Next best action method and system

• US 9,420,100 B2
• Filed: 07/26/2013
• Issued: 08/16/2016
• Est. Priority Date: 07/26/2013
• Status: Expired

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: Bellini, Davide Guglielmo, Rizzo, Danilo, Maga, Matteo
• Primary Examiner(s): AL AUBAIDI, RASHA S
• Application Number:
  US13/952,224
• Publication Number:
  US 20150030151A1
• Time in Patent Office: 1,117 Days
• Field of Search: 37926501-26502, 37926505-26506, 705/14.1, 705/14.13, 705/14.42, 705 726- 729, 705 737- 739
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: H04M 3/5175 Call or contact centers supervision arrangements

Abstract

The Next Best Action (NBA) management system and method is a real time decision solution usable in multiple industries, including Telco industry. The NBA system identify offers for call agents to pitch to customers, calculate and allow call center organizations and agents to view performance analytics, including an Inbound Campaign Agent Performance Index (ICAPI) for each agent. The NBA system uses usage oriented key performance indicators (KPIs) and sales oriented KPIs to add a weighted portion of the usage oriented KPIs and the sales oriented KPIs to calculate the ICAPI for each agent.

Claims

1. A Next Best Action (NBA) management system comprising:

a channel to couple a call agent to the NBA management system; and

a memory coupled to a processor, the memory comprising;

a NBA data mart that stores data about users, offers, business rules and customer analytics that are used by the NBA management system to calculate offers, wherein the data is extracted from external sources and transformed into flattened data structures in the data mart including tables used to read information when making an offer decision, the data mart further comprising;

a staging area that collects the data from source systems;

a batch area to perform extract, transform and load (ETL) processes on the data and to elaborate data structures within the batch area including an analytic Customer Analytic Record (CAR) table that comprises one single record per customer; and

a real-time area where applications read and store calculated decisions, the real-time area comprising a plurality of data structures including a decision CAR table that comprises one or more columns of key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs, wherein data within the decision CAR table comprises field data mapped from the analytic CAR table of the batch area;

a treatment eligibility component executable by the processor to;

determine based on the Usage oriented KPIs, the Sales oriented KPIs or both, a customer treatment including development treatment, retention treatment, education treatment, next best offer, or some combination thereof, and communicate the customer treatment to a call agent via the channel and an end user application, the end user application comprising a user interface, during a customer call with the call agent;

an offer prioritization component executable by the processor to;

identify a priority score for an offer by multiplying a probability of a positive response to the offer by one or more values, wherein the processor calculates the probability of a positive response using adaptive models to calculate a propensity for each offer for each customer and the one or more values multiplied by the probability includes one or more of;

a value based on a projected financial value of an acceptance of the offer by the customer;

a customer strategy weight based on a selected strategy for the customer;

an offer weight used to up-weight or down-weight individual offers based on relevance of an offer to the customer, and

a context weight used to up-weight the offer based on information obtained during the customer call; and

communicate the priority score for the offer to the call agent via the channel and the end user application;

an agent Performance component executable by the processor to;

receive, into the memory, performance results for the call agent used to calculate the Usage oriented KPIs and the Sales oriented KPIs; and

calculate an Inbound Campaign Agent Performance Index (ICAPI) for each call agent using the Usage oriented KPIs and the Sales oriented KPIs by adding a weighted portion of the Usage oriented KPIs and the Sales oriented KPIs;

the end user application comprising the user interface executable by the processor to;

display for each call agent the ICAPI as a progression bar on the user interface; and

display to the call agent during the customer call, a customer response selection indicator where a selectable customer preference for an offer includes choices of accept, decline, and the hold the offer for the customer'"'"'s consideration, wherein the customer response selection indicator is selectable by clicking on the user interface presented to the call agent.

2. The system of claim 1, wherein the usage oriented KPIs include:

usage rate percentage equal to a number of NBA interactions divided by a total call center customer calls;

Pitching Rate percentage equal to a number of NBA recorded interactions divided by a number of NBA eligible calls; and

Handling Efficiency percentage equal to a number of NBA optimal handling time interactions divided by number of NBA total handling time.

3. The system of claim 2, wherein the NBA recorded interactions include the number of NBA calls eligible with at least one customer response;

wherein the Sales oriented KPIs include;

Negotiation Efficiency percentage equal to the number of NBA recorded interactions divided into a number of accepted offers added to a number of saved offers multiplied by a saved offers coefficient; and

Generated Value percentage equal to number of optimal projected sales value target divided by a number of projected sales value.

