WO2001080538A1 - Method and system for scheduling inbound inquiries - Google Patents

Method and system for scheduling inbound inquiries Download PDF

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Publication number
WO2001080538A1
WO2001080538A1 PCT/US2001/011311 US0111311W WO0180538A1 WO 2001080538 A1 WO2001080538 A1 WO 2001080538A1 US 0111311 W US0111311 W US 0111311W WO 0180538 A1 WO0180538 A1 WO 0180538A1
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WIPO (PCT)
Prior art keywords
inbound
call
information
inquiries
calls
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PCT/US2001/011311
Other languages
French (fr)
Inventor
Daniel N. Duncan
Alexander N. Svoronos
Thomas J. Miller
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Austin Logistics Incorporated
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Publication date
Application filed by Austin Logistics Incorporated filed Critical Austin Logistics Incorporated
Priority to AU2001253222A priority Critical patent/AU2001253222A1/en
Publication of WO2001080538A1 publication Critical patent/WO2001080538A1/en

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Classifications

    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04MTELEPHONIC COMMUNICATION
    • H04M3/00Automatic or semi-automatic exchanges
    • H04M3/42Systems providing special services or facilities to subscribers
    • H04M3/50Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
    • H04M3/51Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
    • H04M3/523Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing with call distribution or queueing
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/04Real-time or near real-time messaging, e.g. instant messaging [IM]
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/21Monitoring or handling of messages
    • H04L51/226Delivery according to priorities

Definitions

  • This invention relates in general to the fields of telephony and computer networks, and more particularly to a method and system for scheduling inbound inquires made by telephone or by other electronic messages.
  • Telephone calling centers represent the front iine for customer service and marketing operations of many businesses. Typical calling centers receive or make hundreds of telephone calls per day with the aid of automated telephony equipment . With the Internet growing in importance as a way of communicating with customers, calling centers have also evolved to send and respond to electronic messages, such as e-mail or instant messages.
  • Calling centers often play a dual role of both sending outbound inquiries and answering inbound inquiries. For instance, calling centers use predictive dialers that automatically dial outbound telephone calls to contact individuals and then transfer the contacted individuals to agents when the individual answers the phone. Inbound telephone calls by individuals to the calling center are received by telephony equipment in the calling center and distributed to agents as the agents become available. Calling centers often combine outbound and inbound functions as a way to improve the talk time efficiency of calling center agents. Thus, for instance, when inbound calls have expected hold times that are acceptable, agents may be reassigned to place outbound telephone calls to help ensure that the agents are fully occupied.
  • ACD automatic call distribution system
  • Inbound calls may be routed to different queues based on rules and data, allowing a basic prioritization of inbound calls. , For example, inbound callers seeking information about a new credit card account might be assigned to a different queue than inbound callers having questions about their account balances. Once assigned to a queue, calls in that queue are generally handled in a first-in-first-out basis. Thus, a caller's hold time generally depends upon the caller's depth in the queue.
  • VRU voice response unit
  • ACD advanced integrated circuit card
  • VOIP voice over internet protocol
  • ACD and VRU systems collect caller information when inbound calls are received.
  • caller information is automated number identification ("ANI") information provided by telephone networks that identify the telephone number of the inbound call .
  • DNIS destination number identification system information
  • ANI automated number identification
  • DNIS destination number identification system information
  • ACD conventional calling centers are able to gather information on the caller and provide that information to ⁇ the agent .
  • the use of VOIP improves the integration of data and telephony by passing both data and telephony through a network with internet protocol and by combining voice inquiries with electronic message inquiries, such as e-mail.
  • One example of such integration is the Intelligent Contact Management ("ICM") solution sold by CISCO Systems, Inc.
  • Another example is the integrated response systems available from eShare Technologies, described in greater detail at www.eShare.com.
  • telephone receiving devices provide improved distribution of inbound telephone calls to agents, the receiving devices are generally not helpful in managing hold times when the number of inbound calls exceeds the agent answering capacity. For instance, customers tend to make inbound calls for service at similar times.
  • a large volume of inbound calls tends to lead to longer wait times during popular calling periods resulting in customer dissatisfaction.
  • a greater number of inbound callers hang up or "silently" close their accounts by seeking other service providers with better service.
  • Another example of excessive hold times affecting the behavior of inbound callers occurs with telemarketing.
  • the volume of inbound calls in a marketing operation tends to increase dramatically shortly after a television advertisement is aired. Extended hold times result in a greater number of customer hang-ups and lost sales .
  • a method and system for ordering inbound inquiries is provided that substantially eliminates or reduces disadvantages and problems associated with previously developed methods and systems for responding to inbound inquiries.
  • Inbound inquiry information associated with each inbound inquiry is applied to a model to determine a priority value for ordering the inbound inquiry for response relative to other inbound inquiries.
  • inbound inquiries may include inbound telephone calls, e-mails, instant messages, or other electronic messages formats, such as those available through the internet .
  • a telephone call receiving device receives plural inbound telephone calls for distribution to one or more agents.
  • the telephone call receiving device may include an ACD, a VRU, a PBX, a VOIP server or any combination of such devices that are operable to receive plural inbound telephone calls and redirect the inbound telephone calls to one or more agents.
  • the inbound telephone calls have associated caller information, such as ANI or DNIS information, which the receiving device interprets. ANI information identifies the telephone number from which the inbound call originates, and DNIS information identifies the telephone number to which the inbound call was directed.
  • a scheduling module interfaced with or integrated within the receiving device determines an order for the handling of inbound telephone calls based in part on the predicted outcome of the inbound telephone calls.
  • the scheduling module places the inbound calls in a queue, the queue acting as a virtual hold, and applies a caller model to the caller information associated with the inbound calls in order to forecast the predicted outcome of the inbound calls.
  • the order for handling the inbound calls is based on a priority value calculated from the application of a caller model to the caller information by a call evaluation sub-module and based on the capacity of the receiving device.
  • the scheduling module releases the inbound calls from the virtual hold queue and places the inbound calls in the queue of the receiving device.
  • the scheduling module or the receiving device may perform real-time scheduling of inbound call inventory by re-ordering queues of the receiving device based on the priority value.
  • the call evaluation sub-module uses algorithms and models provided by a modeling module that analyzes inbound call histories to forecast outcomes of pending inbound calls. It utilizes the forecasts to compute priority values. For example, in the modeling module, performing logistic regression on prior inbound calls using caller and/or call information and prior call history as independent (or predictive) variables and a dependent variable of caller attrition, provides a model that forecasts pending inbound caller attrition based on the caller and/or call information. Alternatively, performing linear regression modeling on prior inbound calls, using caller and/or call information as independent (or predictive) variables and a dependent variable of connect time, provides a model that forecasts the expected agent talk time for each incoming call.
  • Predictive variables for the logistic and linear regression equations may include call information such as the originating number or exchange, the originating location, the dialed number, the time of day and the likely purpose of the call.
  • call information such as the originating number or exchange, the originating location, the dialed number, the time of day and the likely purpose of the call.
  • caller information such as account information derived from association of the originating number and an account data base, or derived from data input by the inbound caller by a VRU. From caller information and/or call information, additional predictive variables are available for forecasting the outcome of the inbound call, including demographic information that may be associated with the call and/or caller.
  • the call evaluation sub-module estimates one or more quantities of interest with one or more models provided by the modeling module, and computes the call's priority value based on the quantities of interest. For example, the call value of "the probability of a sale per minute of expected talk time" may be estimated by dividing the estimated probability of a sale by the estimated talk time.
  • the call evaluation sub- module uses the estimated quantities of interest to formulate and solve a constrained optimization problem based on conventional mathematical techniques, such as the simplex method for linear problems or the Conjugate gradient and Projected Lagrangian techniques for Non- linear problems.
  • call evaluation sub-module may present a value that represents the solution to maximizing objectives such as agent productivity to either minimize attrition or to produce product sales.
  • the present invention provides a number of important technical advantages.
  • One important technical advantage is that inbound inquiries, such as inbound telephone calls, are ordered for response based at least in part on the predicted outcome of the inbound inquiries. This allows, for instance, agents to respond to customers that are more sensitive to holding time before responding to customers who are less sensitive to holding time.
  • This also allows, as another example, enhanced efficiency of handling of inbound telephone calls by seeking to improve the overall outcomes of the inbound calls based on the forecasted outcomes. For instance, in a telemarketing environment, inbound callers with a higher likelihood of purchasing an item or service may be responded to before customers with a lower probability of a purchase outcome. In fact, computing estimated outcomes and then formulating and solving the appropriate constrained optimization problem provides an ordering sequence that maximizes purchases made by inbound callers responding to a television advertisement. Another important technical advantage of the present invention is that forecasted outcomes are available with minimal caller information. Generally the identity and purpose of inbound calls are difficult to discern because little information is available regarding the inbound caller. The use of statistical analysis of historical inbound calling data allows accurate modeling of outcomes with minimal knowledge of the identity and purpose of the inbound caller.
  • inbound calls are prioritized based on caller and call information.
  • the present invention allows flexible use in a number of inbound inquiry environments such as telemarketing and customer service environments.
  • Caller models may have different predictive variables depending upon the modeled outcome and the caller information obtained with the inbound inquiry. For instance, telemarketing applications using models that forecast probability of a purchase may focus on predictive variables derived from demographic information based on the origination of the inbound call.
  • customer service applications using models that forecast caller attrition may have more detailed predictive variables derived from customer account information.
  • inbound calling models and objectives may be closely tailored to a user's particular application.
  • estimates of the inbound call talk time may lead to constrained optimization solutions designed to maximize the use of the available agent talk time. Further, an overall response strategy that accounts for electronic message inquiries as well as telephone inquiries is more easily adopted.
