Patent Publication Number: US-2015086002-A1

Title: System and method for managing outbound interactions

Description:
FIELD OF INVENTION 
     The method and system disclosed herein, in general, relates to telephony and communications. More particularly, the method and system disclosed herein relates to managing a plurality of outbound interactions. 
     BACKGROUND OF THE INVENTION 
     Customer targeting or solicitation is an integral part of promotional campaigns used by various business enterprises to increase their customer base. Traditionally, products and/or services were promoted using general advertising. As used herein, the term “general advertising” refers to a method of advertising where a business entity typically informs the public at large regarding its products and/or services without specifically targeting a particular customer group. However, general advertising is typically expensive and also not very efficient. Therefore, there is a need for promoting a product and/or service by direct selling, where the potential buyers or customers are approached personally by the business entity which wishes to promote its products and/or services. 
     Quite frequently, call centers assist business entities in the promotion of their products and/or services using the method of direct selling. Conventional methods of direct selling require call center agents to handle each call personally to gauge a customer&#39;s interest in the products and/or services that are being promoted. This then means that the call centers eventually spend precious time and resources on pursuing a single customer. It is therefore a time-consuming process and only marginal success is achieved in the conversion of potential customers into actual customers. This adversely affects the process of customer acquisition for the business entity. Therefore, in order to increase the probability of customer acquisition, the call centers automate the process of placing calls to the customers. For this purpose, the call centers employ automated dialers. An automated dialer refers to an electronic dialing device or software which automatically places outbound calls for the telephone numbers that are fed into the dialing system and proceeds on dialing on a continuous basis until the call is answered by a potential customer. Once the outbound call has been responded to by a potential customer, the call is transferred lo a call center agent who is responsible to handle the call. 
     Some automated dialers forecast the availability of a call center agent to handle a potential customer&#39;s call. These automated dialers are referred to as predictive dialers. Typically, the predictive dialers place interactions, such as calls to contacts in a campaign based on various parameters such as a predicted count of available agents, number of available contacts, time required to connect each contact, etc. When the contact answers the call, the call is transferred to. a free agent, in this method, the selection of the agent by the predictive dialers is done independent of selection of the contact that was called. Furthermore, these predictive dialers do not categorize the contacts and the agents into different groups, and as a result, are often rendered useless, especially when the skills of an agent do not match the requirements of a contact. 
     Moreover, the conventional predictive dialers perform predictions by considering all the call center agents as similar. Therefore, while the predictive dialers specify the number of call center agents who are available to take a particular call, they do not specify which of the call center agents would be available to handle the call. In numerous instances this leads to a scenario where the forecasted call center agents do not have the relevant and required expertise to handle a potential customer&#39;s call. 
     Prevailing automated dialers also do not provide for the facility to transfer customer responded interactions to a call center agent that are appropriately matched to a particular customer. This may result in the loss of opportunity to convert a potential customer into an actual customer and eventually leads to low rates of customer acquisition. 
     Hence, there exists a need for a method and system for managing multiple outbound interactions and matching each customer to an appropriate agent, thereby increasing talk times and productivity of the agents. 
     SUMMARY OF THE INVENTION 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described in the detailed description of the invention. This summary is not intended to identify key or essential inventive concepts of the claimed subject matter, nor is it intended for determining the scope of the claimed subject matter. 
     The method and system disclosed herein addresses the above stated need for managing a plurality of outbound interactions between multiple contacts and agents. An interaction management platform and a parallel predictive dialer are provided. The parallel predictive dialer comprises multiple predictive dialers. 
     The interaction management platform comprises a classification module, a mapping module, an availability determination module, and an interaction processing module. The classification module classifies a plurality of contacts into contact groups based on contact classification criteria. The contact classification criteria comprise, for example, geographical location, language, age, sex, interests, etc., of the contacts in the contact group. In an embodiment the classification module classifies the contacts into the contact groups by applying one or more logical rules to one or more, attributes of the contacts. The logical rules are referred to as “contact filters”. 
     In an embodiment, the interaction management platform further comprises a data acquisition module that acquires personal information of the contacts from various sources. The personal information comprise for example, name, phone number, address, age, etc., of a contact. The contact information of each of the contact in the contact groups is stored in a data repository in the interaction management platform. 