4. The system of claim 1, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

5. The system of claim 1, wherein the probability of a positive response to the offer is modified by a Value versus Volume “

n”

lever that determines whether to place emphasis on Likelihood of acceptance (volume) or financial benefit (value).

6. The system of claim 5, wherein the processor uses an adaptive model per each offer, wherein the strategy weight determines the customer treatment based on tenure of the customer, and spending of the customer.

7. The system of claim 6, wherein the projected financial value for the customer accepting the offer is equal to [(Future annual revenue per user (ARPU)−

Current ARPU)+Monthly Fee]*Estimated Offer Life Time+Activation Cost where Future ARPU=Sum of {[(Last Month Usage−

Offer Bundle)*cost out of bundle]} for traffic types impacted by the offer, and Current ARPU=Sum of {Last month Revenue} for traffic types impacted by the offer.

8. The method of claim 1, wherein the probability of a positive response to the offer is modified by a Value versus Volume “

n”

lever that determines whether to place emphasis on Likelihood of acceptance (volume) or financial benefit (value).

9. The method of claim 8, wherein the processor uses an adaptive model per each offer, wherein the strategy weight determines the customer treatment based on tenure of the customer, and spending of the customer.

10. The method of claim 9, wherein the projected financial value for the customer accepting the offer is equal to [(Future annual revenue per user (ARPU)−

Current ARPU)+Monthly Fee]*Estimated Offer Life Time+Activation Cost where Future ARPU=Sum of {[(Last Month Usage−

Offer Bundle)*cost out of bundle]} for traffic types impacted by the offer, and Current ARPU=Sum of {Last month Revenue} for traffic types impacted by the offer.

11. A Next Best Action (NBA) management method comprising:

storing data in a NBA data mart coupled to a processor and a channel, the data including information about users, offers, business rules and customer analytics that are used by the NBA management system to calculate offers, wherein the data is extracted from external sources and transformed into flattened data structures in the data mart including tables used to read information when making an offer decision, and wherein;

a staging area of the data mart collects the data from source systems;

a batch area in the data mart performs extract, transform and load (ETL) processes on the data and elaborates data structures within the batch area including an analytic Customer Analytic Record (CAR) table that comprises one single record per customer; and

applications read and store calculated decisions in a real-time area of the data mart, the real-time area comprising a plurality of data structures including a decision CAR table that comprises one or more columns of key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs, wherein data within the decision CAR table comprises field data mapped from the analytic CAR table of the batch area;

determining by the processor executing a treatment eligibility component, based on the usage oriented KPIs, the sales oriented KPIs or both, a customer treatment including development treatment, retention treatment, education treatment, next best offer, or some combination thereof, and communicating the customer treatment to a call agent via the channel and an end user application, the end user application comprising a user interface executable on the processor, during a customer call;

identifying by the processor executing an offer prioritization component, a priority score for an offer by multiplying a probability of a positive response to the offer by one or more values, wherein the processor calculates the probability of a positive response using adaptive models to calculate a propensity for each offer for each customer and the one or more values multiplied by the probability includes one or more of;

a value based on a projected financial value of an acceptance of the offer by the customer;

a customer strategy weight based on a selected strategy for the customer;

an offer weight used to up-weight or down-weight individual offers based on relevance of an offer to a customer; and

a context weight used to up-weight the offer based on information obtained during a customer call;

communicating the priority score for the offer to the call agent via the channel and the end user application;

receiving, into the memory, by the processor executing an agent performance component, performance results for the call agent used to calculate the Usage oriented KPIs and the Sales oriented KPIs;

calculating an Inbound Campaign Agent Performance Index (ICAPI) for each call agent using the Usage oriented KPIs and the Sales oriented KPIs by adding a weighted portion of the Usage oriented KPIs and the Sales oriented KPIs;

displaying, using the user interface, for each call agent the ICAPI as a progression bar on the user interface, the ICAPI communicated to the call agent via the channel; and

displaying to the call agent during the customer call, a customer response selection indicator where a selectable customer preference for an offer includes choices of accept, decline, and the hold the offer for the customer'"'"'s consideration, wherein the customer response selection indicator is selectable by clicking on the user interface presented to the call agent.

12. The method of claim 11, wherein the usage oriented KPIs include:

usage rate percentage equal to a number of NBA interactions divided by a total call center customer calls;

pitching Rate percentage equal to a number of NBA recorded interactions divided by a number of NBA eligible calls; and

Handling Efficiency percentage equal to a number of NBA optimal handling time interactions divided by number of NBA total handling time.