  • FIGURE 1 depicts a block diagram of an inbound telephone call receiving device interfaced with an inbound scheduling system
  • FIGURE 2 depicts a flow diagram of a method for ordering inbound callers for response by agents.
  • FIGURES Preferred embodiments of the present invention are illustrated in the FIGURES, like numeral being used to refer to like and corresponding parts of the various drawings .
  • inbound telephone calling centers maintain holding times for inbound callers within desired constraints by adjusting the response capacity of the calling center. For instance, during projected or actual periods of heavy inbound calling volume, additional agents may be assigned to respond to inbound calls by adding agents to the calling center or by reducing the number of outbound calls. However, once the overall capacity of a calling center is reached, inbound calls in excess of calling center capacity will generally result in increased holding times for the inbound callers.
  • Inadequate capacity to handle inbound calls may result from periodic increases in the number of inbound calls during popular calling times, or may result from one time surges due to factors such as system-wide customer service glitches or the effects of advertising.
  • the excess inbound calls are assigned to hold for an available agent in queues of an inbound telephone call receiving device and are handled on a first-in first-out basis for each holding queue.
  • the result of excessive hold times is that customers having a greater sensitivity to long hold times will hang-up in frustration.
  • Responding to holding inbound callers on a first-in- first-out basis does not necessarily provide the most efficient results for a calling center.
  • Agent time is used most efficiently when an agent is responding to inbound callers most likely to achieve a desired outcome. For instance, in a telemarketing role an agent is most productive when speaking with inbound callers likely to purchase the marketed service or product . Similarly, in a customer service role, an agent is most productive when speaking with inbound callers who provide a greater rate of profitability to the calling center.
  • routing calls to agents on a first-in-first-out basis does not provide the most efficient use of agent time when inbound callers having a higher probability of a desired outcome are treated in the same manner as inbound callers having a lower probability of a desired outcome.
  • inbound inquiries are received in alternative formats, such as e-mail or instant messages.
  • FIGURE 1 a block diagram depicts an inbound scheduling system 10 that schedules inbound telephone calls for response by agents in an order based in part on the predicted outcome of the inbound telephone calls.
  • Inbound scheduling system 10 includes a scheduling module 12, a call evaluation sub-module 13, and a modeling module 14, and is interfaced with an inbound call history data base 16 and account information data base 18.
  • Modeling module 14 builds one or more models that forecast the outcomes of inbound calls using inbound call history from data base 16 and/or from account information of data base 18.
  • Scheduling module 12 applies the models to forecast outcomes of pending inbound calls and schedules an order for agents to respond to the pending inbound calls based on the call evaluation sub-module 13.
  • Modeling module 14 builds statistical models and call evaluation sub-module 13 computes the priority value which is used by scheduling module 12. The priority value is the result of computations based on the models, but also of solutions to optimization problems that may be defined on computations based on the models.
  • Inbound scheduling system 10 interfaces with an inbound telephone call receiving device 20.
  • Scheduling system 10 and receiving device 20 may be integrated in a single computing platform, or may be based on separate computing platforms interfaced with proprietary application programming interfaces of the receiving device 20 or interfaced with commercially available application middle ware such as Dialogic 's CT Connect or Microsoft's TAPI .
  • Inbound telephone call receiving device 20 is a conventional telephony device that accepts inbound telephone calls through a telephony interface 22, such as conventional TI or fiber interfaces.
  • Inbound telephone call receiving device 20 may include an ACD, a VRU, a PBX, a VOIP server or any combination of such conventional devices.
  • Inbound telephone calls received through interface 22 are distributed to one or more answering queues 24 for response by agents operating telephony devices 26.
  • FIGURE 1 depicts an embodiment of the present invention that orders inbound telephone calls
  • alternative embodiments apply scheduling module 12 and modeling module 14 to schedule other types of inbound inquiries, such as e-mail or instant message inquiries, by interfacing inbound scheduling system 10 with an appropriate inbound receiving device, such as an internet server.
  • Inbound telephone call receiving device 20 accepts inbound telephone calls through interface 22 and obtains caller information associated with the inbound calls such as ANI and DNIS information.
  • additional caller information such as account information, is obtained through automated interaction with the inbound callers.
  • a VRU may query an inbound caller to provide an account number or a reason for the call, such as to open a new account, to change account information, to check account information, to purchase a particular service or item, or to collect inbound caller information when ANI is not operative, such as when caller-ID is blocked.
  • inbound inquiries may include e- mail or instant messages that provide inquiry information based on login ID, e-mail address, IP or instant message address.
  • additional information can be gathered by an automated e-mail or instant message survey response that requests a phone number, purchase interest, account number or other relevant information.
  • Receiving device 20 passes the caller information to scheduling system 10, such as through a data query, and awaits a response from scheduling system 10 before allocating the inbound call to an answering queue.
  • receiving device 20 provides scheduling system 10 with agent activity and capacity.
  • a receiving device 20 may include both a VRU and an ACD with the ACD providing agent activity information.
  • an "out of order" response may be provided by scheduling system 10 when operator capacity is unavailable or in high use, meaning that the first call in is not necessarily the first call out .
  • Scheduling module 12 keeps inbound calls in a queue that acts as a virtual hold until a response is desired and then releases the inbound call for placement in an answering queue 24.
  • scheduling system 10 responds to queries from receiving device 20 based on the priority of the inbound call, essentially creating an ordered queue on receiving device 20 by delaying the response to inbound calls having lower priorities.
  • scheduling module 12 may re-order queues directly within receiving device 20 to allow realtime ordering of inbound telephone call queues.
  • Scheduling module 12 obtains data to apply to a caller model by performing a look-up based on the caller information received from receiving device 20.
  • Caller information may include account number, zip code, area code, telephone exchange, reservation number or other pertinent information obtained from the inbound caller, such as with a VRU, or derived from information obtained by the receiving device 20 with the inbound call, such as ANI or DNIS information.
  • the nature of caller information depends upon the implementation of scheduling system 10 and is generally configurable through a graphical user interface provided with conventional receiving devices.
  • scheduling module 12 may query and join data from other sources such as zip+4 and credit bureau sources and demographic information otherwise derivable from the caller information.
  • scheduling system 10 When sufficient capacity exists for response by receiving device 20, scheduling system 10 releases calls immediately back to receiving device 20. In other words inbound calls are not delayed if sufficient capacity exists to handle the inbound calls, but are routed for immediate answering. When capacity is tight on receiving device 20, calls are delayed on a virtual hold by scheduling system 10 until an appropriate time based on the priority value computed by the call evaluation sub- module 13. Whether or not inbound calls are delayed, scheduling system 10 gathers and stores data for the inbound calls in the inbound call history data base 16. The outcome of inbound calls is also gathered and stored along with forecasted outcomes to provide a detailed call-by-call history for use in future modeling and for verification of forecasted outcome versus actual outcome. For instance, once an inbound call is completed, results such as a successful connect with an agent, an abandoned call, a purchase, or customer attrition from billing records are associated with inbound calls.
  • Modeling module 14 creates caller models by performing statistical analysis on appropriate data taken from inbound call history data base 16 and account information data base 18.
  • the statistical analysis performed by modeling module 14 builds models by associating the outcome of a call (i.e., the dependent variable) to the information available when the call is received (i.e., the independent variables)
  • the end result of each model is equations that when computed provide a forecast for the outcome of interest (e.g., agent talk time, sale: yes/no, account cancelled within x days: yes/no) .
  • the application of caller models to caller and/or call information may be performed as calls arrive, or may be performed preemptively to calculate potential scores in the beginning of a time period to provide more rapid response when circumstances warrant .
  • One type of statistical analysis appropriate for modeling discrete outcomes is logistic regression.
  • Some examples of forecasted outcomes include estimating probability an inbound caller will hang up in a predetermined hold time, the probability a customer will cancel an account, or the probability the customer will make a purchase.
  • linear regression Another type of statistical analysis appropriate for modeling continuous outcomes, such as talk time or sale amount, is linear regression. For example, the following linear regression equation forecasts agent talk time
  • TOB time-of-day between 8-9 am
  • BAL delinquency level
  • DL delinquency level
  • TT b 0 +b ⁇ TOB+b 2 TODflag+b 3 BAL+b 4 DL
  • b 0 a constant representing the model's intercept
  • b x the parameter for the predictive variable TOB
  • b 3 the parameter for the predictive variable
  • BAL b 4 the parameter for the predictive variable DL
  • statistical models that forecast outcomes may be developed by a number of alternative techniques.
  • neural networks, classification and regression trees (CART) , and Chi squared automatic detection (CHAID) are statistical techniques for modeling both discrete and continuous dependent variables.
  • cluster analysis Another example is cluster analysis, which, with an association of the resulting cluster assignment equations to the dependent variables allows for simplified models or may be used to improve the effectiveness of other techniques.
  • Each alternative statistical technique will result in different forecasting equations which may have advantages for different types of forecasting circumstances.
  • each type of equation will associate an outcome as a dependent variable with the call and caller information available while the call is processed as independent variables .
  • Estimate of dependent variable g(x(l), x(2) , ...x (N) ) where : x(i) stands for the ith independent variable, and g(x(l), x(2) ,...x(N) ) stands for the modeling equation, and can take different forms depending upon the statistical technique chosen.
  • Forecasted outcomes and predictive variables are user defined, and depend on the inbound inquiries being scheduled.
  • the outcome may be: yes/no/hang-up; amount of purchase (continuous) ; amount by type of product (continuous) split by product type; approval of a credit application yes/no.
  • exemplary outcomes may be: customer satisfaction yes/no; closure of account within x days yes/no; change in loan balance within x days (continuous) ; or dispute with a positive resolution/ dispute with a negative resolution/ no dispute.