     Further, the classification module classifies the agents into agent groups based on agent classification criteria. The agent classification criteria comprise, for example, geographical location, language, skill sets, etc., of the agents in the agent groups. In an embodiment, the classification module classifies one or more of the agents under two or more agent groups. 
     The mapping module maps each of the contacts in the contact groups to one or more of the agent groups. 
     The parallel predictive dialer assigns the predictive dialers to the classified agent groups. The predictive dialers then place the outbound interactions to the contacts in the contact groups. In an embodiment, the predictive dialers parallelly predict number of contacts in each of the contact groups for placing the outbound interactions and availability of the agents prior to placing the outbound interactions to the contacts based on prediction criteria. 
     The availability determination module determines available contacts from the contact groups. As used herein, the term “available contacts” are the contacts who have answered the outbound interactions placed by the predictive dialers. 
     The interaction processing module concurrently processes answered interactions. As used herein, the term “answered interactions” refer to the outbound interactions that are answered by the available contacts. The interaction processing module assigns each of the available contacts to one of the agents from the agent groups and transfers each of the answered interactions to the assigned agent. That is, when a parallel predictive dialer dials out an outbound call to a contact and the call is answered, then the interaction processing module selects an agent from an agent group which is mapped to the contact, to converse with the contact. 
     Systems and methods of varying scope are described herein. In addition to the aspects and advantages described in this summary, further aspects and advantages will become apparent by reference to the drawings and with reference to the detailed description that follows. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS  
         FIG. 1  exemplarily illustrates a system for managing a plurality of outbound interactions. 
         FIG. 2  illustrates a method for managing a plurality of outbound interactions. 
         FIG. 3  exemplarily illustrates the architecture of a computer system employed by the interaction management, platform for managing the outbound interactions. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments, which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the embodiments, and it is to be understood that other embodiments may be utilised and that logical, mechanical, electrical and other changes may be made without departing from the scope of the embodiments. The following detailed description is, therefore, not to be taken in a limiting sense. 
       FIG. 1  exemplarily illustrates a system  100  for managing a plurality of outbound interactions. The system  100  comprises an interaction management platform  101  in communication with a parallel predictive dialer  108 . The parallel predictive dialer  108  comprises a plurality of predictive dialers  112 . As used herein, the term “outbound interactions” refers to an interactions initiated by the predictive dialers  112  to a customer on behalf of a client or a business entity. As used herein, the term “predictive dialers  112 ” refers to multiple devices that can automatically initiate multiple interactions simultaneously. The predictive dialers  112  are used for simultaneously placing the outbound interactions to multiple contacts  110 . As used herein, the term “contacts  110 ” refers, to potential customers of a business enterprise, to who the business enterprise wishes to target promotional material or campaigns through agents  109  in a call center. 
     For the purposes of illustration, the “interactions” referred to in the detailed description refers to telephone calls; however, the scope of the “interactions” disclosed herein is not limited to telephone calls but may be extended to various other interactions, for example, online chat, video chat, email, short message service (SMS), web pushes, web collaborations, instant messaging, Multimedia Messaging Service (MMS), etc. 
     The predictive dialers  112  connect the agents  109  with the contacts  110  through a network  111 . The network  111  is a telephone network and/or a data network that connects exchanges, switches, etc. The network  111  is, for example, a wired telephony network, a wireless network, a voice call network, a signaling system number 7 (SS7) network, an internet protocol data network, other data networks, etc. 
     The interaction management platform  101  comprises a classification module  103 , a mapping module  104 , an availability determination module  105 , an interaction processing, module  106 , and a data repository  107 . The classification module  103  classifies a plurality of contacts  110  into contact groups based on contact classification criteria. The contact classification criteria comprise, for example, geographical location, language, age, sex, interests, etc., of a contact. 
