Next best action method and system

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.

Description

1500 1600 that provides a layer of information the NBA system communicates to the rules decision engine (RDE). The RDE contains data structures that may be used for different purposes such as support the NBA user interface (UI), store transaction information, and provide data to the NBA to execute the NBA rules, in terms of customer attributes, offers, agent information, maintain the catalogue of NBA offers, and configure the solution.

The NBA Architecture 1400 data mart may include different schemas for content and purpose, including: a staging area that collects data from the source systems that feed the NBA data mart; a batch area where the ETL processes of NBA data mart may perform (execute), and where the elaborations and calculations are applied, such as averages, aggregations, de-normalizations and so on; a utility area that may include data structures that support jobs and processes of the ETL; and a real-time area where the final outcome of the ETL processes may perform and where the real time application reads and stores calculated decisions.

The NBA Architecture 1400 may employ multiple data flows, including: 1) source systems may provide (communicate) and/or make available data in the form of interfaces and/or extracts to the NBA system; 2) the NBA system processes data from the staging tables/views using ETL performing the proper calculations and manipulations; 3) the NBA system refreshes the Usage History and Contact History data, and the NBA system includes the Usage History and Contact History data in the build of the Analytic customer analytic records (CAR); 4) the NBA system uses data from the previous build of the Analytic CAR (e.g., 24 hours or the day prior) (e.g., for efficiency); 5) the NBA system provides the refreshed Analytic CAR as output of the ETL process, containing real-time up to date data; 6) the NBA system may use a field mapping configuration tool to manage which attributes are populated or dropped from the decision CAR, which may be a subset of the analytic CAR. When the NBA system completes and verifies the decision CAR, a proper process (e.g., using a synonym at the database level) may handle the logical switch between a previous period (e.g., 24 hours or the day prior) and the current period (e.g., 24 hours or the current day) (new) table, ensuring that the NBA engine points to the appropriate table (e.g., current day) for operations; 7) the reference and support data may be loaded into the real-time area including an installed base (e.g., including product holding, list of active MSISDN services and promotions), contact history (e.g., including inbound and outbound contacts); product catalogue (e.g., including a list of offers-services-products to be included in the deal, considering also compatibility and eligibility between offers-services-products), and agents (e.g., including lookup information on skills and positions of each call agent).

The NBA data model may include different types of schemas and related data structures, including objects related to ETL process and data, including: customer info; agents' info; product catalogue; elaboration and calculation temporary structures; and ETL process support (e.g., logging, scheduling, execution . . . etc.). The different types of schemas and related data structures may include objects related to application functioning, including: transactions information; offers to be proposed; back-end handling (e.g., logic, access to repository . . . etc.).

The NBA system may use the staging area to collect data from the different source systems that feed the NBA data mart. In one implementation, the NBA system may not use the staging area to perform data elaboration and manipulation, except for the application of basic rejection rules regarding the information records that do not match the agreed features of each interface (e.g., file name/table name; data types of fields; and not null fields not populated).

The NBA system may use the batch area to perform the ETL process of the NBA data mart, where the NBA system may apply elaboration and calculations, such as averages, aggregations, de-normalizations and etc. The NBA system may elaborate data structures, including: the Customer Analytic Record (CAR) table (Analytic CAR).

The NBA data mart may use the Customer Analytic Record (CAR) table data source for NBA logic. The CAR table may contain customer information (e.g., usage, demographic, billing, etc.), which the NBA data mart may store as one single record per customer). The NBA data mart stores usage history of a configurable period of time (e.g., a historical depth of twelve months in order to consider behaviors and trends for the past twelve months). The NBA data mart stores contact history data about outbound campaigns of a configurable period of time (e.g., a historical depth of six months in order to consider past responses and apply related business rules). The NBA system may use one or more adaptive models to determine the configurable period of time of data to use.

The NBA data mart may use the real-time area to provide a data structure to the NBA logic engine (e.g., real-time with adequate performance). The NBA system may create a subset of the analytic CAR on a configurable frequency (e.g., every day or 24 hours) (as final result of ETL process). The decision CAR table contains the cardinality of analytic CAR (e.g., so then the NBA customer base) and one or more of the columns (KPIs) that the NBA logic may use to determine the best offer for the customer. Also, other data structures may be referenced by the NBA processor executable instructions, including: product catalogue, installed base and information for each agent: the information may not require data manipulation but may be imported directly from the staging area; and internal data structures: used directly by the NBA system to record transaction details and handle GUI and other attributes.

FIG. 17 illustrates an example Entity-Relationship diagram 1800 1700 that the NBA system may use to define the NBA system database schema. The subscription entity contains subscription identifier (e.g., Mobile Subscriber Integrated Services Digital Network-Number (MSISDN)), Account codes and Customer codes. The NBA system may consolidate the information at the customer level (e.g., unique view of customer metadata).