  • agent talk-time continuous
  • agent talk time by type of agent continuous split by agent type, such as general/supervisor/specialist
  • the selection of predictive variables depends upon the type of data available and the circumstances of the outcome which is being forecasted. For example, in a situation in which the inquiries come from individuals known to the calling center, data available for predicting outcomes may include: account information; application information, such as employment, age, income, bank account information; relationship data such as other account information; results of other modeling efforts, such as behavior and response scores; credit bureau data; check clearing data; e-mail domain information; and trigger events, such as solicitations, TV advertisements, and account statements .
  • Data available from a call itself may include information input through a VRU, including branch sequence and initial number called, and the time at the place of the origination of the inbound inquiry.
  • the call environment itself may provide data based on the types and number of calls received in a recent period of time, the type and number within a period of time, such as a particular hour or day, and the results provided by the calls.
  • the call evaluation sub-module computes the priority value.
  • the priority value of a call might be the estimated probability of a purchase. Inbound calls having higher probabilities of purchase may be answered first.
  • the priority value of a call might be given by dividing the estimated probability of purchase by the expected talk time of the call. The most productive calls are given are given a greater priority value for response by an agent. In this way, agent productivity is implicitly improved since a greater portion of the agent ' s time is spent talking with potential customers having a higher probability of making a purchase .
  • scheduling module 12 orders inbound inquiries to explicitly optimize a desired outcome, such as a maximum number of purchases or a minimum number or losses due to attrition, taking into account the limitations of the environment operating at the time.
  • Quantities of interest such as probability of a sale, probability of attrition, or expected talk time, are estimated with models generated by modeling module 14. The estimated quantities of interest are used to solve a constrained optimization problem with conventional mathematical techniques, such as the simplex method for linear problems or the Conjugate gradient and Projected Lagrangian techniques for Non-linear problems.
  • P ⁇ (i) is the estimate for the probability of attrition for the caller's account if the call is not answered
  • p 2 (i) is the estimate for the probability of attrition for the caller's account if the call is answered
  • t(i) is the estimate of the expected talk-time for call i
  • T is the total available Agent time for a user-defined time interval
  • N is the number of calls in queue
  • FIGURE 1 depicts an embodiment of the present invention that orders inbound telephone calls
  • the call priority value may be given by the reduced objective value: q(i) - R*t (i) .
  • FIGURE 1 depicts an embodiment of the present invention that orders inbound telephone calls
  • alternative embodiments apply scheduling module 12 and modeling module 14 to schedule other types of inbound inquiries, such as e-mail or instant message inquiries, by interfacing inbound scheduling system 10 with an appropriate inbound receiving device, such as an internet server.
  • the scheduling module may be receiving inbound inquiries from a plurality of sources (e.g. ACD, VRU, internet server) and returning priority values to unified or separate pools of agents..
  • sources e.g. ACD, VRU, internet server
  • a flow diagram depicts a process for scheduling inbound calls for response by an agent.
  • the process begins at step 30 with the building of models from inbound call history.
  • the inbound call history used to model the outcomes of interest may be a sample drawn from historical inbound calls of the same nature as the outcomes to be modeled or may be specifically designed during a test phase. For instance, a television advertisement aired in a single or limited number of television markets representative of the total targeted audience may be used to generate inbound calls having a volume within the capacity constraints of the calling center.
  • the outcome of the inbound calls from the sample advertisement may then be used to create a model specific to the nature of the product sold by the advertisement.
  • the advertisement-specific model is then used for the time periods during which the advertisement is presented to wider audiences so that inbound calls having a greater probability of resulting in a purchase will have a higher priority for response by an agent.
  • inbound calls are received by the receiving device.
  • inbound calls arrive continuously at the receiving device at rates that vary over time.
  • the receiving device answers the inbound calls in a conventional manner and, at step 34, determines call and/or caller information.
  • Call and/or caller information is determined through analysis of ANI or DNIS information that arrives with inbound calls and also through data gathering such as by interaction with a VRU.
  • call and/or caller information is provided to the scheduling module for a determination of a priority value based on the forecasted outcome of the inbound call.
  • the scheduling module determines if additional information is needed for calculation of the outcome forecast. For instance, account information may be acquired by the receiving device and passed to the scheduling module, or the scheduling module can acquire all or part of the information. If additional information is needed, at step 40, caller information is used to obtain additional account or demographic information.
  • the caller model is applied to caller information, account information and/or demographic information to determine a priority value for the inbound call.
  • the receiving device sorts queues according to the priority value, reducing or eliminating the need for a virtual hold by the release of calls from the scheduling module. For instance, a linked list for receiving devices that support lined list data structures may be used to aid in the scheduling of inbound calls.
  • inbound calls are scheduled for response by an agent interfaced with the receiving device.
  • Inbound calls having lower priority values are placed on virtual hold while inbound calls having higher priority values are returned to the receiving device and placed in a queue for response by an agent.
  • the length of a virtual hold for an inbound call depends upon the volume of inbound calls, the capacity of the receiving device, the talk time of the agents per call and the priority value of an inbound call relative to other pending inbound calls. Based on these factors, an inbound call is placed in virtual hold time and is forwarded to the receiving device in priority value order when agent resources are available and/or when a maximize hold time parameter has been exceeded.
  • the receiving device can sort or change the order of an inbound queue based on available data including the priority value
  • the inbound queues of the receiving device may be re-ordered on a real-time basis as additional inquiries are received.
  • the outcome of inbound calls is stored in the inbound call history data base.
  • the inbound history data base tracks factors such as call success or abandonment and ultimate call outcome.
  • Call outcome may include directly quantifiable factors such as a purchase decision or less quantifiable factors such as customer satisfaction as reflected by account usage, cancellations and related information that is derivable from account databases and other sources.
  • One example of an application of the inbound scheduling system is a credit card service calling center. Customers tend to make inbound calls at similar times of the day which leads to longer hold times when inbound call volumes are high. Often, inbound callers hang up or simply just "silently" close their account when hold times are excessive for that caller.
  • the scheduling system enhances the overall benefit from inbound telephone calls by providing a higher priority to inbound calls that are forecasted to have a desired result, such as increased account usage. Further, the effectiveness may be tested with champion/challenger testing that compares results of subsets of inbound calls in which one segment is prioritized and the other segment is not prioritized or is prioritized with a different priority strategy.
  • agent response to inquiries may be via the same media as the inquiry or through cross- channel communication.
  • an e-mail inquiry may result in an e-mail response or, alternatively, in a telephone call response.
  • the priority of the response may depend, in part, on the media of the inquiry. For instance, generally an e-mail inquiry will have a lower priority than a telephone inquiry since a customer generally will not expect as rapid of a response when the customer sends an e-mail inquiry.
  • an immediate response by a telephone call might provide a better sales outcome for an agent's time, even if a telephone inquiry with a customer having a low probability of purchase is left on hold while the agent places an outbound call.
  • low priority inquiries such as inquiries with a low probability of purchase
  • a low priority inbound telephone caller may be given a voice message that informs the caller of an excess wait time and that he will be contacted at a future time.
  • the future time is determined by the caller's priority compared with the actual and projected priority of other inbound inquiries and the capacity of the agents to respond to the inquires.
  • the capacity of the available agents is projected to exceed inbound inquiry demand and higher priority inquiry backlog in two hours, the low priority inbound caller may be given a message to expect a call in two hours.
  • an automated e-mail message may be provided to an e-mail inquiry informing the e-mail inquirer that he may expect a response at a specific time.
  • inquiries are scheduled for outbound contact attempts on a prioritized basis rather than on a first-in-first-out basis.
  • the inquirer may be prompted for the best time and communication channel, and an outbound contact attempt will be attempted at that time.

Abstract

A method and system (10) for schedules inbound inquiries (22) for response by agents (26) in an order that is based in part on the forecasted outcome of the inbound inquiries. A scheduling module (12) applies inquiry information to a model to forecast the outcome of an inbound inquiry. The forecasted outcome is used to set a priority value for ordering the inquiry. The priority value may be determined by solving a constrained optimization problem that seeks to maximize an agent's (26) productivity to produce sales or to minimize inbound call attrition. The inquiry outcomes are forecasted based on a history stored in inbound call history data base (16) and inquiry information stored in account information data base (18). Statistical analysis such as regression analysis determines the model with the outcome related to the nature of the inquiry.

Description

METHOD AND SYSTEM FOR SCHEDULING INBOUND INQUIRIES
TECHNICAL FIELD
This invention relates in general to the fields of telephony and computer networks, and more particularly to a method and system for scheduling inbound inquires made by telephone or by other electronic messages.
BACKGROUND OF THE INVENTION
Telephone calling centers represent the front iine for customer service and marketing operations of many businesses. Typical calling centers receive or make hundreds of telephone calls per day with the aid of automated telephony equipment . With the Internet growing in importance as a way of communicating with customers, calling centers have also evolved to send and respond to electronic messages, such as e-mail or instant messages.
Calling centers often play a dual role of both sending outbound inquiries and answering inbound inquiries. For instance, calling centers use predictive dialers that automatically dial outbound telephone calls to contact individuals and then transfer the contacted individuals to agents when the individual answers the phone. Inbound telephone calls by individuals to the calling center are received by telephony equipment in the calling center and distributed to agents as the agents become available. Calling centers often combine outbound and inbound functions as a way to improve the talk time efficiency of calling center agents. Thus, for instance, when inbound calls have expected hold times that are acceptable, agents may be reassigned to place outbound telephone calls to help ensure that the agents are fully occupied. One important goal for calling centers that receive inbound inquiries, such as telephone calls or electronic message inquiries, is to transfer the inbound inquiries to appropriate agents as quickly and efficiently as possible. A variety of telephone call receiving devices are commercially available to help meet this goal. One such receiving device is an automatic call distribution system ("ACD") that receives plural inbound telephone calls and then distributes the received inbound calls to agents based on agent skill set, information available about the caller, and rules that match inbound callers to desired queues. Inbound calls may be routed to different queues based on rules and data, allowing a basic prioritization of inbound calls. , For example, inbound callers seeking information about a new credit card account might be assigned to a different queue than inbound callers having questions about their account balances. Once assigned to a queue, calls in that queue are generally handled in a first-in-first-out basis. Thus, a caller's hold time generally depends upon the caller's depth in the queue.