     In an embodiment, the interaction management platform  101  further comprises a data acquisition module  102  that acquires personal information of the contacts  110  from various sources. The sources comprise, for example, lead generation campaigns, websites comprising interests of the contacts, surveys, institutes such as colleges, training institutes, etc. The data acquisition module  102  may also receive the personal information of the contacts via manual entry by an operator. The personal information comprises, for example, name, phone number, address, age, etc., of a contact. The contact information of each of the contact in the contact groups is stored in the data repository  107  of the interaction management platform  101 . In an example, the classification module  103  classifies the contacts based on the geographical region of the contacts  110 . In this example, the geographical location of each contact is identified and stored in the data repository  107 . The classification module  103  classifies the contacts  110  into four groups—north contacts, east contacts, west contacts, and south contacts, based on the geographical region of the contacts  110 . 
     Further, the classification module  103  classifies multiple agents  109  into agent groups based on agent classification criteria. The agent classification criteria comprise, for example, geographical location, language, skill sets, etc., of an agent. Each of the agent groups comprises at least one agent. In an example, the agents  109  are grouped based on the geographical location of the agents  109 , for example, as north agents, east agents, west agents, and south agents. 
     In an embodiment, the classification module  103  classifies the contacts into the contact groups by applying one or more logical rules to one or more attributes of the contacts. The logical rules are referred to as “contact filters”. The attributes of the contacts are, for example, age, gender, interests, geographical location, etc. The classification module  103  applies the logical rules comprising, for example, AND, OR, NOT, LIKE, EQUAL, etc., on the attributes of a contact for creating the contact groups. In an example, a contact group comprises the following contacts: males OR females AND aged&gt;21 AND interested in cars. The contacts  110  for who a particular logical rule or filter rule evaluates to “true”, together belong under one contact group. 
     The mapping module  104  maps each of the contacts  110  in the contact groups with one or more of the agent groups. That is, each contact is assigned to an appropriate agent group. Any of the agents  109  in the agent group associated with the contact may be assigned to converse with the contact. Consider an example, where the agents  109  and the contacts  110  are classified into multiple agent groups and contact groups respectively on the basis of the language spoken. In this example, the contacts  110  who speak Hindi are mapped to agent group/s which comprises agents  109  who can converse in Hindi. 
     The parallel predictive dialer  108  assigns the predictive dialers  112  to the agent groups. The predictive dialers  112  place the outbound interactions to the contacts in the contact groups. The predictive dialers  112  function independent of each other. In an embodiment the predictive dialers  112  concurrently place the outbound interactions to the contacts. 
     In an example, the outbound interactions are in the form of online messages, such as, online chat. In this embodiment, the predictive dialers  112  send chat messages to online contacts. 
     The availability determination, module  105  determines available contacts  110  from the contact groups. As used herein, the term “available contacts  110 ” are the contacts  110  who answer the outbound interactions placed by the predictive dialers  112 . 
     In an embodiment, the predictive dialers  112  parallelly predict number of contacts  110  in each of the contact groups for placing the outbound interactions and availability of the agents  109  prior to placing the outbound interactions to the contacts based on prediction criteria. In this embodiment, the predictive dialers  112  first determine available agents in each of the agent groups. The predictive dialers  112  then select the contacts for placing the outbound interactions based on which of the agents are available. The prediction criteria comprise, for example, prior interaction patterns of the contacts  110  in the contact group, schedule of the agents  109 , etc. For example, if a contact A from contact group XYZ is mapped to agent group BCD and all the agents  109  in the agent group BCD are busy, then the predictive dialers  112  will refrain from placing any outbound interactions to contact A, until one of the agents  109  from agent group BCD is predicted to be free. Therefore, the predictive dialers  112  minimize the time that the agents  109  spend waiting between interactions and minimize the occurrence of a contact  110  answering when no agent  109  is available. The interaction management platform  101  thereby determines which agents  109  are free rather than just how many agents  109  are free and matches the contacts to an appropriate agent. 
     Consider an example where during a regional festival, a relatively high number of contacts  110  of one region have put their phones on voicemail. In this example, the predictive dialers assigned to the particular region detect that a considerably high number of the placed outbound interactions are going to the voicemails of the contacts  110 . The predictive dialers then increase the number of call attempts made to the contacts  110  of the region in order to keep the agents  109  sufficiently busy, thereby ensuring that talk time of the agents  109  remain sufficient enough to ensure acceptable productivity. 