Relationships identified by the Entity-Relationship diagram 1800 1700 may include: Subscription—Account: many-to-one based on Account code (an Account may have more than one MSISDN); Account—Customer: many-to-one based on Customer code (a Customer may have more than one Account); Customer—Subscription: one-to-many based on Customer code (a Customer may have more than one MSISDN); Account—Revenue: one-to-one based on Account code; Account—Billing: one-to-one based on Account code; Subscription—Usage History: one-to-one based on MSISDN; Subscription—Statistical Model Scores: one-to-one based on MSISDN; Subscription—Inbound Contact History: one-to-one based on MSISDN (info will be grouped by NBA call reasons and provided at one row per MSISDN); Subscription—Outbound Contact History: one-to-many based on MSISDN (a MSISDN can be included in more than one campaign produced for outbound); Subscription—Active Installed Base Promotion: one-to-many based on MSISDN (a MSISDN may have more than one promotion); Subscription—Active Installed Base Service: one-to-many based on MSISDN (a MSISDN may have more than one service); Subscription—Top-up: one-to-one based on MSISDN; and Agents' info is not related with other categories.

FIG. 18 illustrates an example Entity-Relationship diagram real-time area 1800. The CAR decision contains the MSISDN as primary key. Relations identified: CAR decision—Active Installed Base Promotion: one-to-many based on MSISDN (a MSISDN may have more than one promotion); CAR decision—Active Installed Base Service: one-to-many based on MSISDN (a MSISDN may have more than one service); CAR decision—Inbound Contact History: one-to-many based on MSISDN (a MSISDN may have been contacted more than once with NBA in the past); CAR decision—Outbound Contact History: one-to-many based on MSISDN (a MSISDN can be included in more than one campaign produced for outbound); and information for agents may not be related with other categories and may be referenced directly as needed (e.g., by the logic engine).

FIG. 19 illustrates a general computer system 1900 which may represent the NBA system, one or more service provider servers, or any of the other computing devices referenced herein. The computer system may include a set of instructions that may be executed to cause the computer system to perform any one or more of the methods or computer based functions disclosed herein. The computer system may operate as a standalone device or may be connected (e.g., using a network) to other computer systems or peripheral devices.

In a networked deployment, the computer system may operate in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system may also be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular embodiment, the computer system may be implemented using electronic devices that provide voice, video or data communication. Further, while a single computer system may be illustrated, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

As illustrated in FIG. 19, the computer system may include a processor 1902, such as, a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor 1902 may be a component in a variety of systems. For example, the processor may be part of a standard personal computer or a workstation. The processor may be one or more general processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developed devices for analyzing and processing data. The processor may implement a software program, such as code generated manually (i.e., programmed).

The computer system may include a memory 1904 that can communicate via a bus. The memory may be a main memory, a static memory, or a dynamic memory. The memory may include, but may not be limited to computer readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one case, the memory may include a cache or random access memory for the processor. Alternatively or in addition, the memory may be separate from the processor, such as a cache memory of a processor, the system memory, or other memory. The memory may be an external storage device or database for storing data. Examples may include a hard drive, compact disc (“CD”), digital video disc (“DVD”), memory card, memory stick, floppy disc, universal serial bus (“USB”) memory device, or any other device operative to store data. The memory may be operable to store instructions 1910 executable by the processor. The functions, acts or tasks illustrated in the figures or described herein may be performed by the programmed processor executing the instructions stored in the memory. The functions, acts or tasks may be independent of the particular type of instructions set, storage media, processor or processing strategy and may be performed by software, hardware, integrated circuits, firm-ware, micro-code and the like, operating alone or in combination. Likewise, processing strategies may include multiprocessing, multitasking, parallel processing and the like.

The computer system may further include a display 1912, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display may act as an interface for the user to see the functioning of the processor, or specifically as an interface with the software stored in the memory or in the drive unit.

Additionally, the computer system may include an input device 1914 configured to allow a user to interact with any of the components of system. The input device may be a number pad, a keyboard, or a cursor control device, such as a mouse, or a joystick, touch screen display, remote control or any other device operative to interact with the system.

The computer system may also include a disk or optical drive unit 1908. The disk drive unit may include a computer-readable medium 1906 in which one or more sets of instructions 1910, e.g. software, can be embedded. Further, the instructions may perform one or more of the methods or logic as described herein. The instructions may reside completely, or at least partially, within the memory and/or within the processor during execution by the computer system. The memory and the processor also may include computer-readable media as discussed above.

The present disclosure contemplates a computer-readable medium that includes instructions or receives and executes instructions responsive to a propagated signal; so that a device connected to a network 1916 may communicate voice, video, audio, images or any other data over the network. Further, the instructions may be transmitted or received over the network via a communication interface 1918. The communication interface may be a part of the processor or may be a separate component. The communication interface 1918 may be created in software or may be a physical connection in hardware. The communication interface 1918 may be configured to connect with a network, external media, the display, or any other components in system 2400 1900, or combinations thereof. The connection with the network may be a physical connection, such as a wired Ethernet connection or may be established wirelessly as discussed below. Likewise, the additional connections with other components of the system may be physical connections or may be established wirelessly. In the case of a service provider server, the service provider server may communicate with users 120A-N through the communication interface.