Another type of call receiving device is a voice response unit ("VRU"), also known as an interactive voice response system. When an inbound call is received by a VRU, the caller is generally greeted with an automated voice that queries for information such as the caller's account number. Information provided by the caller is typically used to route the call to an appropriate queue. VRUs are used in conjunction with ACDs, but also improve performance of less complex receiving devices such as PBX systems .
As telephony migrates from conventional telephone signals to the use of Internet-based computer networks, voice over internet protocol ("VOIP") will become an increasingly common platform for handling inbound telephone calls. One advantage of VOIP is enhanced access to account information for inbound calls with improved speed and accuracy. For example, conventional ACD and VRU systems collect caller information when inbound calls are received. One example of such caller information is automated number identification ("ANI") information provided by telephone networks that identify the telephone number of the inbound call . Another example is destination number identification system information ("DNIS") which allows the purpose of the inbound call to be determined from the telephone number dialed by the inbound caller. Using this caller information and account information gathered by a VRU or
ACD, conventional calling centers are able to gather information on the caller and provide that information to ■ the agent . The use of VOIP improves the integration of data and telephony by passing both data and telephony through a network with internet protocol and by combining voice inquiries with electronic message inquiries, such as e-mail. One example of such integration is the Intelligent Contact Management ("ICM") solution sold by CISCO Systems, Inc. Another example is the integrated response systems available from eShare Technologies, described in greater detail at www.eShare.com. Although telephone receiving devices provide improved distribution of inbound telephone calls to agents, the receiving devices are generally not helpful in managing hold times when the number of inbound calls exceeds the agent answering capacity. For instance, customers tend to make inbound calls for service at similar times. A large volume of inbound calls tends to lead to longer wait times during popular calling periods resulting in customer dissatisfaction. As a consequence, during periods of heavy volumes and long hold times, a greater number of inbound callers hang up or "silently" close their accounts by seeking other service providers with better service. Another example of excessive hold times affecting the behavior of inbound callers occurs with telemarketing. The volume of inbound calls in a marketing operation tends to increase dramatically shortly after a television advertisement is aired. Extended hold times result in a greater number of customer hang-ups and lost sales .
SUMMARY OF THE INVENTION
Therefore a need has arisen for a method and system which orders inbound inquiries, such as telephone calls, to improve the efficiency of responding to the inbound inquiries.
A further need exists for a method and system that forecasts the behavior of those making inbound inquiries, such as inbound telephone callers, to predict the outcome of an inbound inquiry. A further need exists for a method and system that applies the forecasted behavior of those making inbound inquiries, such as inbound telephone callers, to order the inbound inquiries for response by agents.
A further need exists for a method and system that solves for an optimum ordering sequence for responding to inbound inquiries .
In accordance with the present invention, a method and system for ordering inbound inquiries is provided that substantially eliminates or reduces disadvantages and problems associated with previously developed methods and systems for responding to inbound inquiries. Inbound inquiry information associated with each inbound inquiry is applied to a model to determine a priority value for ordering the inbound inquiry for response relative to other inbound inquiries. More specifically, inbound inquiries may include inbound telephone calls, e-mails, instant messages, or other electronic messages formats, such as those available through the internet . In an embodiment for scheduling inbound telephone calls, a telephone call receiving device receives plural inbound telephone calls for distribution to one or more agents. The telephone call receiving device may include an ACD, a VRU, a PBX, a VOIP server or any combination of such devices that are operable to receive plural inbound telephone calls and redirect the inbound telephone calls to one or more agents. The inbound telephone calls have associated caller information, such as ANI or DNIS information, which the receiving device interprets. ANI information identifies the telephone number from which the inbound call originates, and DNIS information identifies the telephone number to which the inbound call was directed. A scheduling module interfaced with or integrated within the receiving device determines an order for the handling of inbound telephone calls based in part on the predicted outcome of the inbound telephone calls. In one embodiment, the scheduling module places the inbound calls in a queue, the queue acting as a virtual hold, and applies a caller model to the caller information associated with the inbound calls in order to forecast the predicted outcome of the inbound calls. The order for handling the inbound calls is based on a priority value calculated from the application of a caller model to the caller information by a call evaluation sub-module and based on the capacity of the receiving device. As calls are scheduled by the scheduling module for handling by the receiving device, the scheduling module releases the inbound calls from the virtual hold queue and places the inbound calls in the queue of the receiving device. In an alternative embodiment, the scheduling module or the receiving device may perform real-time scheduling of inbound call inventory by re-ordering queues of the receiving device based on the priority value.
The call evaluation sub-module uses algorithms and models provided by a modeling module that analyzes inbound call histories to forecast outcomes of pending inbound calls. It utilizes the forecasts to compute priority values. For example, in the modeling module, performing logistic regression on prior inbound calls using caller and/or call information and prior call history as independent (or predictive) variables and a dependent variable of caller attrition, provides a model that forecasts pending inbound caller attrition based on the caller and/or call information. Alternatively, performing linear regression modeling on prior inbound calls, using caller and/or call information as independent (or predictive) variables and a dependent variable of connect time, provides a model that forecasts the expected agent talk time for each incoming call.
Predictive variables for the logistic and linear regression equations may include call information such as the originating number or exchange, the originating location, the dialed number, the time of day and the likely purpose of the call. In addition, they may include caller information such as account information derived from association of the originating number and an account data base, or derived from data input by the inbound caller by a VRU. From caller information and/or call information, additional predictive variables are available for forecasting the outcome of the inbound call, including demographic information that may be associated with the call and/or caller.
In one embodiment, the call evaluation sub-module estimates one or more quantities of interest with one or more models provided by the modeling module, and computes the call's priority value based on the quantities of interest. For example, the call value of "the probability of a sale per minute of expected talk time" may be estimated by dividing the estimated probability of a sale by the estimated talk time.
In another embodiment, the call evaluation sub- module uses the estimated quantities of interest to formulate and solve a constrained optimization problem based on conventional mathematical techniques, such as the simplex method for linear problems or the Conjugate gradient and Projected Lagrangian techniques for Non- linear problems. For example, call evaluation sub-module may present a value that represents the solution to maximizing objectives such as agent productivity to either minimize attrition or to produce product sales. The present invention provides a number of important technical advantages. One important technical advantage is that inbound inquiries, such as inbound telephone calls, are ordered for response based at least in part on the predicted outcome of the inbound inquiries. This allows, for instance, agents to respond to customers that are more sensitive to holding time before responding to customers who are less sensitive to holding time. This also allows, as another example, enhanced efficiency of handling of inbound telephone calls by seeking to improve the overall outcomes of the inbound calls based on the forecasted outcomes. For instance, in a telemarketing environment, inbound callers with a higher likelihood of purchasing an item or service may be responded to before customers with a lower probability of a purchase outcome. In fact, computing estimated outcomes and then formulating and solving the appropriate constrained optimization problem provides an ordering sequence that maximizes purchases made by inbound callers responding to a television advertisement. Another important technical advantage of the present invention is that forecasted outcomes are available with minimal caller information. Generally the identity and purpose of inbound calls are difficult to discern because little information is available regarding the inbound caller. The use of statistical analysis of historical inbound calling data allows accurate modeling of outcomes with minimal knowledge of the identity and purpose of the inbound caller.
Another important technical advantage of the present invention is that inbound calls are prioritized based on caller and call information. The present invention allows flexible use in a number of inbound inquiry environments such as telemarketing and customer service environments. Caller models may have different predictive variables depending upon the modeled outcome and the caller information obtained with the inbound inquiry. For instance, telemarketing applications using models that forecast probability of a purchase may focus on predictive variables derived from demographic information based on the origination of the inbound call. In contrast, customer service applications using models that forecast caller attrition may have more detailed predictive variables derived from customer account information. Thus, inbound calling models and objectives may be closely tailored to a user's particular application. Also, estimates of the inbound call talk time may lead to constrained optimization solutions designed to maximize the use of the available agent talk time. Further, an overall response strategy that accounts for electronic message inquiries as well as telephone inquiries is more easily adopted.
BRIEF DESCRIPTION OF THE DRAWINGS
A more complete understanding of the present invention and advantages thereof may be acquired by referring to the following description taken in conjunction with the accompanying drawings, in which like reference numbers indicate like features, and wherein: FIGURE 1 depicts a block diagram of an inbound telephone call receiving device interfaced with an inbound scheduling system; and
FIGURE 2 depicts a flow diagram of a method for ordering inbound callers for response by agents.
DETAILED DESCRIPTION OF THE INVENTION
Preferred embodiments of the present invention are illustrated in the FIGURES, like numeral being used to refer to like and corresponding parts of the various drawings .
Under normal circumstances, inbound telephone calling centers maintain holding times for inbound callers within desired constraints by adjusting the response capacity of the calling center. For instance, during projected or actual periods of heavy inbound calling volume, additional agents may be assigned to respond to inbound calls by adding agents to the calling center or by reducing the number of outbound calls. However, once the overall capacity of a calling center is reached, inbound calls in excess of calling center capacity will generally result in increased holding times for the inbound callers.
Inadequate capacity to handle inbound calls may result from periodic increases in the number of inbound calls during popular calling times, or may result from one time surges due to factors such as system-wide customer service glitches or the effects of advertising. Generally, the excess inbound calls are assigned to hold for an available agent in queues of an inbound telephone call receiving device and are handled on a first-in first-out basis for each holding queue. Often, the result of excessive hold times is that customers having a greater sensitivity to long hold times will hang-up in frustration.
