US-20260127537-A1 - DATA-DRIVEN PRIORITIZED CUSTOMER RETENTION

Abstract

A computer-implemented method that receives a plurality of customer content and calculates a sentiment score and a weightage score for at least a subset of the plurality of customer content. The method may further include generating a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content. In embodiments, the method further determines an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction and notifies user of the attrition risk score and the first ranked set of recommended action items to be executed. The method may further perform a first action of the first ranked set of recommended action items.

Inventors

• Gail Camille Guerrero
• Geetha R. Subramanyam
• Omar ODIBAT
• Preethi Ravishankar Sulkunte
• Viswanatha Krishnamurthy

Assignees

• KYNDRYL, INC.

Dates

Publication Date

20260507

Application Date

20241105

Claims (20)

1. 1 . A computer-implemented method, comprising: receiving, by a processor set, a plurality of customer content; calculating, by the processor set using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generating, by the processor set using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determining, by the processor set using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notifying a user of the attrition risk score and the first ranked set of recommended action items to be executed; and performing, by the processor, a first action of the first ranked set of recommended action items.
2. 2 . The computer-implemented method of claim 1 , further comprising generating a current customer state and the first ranked set of recommended action items to be executed in response to a triggering event.
3. 3 . The computer-implemented method of claim 2 , wherein the triggering event comprises an execution of at least one action item of the first ranked set of recommended action.
4. 4 . The computer-implemented method of claim 1 , wherein the attrition risk score and the first ranked set of recommended action items are determined based on a combination of the plurality of customer content, the sentiment scores, and the weightage scores.
5. 5 . The computer-implemented method of claim 1 , wherein the plurality of customer content comprises customer communications, demographics data, platform analytics, and historical recommendations.
6. 6 . The computer-implemented method of claim 1 , further comprising: performing a root cause analysis in response to the attrition risk score exceeding a threshold; and recommending a second ranked set of recommended action items based on the root cause analysis.
7. 7 . The computer-implemented method of claim 1 , further comprising receiving feedback data and second data inputs related to an entity based on a customer response to the first ranked set of recommendation actions items.
8. 8 . The computer-implemented method of claim 1 , wherein the first action of the first ranked set of recommended action items comprises a highest ranking action.
9. 9 . A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive a plurality of customer content; calculate, using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generate, using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determine, using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notify a user of the attrition risk score and the first ranked set of recommended action items to be executed; and perform a first action of the first ranked set of recommended action items.
10. 10 . The computer program product of claim 9 , wherein the program instructions are further executable to generate a current customer state and the first ranked set of recommended action items to be executed in response to a triggering event.
11. 11 . The computer program product of claim 10 , wherein the triggering event comprises an execution of at least one action item of the first ranked set of recommended action.
12. 12 . The computer program product of claim 9 , wherein the attrition risk score and the first ranked set of recommended action items are determined based on a combination of the plurality of customer content, the sentiment scores, and the weightage scores.
13. 13 . The computer program product of claim 9 , wherein the plurality of customer content comprises customer communications, demographics data, platform analytics, and historical recommendations.
14. 14 . The computer program product of claim 9 , wherein the program instructions are further executable to: perform a root cause analysis in response to the attrition risk score exceeding a threshold; and recommend a second ranked set of recommended action items based on the root cause analysis.
15. 15 . The computer program product of claim 9 , wherein the program instructions are further executable to receive feedback data and second data inputs related to an entity based on a customer response to the first ranked set of recommendation actions items.
16. 16 . A system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive a plurality of customer content; calculate, using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generate, using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determine, using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notify a user of the attrition risk score and the first ranked set of recommended action items to be executed; and perform a first action of the first ranked set of recommended action items.
17. 17 . The system of claim 16 , wherein the program instructions are further executable to generate a current customer state and the first ranked set of recommended action items be executed in response to a triggering event, and wherein the triggering event comprises an execution of at least one action item of the first ranked set of recommended action.
18. 18 . The system of claim 16 , wherein the attrition risk score and the first ranked set of recommended action items are determined based on a combination of the plurality of customer content, the sentiment scores, and the weightage scores.
19. 19 . The system of claim 16 , wherein the program instructions are further executable to: perform a root cause analysis in response to the attrition risk score exceeding a threshold; and recommend a second ranked set of recommended action items based on the root cause analysis.
20. 20 . The system of claim 16 , wherein the program instructions are further executable to receive feedback data and second data inputs related to an entity based on a customer response to the first ranked set of recommendation actions items.

Description

BACKGROUND Aspects of the present invention relate generally to determining attrition risk for customers of cloud resource management services and determining the root cause for customer attrition. Customer attrition is the loss of clients or customers and an indicator of whether the company is performing well and providing value to customers. SUMMARY In a first aspect of the present invention, there is a computer-implemented method including: receiving, by a processor set, a plurality of customer content; calculating, by the processor set using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generating, by the processor set using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determining, by the processor set using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notifying a user of the attrition risk score and the first ranked set of recommended action items to be executed; and performing, by the processor, a first action of the first ranked set of recommended action items. In another aspect of the present invention, there is a computer program product including one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to: receive a plurality of customer content; calculate, using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generate, using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determine, using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notify a user of the attrition risk score and the first ranked set of recommended action items to be executed; and perform a first action of the first ranked set of recommended action items. In another aspect of the present invention, there is a system including a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media. The program instructions are executable to: receive a plurality of customer content; calculate, using a first machine learning algorithm, a sentiment score and a weightage score for at least a subset of the plurality of customer content; generate, using a second machine learning algorithm, a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content; determine, using a third machine learning algorithm, an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction; notify a user of the attrition risk score and the first ranked set of recommended action items to be executed; and perform a first action of the first ranked set of recommended action items. BRIEF DESCRIPTION OF THE DRAWINGS Aspects of the present invention are described in the detailed description which follows, in reference to the noted plurality of drawings by way of non-limiting examples of exemplary embodiments of the present invention. FIG. 1 depicts a cloud computing node according to an embodiment of the present invention. FIG. 2 depicts a cloud computing environment according to an embodiment of the present invention. FIG. 3 depicts abstraction model layers according to an embodiment of the present invention. FIG. 4 shows a block diagram of an exemplary environment in accordance with aspects of the present invention. FIG. 5 shows a flowchart of an exemplary method in accordance with aspects of the present invention. FIG. 6 shows an exemplary flow diagram of an exemplary method in accordance with aspects of the present invention. FIG. 7 shows an exemplary flow diagram of an exemplary method in accordance with aspects of the present invention. FIG. 8 shows an exemplary dashboard display of an exemplary embodiment in accordance with aspects of the present invention. DETAILED DESCRIPTION Aspects of the present invention relate generally to determining attrition risk for customers of cloud resource management services and determining the root cause f