The network 1916 may include wired networks, wireless networks, or combinations thereof. The wireless network may be a cellular telephone network, an 802.11, 802.16, 802.20, or WiMax network. Further, the network may be a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and may utilize a variety of networking protocols now available or later developed including, but not limited to TCP/IP based networking protocols.

The computer-readable medium may be a single medium, or the computer-readable medium may be a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” may also include any medium that may be capable of storing, encoding or carrying a set of instructions for execution by a processor or that may cause a computer system to perform any one or more of the methods or operations disclosed herein.

The computer-readable medium may include a solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. The computer-readable medium also may be a random access memory or other volatile re-writable memory. Additionally, the computer-readable medium may include a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals such as a signal communicated over a transmission medium. A digital file attachment to an e-mail or other self-contained information archive or set of archives may be considered a distribution medium that may be a tangible storage medium. Accordingly, the disclosure may be considered to include any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions may be stored.

Alternatively or in addition, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, may be constructed to implement one or more of the methods described herein. Applications that may include the apparatus and systems of various embodiments may broadly include a variety of electronic and computer systems. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that may be communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system may encompass software, firmware, and hardware implementations.

The methods described herein may be implemented by software programs executable by a computer system. Further, implementations may include distributed processing, component/object distributed processing, and parallel processing. Alternatively or in addition, virtual computer system processing maybe constructed to implement one or more of the methods or functionality as described herein.

Although components and functions are described that may be implemented in particular embodiments with reference to particular standards and protocols, the components and functions are not limited to such standards and protocols. For example, standards for Internet and other packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed herein are considered equivalents thereof.

The illustrations described herein are intended to provide a general understanding of the structure of various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of apparatus, processors, and systems that utilize the structures or methods described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Additionally, the illustrations are merely representational and may not be drawn to scale. Certain proportions within the illustrations may be exaggerated, while other proportions may be minimized. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive.

The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments, which fall within the true spirit and scope of the description. Thus, to the maximum extent allowed by law, the scope is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description.

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 GeneratedValue 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.

22. 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 key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs;

a treatment eligibility component executable by the processor to:

determine based on the Usage oriented KPls, the Sales oriented KPIs or both, a customer treatment including development treatment, retention treatment, education treatment, next best offer, or a 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 at least one individual offer by multiplying a probability of a positive response to the at least one individual offer by one or more values comprising:

a value based on a projected financial value of an acceptance of the at least one individual offer by the customer;

a customer strategy weight based on a selected strategy for the customer for the at least one individual offer;

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

a context weight used to up-weight the at least one individual 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; and

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.

23. The system of claim 22, 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.

24. The system of claim 23, 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.

25. The system of claim 22, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

26. The system of claim 22, wherein the probability of a positive response to the at least one individual 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); and wherein the processor calculates the probability of a positive response using an adaptive model for each offer for each customer.

27. The system of claim 26, 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.

28. The system of claim 27, 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.

29. 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 key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs;

determining by the processor executing a treatment eligibility component, based on the usage oriented KPls, 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 at least one individual offer by multiplying a probability of a positive response to the at least one individual offer by one or more values comprising one or more of:

a value based on a projected financial value of an acceptance of the at least one individual offer by the customer;

a customer strategy weight based on a selected strategy for the customer for the at least one individual offer;

an offer weight used to up-weight or down-weight the at least one individual offer based on relevance of the at least one individual offer to a customer; and

a context weight used to up-weight the at least one individual 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; and

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.

30. The method of claim 29, 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.

31. The method of claim 30, 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.

32. The method of claim 29, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

33. The method of claim 29, 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), and wherein the processor calculates the probability of a positive response using an adaptive model for each offer for each customer.

34. The method of claim 33, 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.

35. The method of claim 34, 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.

36. 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 key performance indicators (KPIs) including Usage oriented KPIs and Sales oriented KPIs;

determine, based on the Usage oriented KPls, the Sales oriented KPIs or both, a customer treatment including development treatment, retention treatment, education treatment, next best offer, or a 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 at least one individual offer by multiplying a probability of a positive response to the at least one individual offer by one or more values comprising:

a value based on a projected financial value of an acceptance of the at least one individual offer by the customer;

a customer strategy weight based on a selected strategy for the customer for the at least one individual;

an offer weight used to up-weight or down-weight the at least one individual offer based on relevance of at least one individual 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 at least one individual 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; and

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.

37. The product of claim 36, 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.

38. The product of claim 37, 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.

39. The product of claim 36, wherein the next best offer includes education treatment, Churn Prevention treatment and cross sell offers.

40. The product of claim 36, 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), and wherein the processor calculates the probability of a positive response using an adaptive model for each offer for each customer and the one or more values multiplied by the probability of a positive response.

41. The product of claim 40, 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.

42. The product of claim 41, 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.