Responding to holding inbound callers on a first-in- first-out basis does not necessarily provide the most efficient results for a calling center. Agent time is used most efficiently when an agent is responding to inbound callers most likely to achieve a desired outcome. For instance, in a telemarketing role an agent is most productive when speaking with inbound callers likely to purchase the marketed service or product . Similarly, in a customer service role, an agent is most productive when speaking with inbound callers who provide a greater rate of profitability to the calling center. Thus, routing calls to agents on a first-in-first-out basis does not provide the most efficient use of agent time when inbound callers having a higher probability of a desired outcome are treated in the same manner as inbound callers having a lower probability of a desired outcome. The same principle applies when inbound inquiries are received in alternative formats, such as e-mail or instant messages.
Referring now to FIGURE 1, a block diagram depicts an inbound scheduling system 10 that schedules inbound telephone calls for response by agents in an order based in part on the predicted outcome of the inbound telephone calls. Inbound scheduling system 10 includes a scheduling module 12, a call evaluation sub-module 13, and a modeling module 14, and is interfaced with an inbound call history data base 16 and account information data base 18. Modeling module 14 builds one or more models that forecast the outcomes of inbound calls using inbound call history from data base 16 and/or from account information of data base 18. Scheduling module 12 applies the models to forecast outcomes of pending inbound calls and schedules an order for agents to respond to the pending inbound calls based on the call evaluation sub-module 13. Modeling module 14 builds statistical models and call evaluation sub-module 13 computes the priority value which is used by scheduling module 12. The priority value is the result of computations based on the models, but also of solutions to optimization problems that may be defined on computations based on the models.
Inbound scheduling system 10 interfaces with an inbound telephone call receiving device 20. Scheduling system 10 and receiving device 20 may be integrated in a single computing platform, or may be based on separate computing platforms interfaced with proprietary application programming interfaces of the receiving device 20 or interfaced with commercially available application middle ware such as Dialogic 's CT Connect or Microsoft's TAPI . Inbound telephone call receiving device 20 is a conventional telephony device that accepts inbound telephone calls through a telephony interface 22, such as conventional TI or fiber interfaces. Inbound telephone call receiving device 20 may include an ACD, a VRU, a PBX, a VOIP server or any combination of such conventional devices. Inbound telephone calls received through interface 22 are distributed to one or more answering queues 24 for response by agents operating telephony devices 26. Although FIGURE 1 depicts an embodiment of the present invention that orders inbound telephone calls, alternative embodiments apply scheduling module 12 and modeling module 14 to schedule other types of inbound inquiries, such as e-mail or instant message inquiries, by interfacing inbound scheduling system 10 with an appropriate inbound receiving device, such as an internet server. Inbound telephone call receiving device 20 accepts inbound telephone calls through interface 22 and obtains caller information associated with the inbound calls such as ANI and DNIS information. When receiving device 20 includes a VRU, additional caller information, such as account information, is obtained through automated interaction with the inbound callers. For instance, a VRU may query an inbound caller to provide an account number or a reason for the call, such as to open a new account, to change account information, to check account information, to purchase a particular service or item, or to collect inbound caller information when ANI is not operative, such as when caller-ID is blocked. In an alternative embodiment, inbound inquiries may include e- mail or instant messages that provide inquiry information based on login ID, e-mail address, IP or instant message address. In such an embodiment, additional information can be gathered by an automated e-mail or instant message survey response that requests a phone number, purchase interest, account number or other relevant information. Receiving device 20 passes the caller information to scheduling system 10, such as through a data query, and awaits a response from scheduling system 10 before allocating the inbound call to an answering queue. In addition, receiving device 20 provides scheduling system 10 with agent activity and capacity. For instance, a receiving device 20 may include both a VRU and an ACD with the ACD providing agent activity information. When receiving device 20 includes a VRU, an "out of order" response may be provided by scheduling system 10 when operator capacity is unavailable or in high use, meaning that the first call in is not necessarily the first call out .
Scheduling module 12 keeps inbound calls in a queue that acts as a virtual hold until a response is desired and then releases the inbound call for placement in an answering queue 24. Thus, scheduling system 10 responds to queries from receiving device 20 based on the priority of the inbound call, essentially creating an ordered queue on receiving device 20 by delaying the response to inbound calls having lower priorities. In one alternative embodiment, scheduling module 12 may re-order queues directly within receiving device 20 to allow realtime ordering of inbound telephone call queues.
Scheduling module 12 obtains data to apply to a caller model by performing a look-up based on the caller information received from receiving device 20. Caller information may include account number, zip code, area code, telephone exchange, reservation number or other pertinent information obtained from the inbound caller, such as with a VRU, or derived from information obtained by the receiving device 20 with the inbound call, such as ANI or DNIS information. The nature of caller information depends upon the implementation of scheduling system 10 and is generally configurable through a graphical user interface provided with conventional receiving devices. In addition to the caller information, scheduling module 12 may query and join data from other sources such as zip+4 and credit bureau sources and demographic information otherwise derivable from the caller information.
When sufficient capacity exists for response by receiving device 20, scheduling system 10 releases calls immediately back to receiving device 20. In other words inbound calls are not delayed if sufficient capacity exists to handle the inbound calls, but are routed for immediate answering. When capacity is tight on receiving device 20, calls are delayed on a virtual hold by scheduling system 10 until an appropriate time based on the priority value computed by the call evaluation sub- module 13. Whether or not inbound calls are delayed, scheduling system 10 gathers and stores data for the inbound calls in the inbound call history data base 16. The outcome of inbound calls is also gathered and stored along with forecasted outcomes to provide a detailed call-by-call history for use in future modeling and for verification of forecasted outcome versus actual outcome. For instance, once an inbound call is completed, results such as a successful connect with an agent, an abandoned call, a purchase, or customer attrition from billing records are associated with inbound calls.
Modeling module 14 creates caller models by performing statistical analysis on appropriate data taken from inbound call history data base 16 and account information data base 18. The statistical analysis performed by modeling module 14 builds models by associating the outcome of a call (i.e., the dependent variable) to the information available when the call is received (i.e., the independent variables) The end result of each model is equations that when computed provide a forecast for the outcome of interest (e.g., agent talk time, sale: yes/no, account cancelled within x days: yes/no) . The application of caller models to caller and/or call information may be performed as calls arrive, or may be performed preemptively to calculate potential scores in the beginning of a time period to provide more rapid response when circumstances warrant .
One type of statistical analysis appropriate for modeling discrete outcomes (e.g., sale: yes/no, account cancelled within x days: yes/no) is logistic regression. Some examples of forecasted outcomes include estimating probability an inbound caller will hang up in a predetermined hold time, the probability a customer will cancel an account, or the probability the customer will make a purchase. As an example, the following logistic regression equation forecasts the probability of purchase based on the independent variables income and age: exp (a0+aι*age+a2*income) / [1+exp (a0+aι*age+a2*income) ] where : a0 = a constant representing the model ' s intercept ax = the parameter for the predictive variable age a2 = the parameter for the predictive variable income
Another type of statistical analysis appropriate for modeling continuous outcomes, such as talk time or sale amount, is linear regression. For example, the following linear regression equation forecasts agent talk time
("TT") based on independent variables time-on-books
("TOB") , time-of-day between 8-9 am ("TOD"), balance
("BAL") and delinquency level ("DL") :
TT=b0+bιTOB+b2TODflag+b3BAL+b4DL b0 = a constant representing the model's intercept bx = the parameter for the predictive variable TOB b2 = the parameter for the predictive variable TOD (i.e., Was the call between 8-9 (l=yes, 2=no) ) b3 = the parameter for the predictive variable BAL b4 = the parameter for the predictive variable DL
In alternative embodiments, statistical models that forecast outcomes may be developed by a number of alternative techniques. For instance, neural networks, classification and regression trees (CART) , and Chi squared automatic detection (CHAID) are statistical techniques for modeling both discrete and continuous dependent variables. Another example is cluster analysis, which, with an association of the resulting cluster assignment equations to the dependent variables allows for simplified models or may be used to improve the effectiveness of other techniques. Each alternative statistical technique will result in different forecasting equations which may have advantages for different types of forecasting circumstances.
Essentially, however, each type of equation will associate an outcome as a dependent variable with the call and caller information available while the call is processed as independent variables . In general mathematical terms, for each possible discrete outcome, such as sale: yes/no, account cancelled within x days: yes/no, where i=l,...M: Prob(outcome=i) =fi(x(l) , x (2) ,...x(N) ) where : x(i) stands for the ith independent variable, and fi(x(l), x(2) ,...x(N) ) stands for the modeling equation for outcome i and can take different forms depending upon the statistical technique chosen
For each continuous outcome, such as talk-time or amount of sale: Estimate of dependent variable = g(x(l), x(2) , ...x (N) ) where : x(i) stands for the ith independent variable, and g(x(l), x(2) ,...x(N) ) stands for the modeling equation, and can take different forms depending upon the statistical technique chosen.
