1 , further comprising: providing, for display via the client device, an additional personalized intent digital classification option corresponding to an additional predicted client intent classification via the automated interactive digital text thread.
7. The computer-implemented method of claim 1 , wherein executing the client self-service workflow based on receiving the indication of the user interaction with the personalized escalation digital text reply option comprises:
executing a query corresponding to the predicted client-agent escalation class to identify a query response; and
providing, for display, via the client device, a digital text reply comprising the query response.
8. A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause a computer system to:
extract client features corresponding to a client device participating in an automated interactive digital text thread;
generate, utilizing an agent escalation machine learning model, a plurality of predicted client-agent escalation classes and a plurality of escalation class probabilities from the client features;
select a predicted client-agent escalation class from the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities;
provide, for display via the client device, a personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread, wherein the personalized escalation digital text reply option is associated with a client self-service workflow corresponding to the predicted client-agent escalation class; and
execute the client self-service workflow based on receiving an indication of a user interaction with the personalized escalation digital text reply option.
9. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to:
extract the client features by extracting historical device activity of a digital account corresponding to the client device, and
generate, utilizing the agent escalation machine learning model, the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities from the historical device activity of the digital account corresponding to the client device.
10. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to generate the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing the agent escalation machine learning model trained from a plurality of client-agent escalation class predictions with ground truth client-agent escalation classes.
11. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to generate the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing one or more of a neural network, a random forest model, or a gradient boosted decision tree model.
12. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to provide the personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread by:
generating an escalation ranking of the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities; and
selecting the personalized escalation digital text reply option according to the escalation ranking.
13. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to provide, for display via the client device, an additional personalized intent digital classification option corresponding to an additional predicted client intent classification via the automated interactive digital text thread.
14. The non-transitory computer-readable medium of claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to execute the client self-service workflow based on receiving the indication of the user interaction with the personalized escalation digital text reply option by:
executing a query corresponding to the predicted client-agent escalation class to identify a query response; and
providing, for display, via the client device, a digital text reply comprising the query response.
15. A system comprising:
at least one processor; and
at least one non-transitory computer-readable storage medium storing instructions that, when executed by the at least one processor, cause the system to:
extract client features corresponding to a client device participating in an automated interactive digital text thread;
generate, utilizing an agent escalation machine learning model, a plurality of predicted client-agent escalation classes and a plurality of escalation class probabilities from the client features;
select a predicted client-agent escalation class from the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities;
provide, for display via the client device, a personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread, wherein the personalized escalation digital text reply option is associated with a client self-service workflow corresponding to the predicted client-agent escalation class; and
execute the client self-service workflow based on receiving an indication of a user interaction with the personalized escalation digital text reply option.
16. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to:
extract the client features by extracting historical device activity of a digital account corresponding to the client device, and
generate, utilizing the agent escalation machine learning model, the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities from the historical device activity of the digital account corresponding to the client device.
17. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing the agent escalation machine learning model trained from a plurality of client-agent escalation class predictions with ground truth client-agent escalation classes.
18. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to generate the plurality of predicted client-agent escalation classes and the plurality of escalation class probabilities utilizing one or more of a neural network, a random forest model, or a gradient boosted decision tree model.
19. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to provide the personalized escalation digital text reply option corresponding to the predicted client-agent escalation class via the automated interactive digital text thread by:
generating an escalation ranking of the plurality of predicted client-agent escalation classes utilizing the plurality of escalation class probabilities; and
selecting the personalized escalation digital text reply option according to the escalation ranking.
20. The system of claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to provide, for display via the client device, an additional personalized intent digital classification option corresponding to an additional predicted client intent classification via the automated interactive digital text thread.