     The parallel predictive dialer  108  thereby allows running of multiple predictive dialers  112  that performs the prediction and selection of the contacts  110  to be dialed on the basis of various attributes of the contacts  110  and how the agents  109  are mapped for catering to those attributes. In an example, the attribute for each contact is “region”. 
     The interaction processing module  106  concurrently processes answered interactions. As used herein, the term “answered interactions” refers to the outbound interactions that are answered by the available contacts  110 . Further, the interaction processing module  306  assigns each of the available contacts  110  to one of the agents  109  from the agent groups and transfers each of the answered interactions to the assigned agent. That is, when predictive dialer  112  dials an outbound interaction to a contact and the interaction is answered by the contact, then the interaction processing module  106  selects a free agent from the agent group to which the contact is mapped. As used herein, the term “free agent” refers to an agent who is currently available to handle the answered interactions. For assigning an agent to a contact, the call processing may employ various assignment methods, for example, least recently used (LRU), etc. In the LRU method, when a call is answered by a contact, the interaction processing module  106  assigns the agent from an agent queue mapped to the contact who is least recently used. 
     In an embodiment, the classification module  103  classifies one or more of the agents under two or more agent groups. The agents  109  classified under two or more agent groups are herein referred to as “shared resource”. In this embodiment, the same agent may be assigned to contacts  110  belonging to different contact groups. Further, during assignment of the interactions to the agent, the interaction processing module  106  may consider the agent as available in equal fractions of time in each of the agent groups that the agent belongs to, or may consider the agent to be available in each agent group in a time slice or a weighted time slice manner, etc. The process of utilizing a shared resource to efficiently manage the outbound interactions is referred to as “resource blending”. 
     The interaction processing module  106  performs scheduling of the shared resource based on scheduling algorithms, for example, round robin scheduling, weighted scheduling, etc. In round robin scheduling, the interaction processing module  106  assigns free agents  109  to contacts  110  in a circular order, without priority. In weighted scheduling, the interaction processing module  106  assigns the tree agents  109  to the contacts  110  based on weights assigned for a shared resource to each contact group. In an example, a shared resource may be assigned weights of 30% of the agents&#39; time in contact group X and 70% of the agent&#39;s time in contact group Y. 
       FIG. 2  illustrates a method for managing outbound interactions between multiple agents  109  and multiple contacts  110 . The method comprises the steps of classifying a plurality of contacts  110  into contact groups based on contact classification criteria at step  201 , classifying a plurality of agents  109  into agent groups based on agent classification criteria at step  202 , mapping each the contacts  110  in the contact groups with one or more of the agent groups at step  203 , assigning one of the predictive dialers to each of the agent groups at step  204 , simultaneously placing the outbound interactions to the contacts  110  in the contact groups at step  205 , determining available contacts  110  from the contact groups at step  206 , and concurrently processing the answered interactions at step  207 . In an embodiment, the predictive dialers  112  assigned to each of the agent groups parallelly predict number of the contacts in the contact groups and availability of the agents prior to placing the outbound interactions to the contacts based on prediction criteria. 
     In an embodiment, the Interaction management platform  101  disclosed herein runs locally on a computer system. In another embodiment, the interaction management platform  101  runs remotely over the network  111  by employing a web browser and a remote server, a mobile device, or other electronic devices. 
     Consider an example, where a telemarketing campaign has to reach out to 50000 contacts  110  across India. In this example, there are 20 agents 109 trained for the telemarketing campaign. Since the contacts  110  typically speak languages according to their geographical location, the interaction management platform  101  classifies the 50000 contacts  110  into 4 contact groups—north contacts, east contacts, east contacts, and west contacts. Further, the interaction management platform  101  classifies the agents  109  based on the language spoken by the agents  109  into four agent groups—north agent, south agent, east agent, and west agent. 
     The interaction management platform  101  then maps each contact in the contact groups to an agent group. In this example, each of the contacts  110  in the north contacts group are mapped to the north agent, each of the contacts  110  in the south contacts group are mapped to the south agent, each of the contacts  110  in the east contacts group are mapped to the east agent, and each of the contacts  110  in the west contacts group are mapped to the west agent. 