Forecasted outcomes and predictive variables are user defined, and depend on the inbound inquiries being scheduled. As an example, for inbound inquiries related to a solicitation effort, such as telephone calls following a TV advertisement, the outcome may be: yes/no/hang-up; amount of purchase (continuous) ; amount by type of product (continuous) split by product type; approval of a credit application yes/no. As another example, for customer service inquiries, exemplary outcomes may be: customer satisfaction yes/no; closure of account within x days yes/no; change in loan balance within x days (continuous) ; or dispute with a positive resolution/ dispute with a negative resolution/ no dispute. Other types of outcomes that may be of interest to both post-solicitation and customer service inquiries include: agent talk-time (continuous); agent talk time by type of agent (continuous split by agent type, such as general/supervisor/specialist) . The selection of predictive variables depends upon the type of data available and the circumstances of the outcome which is being forecasted. For example, in a situation in which the inquiries come from individuals known to the calling center, data available for predicting outcomes may include: account information; application information, such as employment, age, income, bank account information; relationship data such as other account information; results of other modeling efforts, such as behavior and response scores; credit bureau data; check clearing data; e-mail domain information; and trigger events, such as solicitations, TV advertisements, and account statements . When geographic location of the call or caller can be established, this may yield additional predictive data, such as zip+4 credit bureau information, census demographics, and third party models, such as credit bureau clusters . Data available from a call itself may include information input through a VRU, including branch sequence and initial number called, and the time at the place of the origination of the inbound inquiry. In addition, the call environment itself may provide data based on the types and number of calls received in a recent period of time, the type and number within a period of time, such as a particular hour or day, and the results provided by the calls.
Once the modeling equations are applied and outcomes such as probability of purchase or expected talk time are estimated, the call evaluation sub-module computes the priority value. In one embodiment of the invention, the priority value of a call might be the estimated probability of a purchase. Inbound calls having higher probabilities of purchase may be answered first. In another embodiment, the priority value of a call might be given by dividing the estimated probability of purchase by the expected talk time of the call. The most productive calls are given are given a greater priority value for response by an agent. In this way, agent productivity is implicitly improved since a greater portion of the agent ' s time is spent talking with potential customers having a higher probability of making a purchase .
In another embodiment of the invention, scheduling module 12 orders inbound inquiries to explicitly optimize a desired outcome, such as a maximum number of purchases or a minimum number or losses due to attrition, taking into account the limitations of the environment operating at the time. Quantities of interest, such as probability of a sale, probability of attrition, or expected talk time, are estimated with models generated by modeling module 14. The estimated quantities of interest are used to solve a constrained optimization problem with conventional mathematical techniques, such as the simplex method for linear problems or the Conjugate gradient and Projected Lagrangian techniques for Non-linear problems.
One example of optimization applied to inbound telephone calls is the maximization of agent productivity to minimize attrition of inbound callers, as illustrated by the following equation: Max sum x(i) * (p2 (i) -pi (i) ) i=l,...N Subject to: sum x(i) *t (i) =< T i=l,..JT x(i) in (0,1) where : x(i) (the decision variable) denotes whether call i should be kept or dropped
Pι(i) is the estimate for the probability of attrition for the caller's account if the call is not answered p2(i) is the estimate for the probability of attrition for the caller's account if the call is answered t(i) is the estimate of the expected talk-time for call i
T is the total available Agent time for a user-defined time interval
N is the number of calls in queue Once the constrained optimization problem is solved, letting Q be the optimal dual variable for the talk-time constraint, the call priority value may be given by the reduced objective value: p2(i)-pι(i) - Q*t (i) .
Another1 example of optimization applied to inbound telephone calls is the maximization of agent productivity to produce sales to inbound callers, as illustrated by the following equation:
Max sum x(i) *q(i) i=l,...N Subject to: sum x(i) *t (i) =< T i=l,...N x(i) in (0,1) where : x(i) (the decision variable) denotes whether call i should be kept or dropped q(i) is the estimate for the probability that the call will result in a sale t(i) is the estimate of the expected talk-time for call i T is the total available Agent time for a user-defined time interval
N is the number of calls in queue. Once the constrained optimization problem is solved, letting R be the optimal dual variable for the talk time constraint, the call priority value may be given by the reduced objective value: q(i) - R*t (i) . Although FIGURE 1 depicts an embodiment of the present invention that orders inbound telephone calls, alternative embodiments apply scheduling module 12 and modeling module 14 to schedule other types of inbound inquiries, such as e-mail or instant message inquiries, by interfacing inbound scheduling system 10 with an appropriate inbound receiving device, such as an internet server. The scheduling module may be receiving inbound inquiries from a plurality of sources (e.g. ACD, VRU, internet server) and returning priority values to unified or separate pools of agents..
Referring now to FIGURE 2, a flow diagram depicts a process for scheduling inbound calls for response by an agent. The process begins at step 30 with the building of models from inbound call history. The inbound call history used to model the outcomes of interest may be a sample drawn from historical inbound calls of the same nature as the outcomes to be modeled or may be specifically designed during a test phase. For instance, a television advertisement aired in a single or limited number of television markets representative of the total targeted audience may be used to generate inbound calls having a volume within the capacity constraints of the calling center. The outcome of the inbound calls from the sample advertisement may then be used to create a model specific to the nature of the product sold by the advertisement. The advertisement-specific model is then used for the time periods during which the advertisement is presented to wider audiences so that inbound calls having a greater probability of resulting in a purchase will have a higher priority for response by an agent.
At step 32, inbound calls are received by the receiving device. Generally, inbound calls arrive continuously at the receiving device at rates that vary over time. The receiving device answers the inbound calls in a conventional manner and, at step 34, determines call and/or caller information. Call and/or caller information is determined through analysis of ANI or DNIS information that arrives with inbound calls and also through data gathering such as by interaction with a VRU.
At step 36, call and/or caller information is provided to the scheduling module for a determination of a priority value based on the forecasted outcome of the inbound call. At step 38, the scheduling module determines if additional information is needed for calculation of the outcome forecast. For instance, account information may be acquired by the receiving device and passed to the scheduling module, or the scheduling module can acquire all or part of the information. If additional information is needed, at step 40, caller information is used to obtain additional account or demographic information. At step 42, the caller model is applied to caller information, account information and/or demographic information to determine a priority value for the inbound call. At step 43, in one embodiment, the receiving device sorts queues according to the priority value, reducing or eliminating the need for a virtual hold by the release of calls from the scheduling module. For instance, a linked list for receiving devices that support lined list data structures may be used to aid in the scheduling of inbound calls.
At step 44, inbound calls are scheduled for response by an agent interfaced with the receiving device. Inbound calls having lower priority values are placed on virtual hold while inbound calls having higher priority values are returned to the receiving device and placed in a queue for response by an agent. The length of a virtual hold for an inbound call depends upon the volume of inbound calls, the capacity of the receiving device, the talk time of the agents per call and the priority value of an inbound call relative to other pending inbound calls. Based on these factors, an inbound call is placed in virtual hold time and is forwarded to the receiving device in priority value order when agent resources are available and/or when a maximize hold time parameter has been exceeded. Alternatively, in embodiments in which the receiving device can sort or change the order of an inbound queue based on available data including the priority value, the inbound queues of the receiving device may be re-ordered on a real-time basis as additional inquiries are received.
At step 46, the outcome of inbound calls is stored in the inbound call history data base. The inbound history data base tracks factors such as call success or abandonment and ultimate call outcome. Call outcome may include directly quantifiable factors such as a purchase decision or less quantifiable factors such as customer satisfaction as reflected by account usage, cancellations and related information that is derivable from account databases and other sources. One example of an application of the inbound scheduling system is a credit card service calling center. Customers tend to make inbound calls at similar times of the day which leads to longer hold times when inbound call volumes are high. Often, inbound callers hang up or simply just "silently" close their account when hold times are excessive for that caller. Other customers are less sensitive to hold times and thus less likely to alter their purchasing habits or account status as a factor of hold times. The scheduling system enhances the overall benefit from inbound telephone calls by providing a higher priority to inbound calls that are forecasted to have a desired result, such as increased account usage. Further, the effectiveness may be tested with champion/challenger testing that compares results of subsets of inbound calls in which one segment is prioritized and the other segment is not prioritized or is prioritized with a different priority strategy.
Another example of the present invention is an application for an integrated response center that simultaneously accepts inquiries from different types of communication media, such as simultaneous inquires from telephone calls, VOIP, e-mails and instant messages. In such an environment, agent response to inquiries may be via the same media as the inquiry or through cross- channel communication. For instance, an e-mail inquiry may result in an e-mail response or, alternatively, in a telephone call response. Further, the priority of the response may depend, in part, on the media of the inquiry. For instance, generally an e-mail inquiry will have a lower priority than a telephone inquiry since a customer generally will not expect as rapid of a response when the customer sends an e-mail inquiry. However, if the customer who sent the e-mail inquiry has a high probability of purchase, an immediate response by a telephone call might provide a better sales outcome for an agent's time, even if a telephone inquiry with a customer having a low probability of purchase is left on hold while the agent places an outbound call.
In a highly constrained resource environment, particularly low priority inquiries, such as inquiries with a low probability of purchase, may be scheduled for outbound attempts at a later time in order to preserve response resources for higher priority inquiries. For instance, a low priority inbound telephone caller may be given a voice message that informs the caller of an excess wait time and that he will be contacted at a future time. The future time is determined by the caller's priority compared with the actual and projected priority of other inbound inquiries and the capacity of the agents to respond to the inquires. Thus, if the capacity of the available agents is projected to exceed inbound inquiry demand and higher priority inquiry backlog in two hours, the low priority inbound caller may be given a message to expect a call in two hours. Similarly, an automated e-mail message may be provided to an e-mail inquiry informing the e-mail inquirer that he may expect a response at a specific time. In this way, inquiries are scheduled for outbound contact attempts on a prioritized basis rather than on a first-in-first-out basis. In one alternative embodiment, the inquirer may be prompted for the best time and communication channel, and an outbound contact attempt will be attempted at that time. Although the present invention has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

WHAT IS CLAIMED IS:
1. A method for ordering inbound inquiries, the method comprising: receiving plural inbound inquiries, each inbound inquiry having associated inquiry information; applying a model to the inquiry information to determine a priority value for each inquiry; and ordering the inbound inquiries with the priority values .
2. The method of Claim 1 wherein the method inquiries comprise e-mail messages.
3. The method of Claim 1 wherein the method inquiries comprise instant messages.
4. The method of Claim 1 wherein the inbound inquiries comprise inbound telephone calls having associated caller information.