13. The method of claim 12, wherein the NBA recorded interactions include the number of NBA calls eligible with at least one customer response;

wherein the Sales oriented KPIs include;

Negotiation Efficiency percentage equal to the number of NBA recorded interactions divided into a number of accepted offers added to a number of saved offers multiplied by a saved offers coefficient; and

Generated Value percentage equal to number of optimal projected sales value target divided by a number of projected sales value.

14. The method of claim 11, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

15. A Next Best Action (NBA) management product comprising:

a non-transitory computer readable medium with processor executable instructions stored thereon, wherein the instructions when executed by a processor cause the processor to;

store data in a NBA data mart coupled to a processor and a channel, the data including information about users, offers, business rules and customer analytics that are used by the NBA management system to calculate offers, wherein the data is extracted from external sources and transformed into flattened data structures in the data mart including tables used to read information when making an offer decision, and wherein;

a staging area of the data mart collects the data from source systems;

a batch area in the data mart performs extract, transform and load (ETL) processes on the data and elaborates data structures within the batch area including an analytic Customer Analytic Record (CAR) table that comprises one single record per customer; and

applications read and store calculated decisions in a real-time area of the data mart, the real-time area comprising a plurality of data structures including a decision CAR table that comprises one or more columns of key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs, wherein data within the decision CAR table comprises field data mapped from the analytic CAR table of the batch area;

determine, based on the Usage oriented KPIs, the Sales oriented KPIs or both, a customer treatment including development treatment, retention treatment, education treatment, next best offer, or some combination thereof, and communicate the customer treatment to a call agent via the channel and an end user application, the end user application comprising a user interface executable on the processor, during a customer call;

identify a priority score for an offer by multiplying a probability of a positive response to the offer by one or more values, wherein the processor calculates the probability of a positive response using adaptive models to calculate a propensity for each offer for each customer and the one or more values multiplied by the probability includes one or more of;

a value based on a projected financial value of an acceptance of the offer by the customer;

a customer strategy weight based on a selected strategy for the customer;

an offer weight used to up-weight or down-weight individual offers based on relevance of an offer to a customer; and

a context weight used to up-weight the offer based on information obtained during a customer call;

communicate the priority score for the offer to the call agent via the channel and the end user application;

receive, into the memory, performance results for the call agent used to calculate the Usage oriented KPIs and the Sales oriented KPIs;

calculate an Inbound Campaign Agent Performance Index (ICAPI) for each call agent using the Usage oriented KPIs and the Sales oriented KPIs by adding a weighted portion of the Usage oriented KPIs and the Sales oriented KPIs;

display, using the user interface, for each call agent the ICAPI as a progression bar on the user interface, the ICAPI communicated to the call agent via the channel; and

display to the call agent during a customer call a customer response selection indicator where a selectable customer preference for an offer includes choices of accept, decline, and the hold the offer for a customer'"'"'s consideration, wherein the customer response selection indicator is selectable by clicking on the user interface presented to the call agent.

16. The product of claim 15, wherein the usage oriented KPIs include:

usage rate percentage equal to a number of NBA interactions divided by a total call center customer calls;

Pitching Rate percentage equal to a number of NBA recorded interactions divided by a number of NBA eligible calls; and

Handling Efficiency percentage equal to a number of NBA optimal handling time interactions divided by number of NBA total handling time.

17. The product of claim 16, wherein the NBA recorded interactions include the number of NBA calls eligible with at least one customer response;

wherein the Sales oriented KPIs include;

Negotiation Efficiency percentage equal to the number of NBA recorded interactions divided into a number of accepted offers added to a number of saved offers multiplied by a saved offers coefficient; and

Generated Value percentage equal to number of optimal projected sales value target divided by a number of projected sales value.

18. The product of claim 15, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

19. The product of claim 15, wherein the probability of a positive response to the offer is modified by a Value versus Volume “

n”

lever that determines whether to place emphasis on Likelihood of acceptance (volume) or financial benefit (value).

20. The product of claim 19, wherein the processor uses an adaptive model per each offer, wherein the strategy weight determines the customer treatment based on tenure of the customer, and spending of the customer.

21. The product of claim 20, wherein the projected financial value for the customer accepting the offer is equal to [(Future annual revenue per user (ARPU)−

Current ARPU)+Monthly Fee]*Estimated Offer Life Time+Activation Cost where Future ARPU=Sum of {[(Last Month Usage−

Offer Bundle)*cost out of bundle]} for traffic types impacted by the offer, and Current ARPU=Sum of {Last month Revenue} for traffic types impacted by the offer.