     In this example, four predictive dialers  112  are assigned to each of the four contact groups. The predictive dialers  112  then place the outbound interactions to the contacts  110  in the contact groups. The interaction management platform  101  then determines the available contacts from the contact groups. Further, the call processing platform assigns each of the available contacts  110  to a free agent based on a scheduling method and transfers the call to the assigned agent. 
       FIG. 3  exemplarily illustrates the architecture of a computer system  300  employed by the interaction management platform  101  managing a plurality of outbound interactions. The computer system  300  comprises, for example, a processor  301 , a memory unit  302  for storing programs and data, an input/output (I/O) controller  303 , a network interface  304 , a data bus  305 , input devices  306 , output devices  309 , etc. 
     The processor  301  is an electronic circuit that executes computer programs. The memory unit  302  stores programs, applications, and data. For example, the modules  101 ,  102 ,  103 ,  104 ,  105 ,  106 , and  107  of the interaction management platform  101  are stored on the memory unit  302  of the computer-system  300 . The memory unit  302  is, for example, a random access memory (RAM) or another type of dynamic storage device that stores information and instructions for execution by the processor  301 . The memory unit  302  also stores temporary variables and other intermediate information used during execution of the instructions by the processor  301 . The computer system  300  may further comprise a read only memory (ROM) or another type of static storage device that stores static information and instructions for the processor  301 . The network interface  304  enables connection of the computer system  300  to the network. The network interface  304  comprises, for example, an infrared (IR) interface, a WiFi interface, a universal serial bus interface (USB), a local area network (LAN), a wide area network (WAN) interface, etc. The I/O controller  303  controls the input and output actions performed, for example, by administrators of the interaction management platform  101 . The data bus  305  permits communication between the modules, for example,  101 ,  102 ,  103 ,  104 ,  103 ,  106 ,  107 , etc., of the interaction management platform  101 . 
     The input devices  306  are used for inputting data into the computer system  300 . The input devices  306  are, for example, a keyboard such as an alphanumeric keyboard, a joystick, a mouse, a touch pad, a light pen, etc. The output devices  309  output the results of the actions computed by the interaction management platform  101 , for example, to administrators of the interaction management platform  101 . 
     The computer system  300  may comprise, for example, a fixed media drive  307  and a removable media drive  308  for receiving removable media. Computer applications and programs are used for operating the computer system  300 . The programs may be loaded onto the fixed media drive  307  and into the memory unit  302  of the computer system  300  via the removable media drive  308 . In an embodiment, the computer applications and programs may be loaded directly via a communication network. 
     The processor  301  retrieves the instructions for executing the modules, for example,  101 ,  102 ,  103 ,  104 ,  105 ,  106 ,  107 , etc., of the interaction management platform  101  from the program memory in the form of signals. A program counter determines the location of the instructions in the program memory. The program counter stores a number that identifies the current position in the program of the modules, for example,  101 ,  102 ,  103 ,  104 ,  105 ,  106 ,  107 , etc., of the interaction management platform  101 . 
     The instructions fetched by the processor  301  from the program memory after being processed are decoded. The instructions are placed in an instruction register in the processor  301 . After processing and decoding, the processor  301  executes the instructions. For example, the classification module  103  defines instructions for classifying a plurality of contacts into contact groups and a plurality of agents into the agent groups. The mapping module  104  defines instructions for mapping each of the contacts in the contact groups to one or more of the agent groups. The availability determination module defines instructions for determining available contacts from the contact groups. The call processing module defines instructions for concurrently processing answered interactions. 
     The processor  301  of the interaction management platform  101  retrieves the instructions defined by the data acquisition module  102 , the classification module  103 , the mapping module  104 , the availability determination module  105 , the interaction processing module  106 , and the data repository  107  and executes the instructions to obtain one or more outputs. 
     For purposes of illustration, the detailed description refers to the interaction management platform  101  being run locally on a computer system  300 ; however the scope of the computer implemented method and system  100  disclosed herein is not limited to the interaction management platform  101  being run locally on the computer system  300  via the processor  301 , but may be extended to run remotely over a communication network by employing a web browser and a remote server, a mobile device, or other electronic devices. 
     This written description uses examples to describe the subject matter herein, including the best mode, and also to enable any person skilled in the art to make and use the subject matter. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal language of the claims.