5. The method of Claim 4 wherein the caller information comprises automatic number identification information.
6. The method of Claim 4 wherein the caller information comprise destination number identification information.
7. The method of Claim 4 further comprising: gathering the caller information with a voice response unit.
8. The method of Claim 4 further comprising: associating demographic information with each inbound telephone call based on the caller information of the inbound call; and applying the model to the caller information to determine the priority value for each telephone call.
9. The method of Claim 4 wherein the model predicts caller behavior.
10. The method of Claim 9 wherein the priority value comprises a probability that the telephone call will result in a purchase.
11. The method of Claim 9 wherein the priority value comprises a probability that the caller associated with the telephone call will terminate the call after a hold time.
12. The method of Claim 1 further comprising: developing plural models from a history of inbound inquiries to forecast plural outcomes that determine the priority value.
13. The method of Claim 12 wherein developing the model further comprises : applying regression analysis to the history to calculate the priority value.
14. The method of Claim 12 further comprising: determining the outcomes of the plural inbound inquiries; and updating the history with the outcomes of the plural inbound inquiries.'
15. The method of Claim 12 wherein developing the caller model further comprises: updating the model with the updated history.
16. A method for determining inbound telephone call priority, the method comprising: developing one or more models from a history of inbound calls, the history having caller information and outcome results from inbound telephone calls; applying the model to caller information of a pending inbound call to predict an outcome of the pending inbound call; and associating a priority with the pending inbound call, the priority based on the predicted outcome.
17. The method of Claim 16 wherein the caller information comprises telephony information received with the pending inbound call.
18. The method of Claim 17 wherein the telephony information comprises automatic number identification information.
19. The method of Claim 17 wherein the telephony information comprises destination number identification information.
20. The method of Claim 17 wherein the caller information further comprises account information, the method further comprising: obtaining account information for the pending inbound call, the account information stored in a database by association with the telephony information.
21. The method of Claim 17 wherein the telephony information further comprises information input by the caller through a voice response unit.
22. The method of Claim 21 further comprising: obtaining account information for the pending inbound call based on the telephony information.
23. The method of Claim 16 wherein developing a model further comprises: using the caller information as predictive variables that model outcome results.
24. The method of Claim 23 wherein the model comprises a logistic regression model.
25. The method of Claim 23 wherein the model comprises a linear regression model.
26. The method of Claim 16 further comprising: placing the pending inbound call in the queue of an automatic call distribution system in an order based on the priority of the pending inbound call .
27. The method of Claim 26 wherein the predicted outcome comprises a purchase resulting from the pending inbound call.
28. The method of Claim 26 wherein the predicted outcome comprises the hold time of the pending inbound call .
29. The method of Claim 16 wherein associating a priority further comprises optimizing the order for the inbound telephone calls.
30. The method of Claim 29 wherein optimizing the order comprises solving a constrained optimization problem using one or estimates from one or more models.
31. The method of Claim 29 wherein optimizing further comprises maximizing agent productivity to minimize caller attrition.
32. The method of Claim 29 wherein optimizing further comprises maximizing agent productivity to produce sales.
33. A system for scheduling inbound calls, the system comprising: a receiving device operable to receive plural inbound inquiries and to provide the inbound inquiries to one or more agents; a scheduling module interfaced with the receiving device, the scheduling model operable to order the inbound inquiries for handling by the receiving device, the order based in part on the predicted outcome of the inbound inquiries.
34. The system of Claim 33 wherein the inbound inquiries comprise inbound telephone calls.
35. The system of Claim 33 wherein the receiving device comprises an automatic call distribution system.
36. The system of Claim 33 wherein the receiving device comprises a server that supports voice over internet protocol.
37. The system of Claim 33 wherein the receiving device comprises a voice response unit.
38. The system of Claim 34 further comprising: an inbound call history data base operable to store outcome results and caller information from plural completed inbound calls; and a modeling module interfaced with the history database and operable to model inbound call outcomes from the stored outcome results and caller information.
39. A system for responding to inbound calls, the system comprising: a telephone call receiving device interfaced with a network to receive plural inbound calls; and a scheduling system associated with the receiving device and having a scheduling module that prioritizes the inbound calls in accordance with forecasted outcomes for the inbound calls; wherein the scheduling system places one or more inbound calls on hold and then releases the inbound call from hold based on the priority of the inbound call .
40. The system of Claim 39 wherein the telephone call receiving device comprises an automatic call distribution system that incorporates the scheduling system.
41. The system of Claim 39 wherein the scheduling system forecasts outcomes with a model derived from a history of inbound calls.
42. The system of Claim 39 wherein the scheduling system orders the inbound calls to optimize an objective function.
43. The system of Claim 42 wherein the objective function comprises agent productivity to minimize inbound call attrition.
44. A method for ordering inbound inquiries, the method comprising: receiving plural inbound inquiries, from plural inquiry media, each inbound inquiry having associated inquiry information; applying the inquiry information to one or more models to determine a priority value for each inquiry; and ordering the inbound inquiries with the priority values.
45. The method of Claim 44 wherein the plural media comprise telephone calls and e-mail messages.
46. The method of Claim 45 wherein the plural media further comprise instant messages.
47. The method of Claim 45 wherein the plural media further comprise voice of internet protocol.
48. The method of Claim 44 further comprising: scheduling one or more inbound inquiries for an outbound contact attempt at a time based on the priority of the inbound inquiry.
49. The method of Claim 48 further comprising: informing the inbound inquirer of the time of the outbound contact attempt .
50. The method of Claim 44 further comprising: asking the inbound inquirer for a channel and time response; and scheduling a response at the channel and time.
PCT/US2001/011311 2000-04-12 2001-04-06 Method and system for scheduling inbound inquiries WO2001080538A1 (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1459189A1 (en) * 2001-12-28 2004-09-22 Simdesk Technologies, Inc. Instant messaging system
WO2014053017A1 (en) * 2012-10-03 2014-04-10 Iselect Ltd Systems and methods for use in marketing

Families Citing this family (208)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6104802A (en) 1997-02-10 2000-08-15 Genesys Telecommunications Laboratories, Inc. In-band signaling for routing
US7031442B1 (en) 1997-02-10 2006-04-18 Genesys Telecommunications Laboratories, Inc. Methods and apparatus for personal routing in computer-simulated telephony
US6480600B1 (en) 1997-02-10 2002-11-12 Genesys Telecommunications Laboratories, Inc. Call and data correspondence in a call-in center employing virtual restructuring for computer telephony integrated functionality
US6985943B2 (en) 1998-09-11 2006-01-10 Genesys Telecommunications Laboratories, Inc. Method and apparatus for extended management of state and interaction of a remote knowledge worker from a contact center
US6711611B2 (en) 1998-09-11 2004-03-23 Genesis Telecommunications Laboratories, Inc. Method and apparatus for data-linking a mobile knowledge worker to home communication-center infrastructure
USRE46528E1 (en) 1997-11-14 2017-08-29 Genesys Telecommunications Laboratories, Inc. Implementation of call-center outbound dialing capability at a telephony network level
US7907598B2 (en) 1998-02-17 2011-03-15 Genesys Telecommunication Laboratories, Inc. Method for implementing and executing communication center routing strategies represented in extensible markup language
US6332154B2 (en) 1998-09-11 2001-12-18 Genesys Telecommunications Laboratories, Inc. Method and apparatus for providing media-independent self-help modules within a multimedia communication-center customer interface
US6785710B2 (en) * 1998-06-22 2004-08-31 Genesys Telecommunications Laboratories, Inc. E-mail client with programmable address attributes
USRE46153E1 (en) 1998-09-11 2016-09-20 Genesys Telecommunications Laboratories, Inc. Method and apparatus enabling voice-based management of state and interaction of a remote knowledge worker in a contact center environment
US7103167B2 (en) 2002-05-20 2006-09-05 Callwave, Inc. Systems and methods for call screening
US6445788B1 (en) * 1999-06-17 2002-09-03 Genesys Telecommunications Laboratories, Inc. Method and apparatus for providing fair access to agents in a communication center
US7536002B1 (en) * 1999-07-09 2009-05-19 Jpmorgan Chase Bank, National Association System and method of intelligent call routing for cross sell offer selection based on optimization parameters or account-level data
US7929978B2 (en) 1999-12-01 2011-04-19 Genesys Telecommunications Laboratories, Inc. Method and apparatus for providing enhanced communication capability for mobile devices on a virtual private network
US7324635B2 (en) 2000-05-04 2008-01-29 Telemaze Llc Branch calling and caller ID based call routing telephone features
US7142662B2 (en) 2000-07-11 2006-11-28 Austin Logistics Incorporated Method and system for distributing outbound telephone calls
US7103173B2 (en) 2001-07-09 2006-09-05 Austin Logistics Incorporated System and method for preemptive goals based routing of contact records
AU2001281122A1 (en) * 2000-08-05 2002-02-18 Okraa, Llc System and method for aligning data
JP2002297900A (en) * 2001-03-30 2002-10-11 Ibm Japan Ltd Control system for reception by businesses, user side terminal device, reception side terminal device, management server queue monitoring device, method of allocating reception side terminals, and storage medium
US20040240642A1 (en) * 2001-06-18 2004-12-02 Crandell Jeffrey L. Apparatus, systems and methods for managing incoming and outgoing communication
US7054434B2 (en) 2001-07-09 2006-05-30 Austin Logistics Incorporated System and method for common account based routing of contact records
US7715546B2 (en) 2001-07-09 2010-05-11 Austin Logistics Incorporated System and method for updating contact records
US7184992B1 (en) * 2001-11-01 2007-02-27 George Mason Intellectual Properties, Inc. Constrained optimization tool
US7167551B2 (en) * 2001-12-12 2007-01-23 International Business Machines Corporation Intermediary device based callee identification
US9088645B2 (en) 2001-12-12 2015-07-21 International Business Machines Corporation Intermediary device initiated caller identification
US7103172B2 (en) * 2001-12-12 2006-09-05 International Business Machines Corporation Managing caller profiles across multiple hold queues according to authenticated caller identifiers
US7076051B2 (en) * 2001-12-12 2006-07-11 International Business Machines Corporation Promoting caller voice browsing in a hold queue
US7215759B2 (en) * 2001-12-12 2007-05-08 International Business Machines Corporation Hold queue wait estimations
US7245716B2 (en) 2001-12-12 2007-07-17 International Business Machines Corporation Controlling hold queue position adjustment
US7443970B2 (en) 2001-12-17 2008-10-28 International Business Machines Corporation Logging calls according to call context
US20040068421A1 (en) * 2002-04-16 2004-04-08 Georges Drapeau Patient station with integrated customer support
US7545925B2 (en) * 2002-12-06 2009-06-09 At&T Intellectual Property I, L.P. Method and system for improved routing of repair calls to a call center
US20040189698A1 (en) * 2003-03-26 2004-09-30 Nortel Networks Limited Instant messaging to service bureau
US6970547B2 (en) * 2003-05-12 2005-11-29 Onstate Communications Corporation Universal state-aware communications
US8984118B2 (en) * 2003-06-30 2015-03-17 Comverse, Ltd. Automatic messaging client launcher for a communication device
US8094804B2 (en) 2003-09-26 2012-01-10 Avaya Inc. Method and apparatus for assessing the status of work waiting for service
US20050071212A1 (en) * 2003-09-26 2005-03-31 Flockhart Andrew D. Method and apparatus for business time computation in a resource allocation system
US7460652B2 (en) 2003-09-26 2008-12-02 At&T Intellectual Property I, L.P. VoiceXML and rule engine based switchboard for interactive voice response (IVR) services
US20050071241A1 (en) * 2003-09-26 2005-03-31 Flockhart Andrew D. Contact center resource allocation based on work bidding/auction
US7770175B2 (en) * 2003-09-26 2010-08-03 Avaya Inc. Method and apparatus for load balancing work on a network of servers based on the probability of being serviced within a service time goal
US7231034B1 (en) * 2003-10-21 2007-06-12 Acqueon Technologies, Inc. “Pull” architecture contact center
US7356475B2 (en) * 2004-01-05 2008-04-08 Sbc Knowledge Ventures, L.P. System and method for providing access to an interactive service offering
DE102004004276A1 (en) * 2004-01-28 2005-09-15 Siemens Ag Communication arrangement for automated acceptance and switching of an incoming communication connection
US7742580B2 (en) * 2004-02-05 2010-06-22 Avaya, Inc. Methods and apparatus for context and experience sensitive prompting in voice applications
US7953859B1 (en) 2004-03-31 2011-05-31 Avaya Inc. Data model of participation in multi-channel and multi-party contacts
US7933762B2 (en) * 2004-04-16 2011-04-26 Fortelligent, Inc. Predictive model generation
US8170841B2 (en) * 2004-04-16 2012-05-01 Knowledgebase Marketing, Inc. Predictive model validation
US7499897B2 (en) * 2004-04-16 2009-03-03 Fortelligent, Inc. Predictive model variable management
US7562058B2 (en) * 2004-04-16 2009-07-14 Fortelligent, Inc. Predictive model management using a re-entrant process
US7730003B2 (en) * 2004-04-16 2010-06-01 Fortelligent, Inc. Predictive model augmentation by variable transformation
US8165853B2 (en) * 2004-04-16 2012-04-24 Knowledgebase Marketing, Inc. Dimension reduction in predictive model development
US7725300B2 (en) * 2004-04-16 2010-05-25 Fortelligent, Inc. Target profiling in predictive modeling
US20050234761A1 (en) * 2004-04-16 2005-10-20 Pinto Stephen K Predictive model development
US7650293B2 (en) * 2004-04-27 2010-01-19 Verint Americas, Inc. System and method for workforce requirements management
US8130929B2 (en) * 2004-05-25 2012-03-06 Galileo Processing, Inc. Methods for obtaining complex data in an interactive voice response system
US8738412B2 (en) * 2004-07-13 2014-05-27 Avaya Inc. Method and apparatus for supporting individualized selection rules for resource allocation
FR2873526A1 (en) * 2004-07-21 2006-01-27 France Telecom METHOD AND SYSTEM FOR MANAGING IDENTITY OVERLOAD AND PRIVATE / PUBLIC AVAILABILITY OF AN INSTANT MESSAGING ADDRESS
US7936861B2 (en) * 2004-07-23 2011-05-03 At&T Intellectual Property I, L.P. Announcement system and method of use
US20060026049A1 (en) * 2004-07-28 2006-02-02 Sbc Knowledge Ventures, L.P. Method for identifying and prioritizing customer care automation
US8165281B2 (en) * 2004-07-28 2012-04-24 At&T Intellectual Property I, L.P. Method and system for mapping caller information to call center agent transactions
US7580837B2 (en) * 2004-08-12 2009-08-25 At&T Intellectual Property I, L.P. System and method for targeted tuning module of a speech recognition system
US7602898B2 (en) * 2004-08-18 2009-10-13 At&T Intellectual Property I, L.P. System and method for providing computer assisted user support
US7949121B1 (en) 2004-09-27 2011-05-24 Avaya Inc. Method and apparatus for the simultaneous delivery of multiple contacts to an agent
US8234141B1 (en) 2004-09-27 2012-07-31 Avaya Inc. Dynamic work assignment strategies based on multiple aspects of agent proficiency
US7197130B2 (en) 2004-10-05 2007-03-27 Sbc Knowledge Ventures, L.P. Dynamic load balancing between multiple locations with different telephony system
US7668889B2 (en) 2004-10-27 2010-02-23 At&T Intellectual Property I, Lp Method and system to combine keyword and natural language search results
US7657005B2 (en) * 2004-11-02 2010-02-02 At&T Intellectual Property I, L.P. System and method for identifying telephone callers
US7724889B2 (en) * 2004-11-29 2010-05-25 At&T Intellectual Property I, L.P. System and method for utilizing confidence levels in automated call routing
US7864942B2 (en) 2004-12-06 2011-01-04 At&T Intellectual Property I, L.P. System and method for routing calls
US7242751B2 (en) 2004-12-06 2007-07-10 Sbc Knowledge Ventures, L.P. System and method for speech recognition-enabled automatic call routing
US7295657B1 (en) 2004-12-07 2007-11-13 International Business Machines Corporation Automated selection of a backup recipient and distribution of an instant messaging request to the backup recipient
US7298831B1 (en) * 2004-12-07 2007-11-20 International Business Machines Corporation Automated distribution of an instant messaging request for an unavailable intended recipient to a backup recipient
US8000455B1 (en) 2004-12-09 2011-08-16 Callwave, Inc. Methods and systems for call processing
US7409048B2 (en) 2004-12-09 2008-08-05 Callwave, Inc. Call processing and subscriber registration systems and methods
US20060126811A1 (en) * 2004-12-13 2006-06-15 Sbc Knowledge Ventures, L.P. System and method for routing calls
US7751551B2 (en) 2005-01-10 2010-07-06 At&T Intellectual Property I, L.P. System and method for speech-enabled call routing
US7627096B2 (en) * 2005-01-14 2009-12-01 At&T Intellectual Property I, L.P. System and method for independently recognizing and selecting actions and objects in a speech recognition system
US7450698B2 (en) * 2005-01-14 2008-11-11 At&T Intellectual Property 1, L.P. System and method of utilizing a hybrid semantic model for speech recognition
US7627109B2 (en) 2005-02-04 2009-12-01 At&T Intellectual Property I, Lp Call center system for multiple transaction selections
US8223954B2 (en) 2005-03-22 2012-07-17 At&T Intellectual Property I, L.P. System and method for automating customer relations in a communications environment
US7636432B2 (en) * 2005-05-13 2009-12-22 At&T Intellectual Property I, L.P. System and method of determining call treatment of repeat calls
EP1729247A1 (en) * 2005-06-01 2006-12-06 InVision Software AG Resource planning for employees
US8005204B2 (en) * 2005-06-03 2011-08-23 At&T Intellectual Property I, L.P. Call routing system and method of using the same
US7657020B2 (en) * 2005-06-03 2010-02-02 At&T Intellectual Property I, Lp Call routing system and method of using the same
US8503663B2 (en) * 2005-06-30 2013-08-06 Interactive Intelligence, Inc. System and method for agent queue activation in a contact center
US8503641B2 (en) * 2005-07-01 2013-08-06 At&T Intellectual Property I, L.P. System and method of automated order status retrieval
US8526577B2 (en) * 2005-08-25 2013-09-03 At&T Intellectual Property I, L.P. System and method to access content from a speech-enabled automated system
US8548157B2 (en) 2005-08-29 2013-10-01 At&T Intellectual Property I, L.P. System and method of managing incoming telephone calls at a call center
US20070067197A1 (en) * 2005-09-16 2007-03-22 Sbc Knowledge Ventures, L.P. Efficiently routing customer inquiries created with a self-service application
US8577014B2 (en) * 2005-11-04 2013-11-05 At&T Intellectual Property I, L.P. System and method of managing calls at a call center
US9008075B2 (en) 2005-12-22 2015-04-14 Genesys Telecommunications Laboratories, Inc. System and methods for improving interaction routing performance
US20070168444A1 (en) * 2006-01-18 2007-07-19 Yen-Fu Chen Method for automatically initiating an instant messaging chat session based on a calendar entry
US8972494B2 (en) * 2006-01-19 2015-03-03 International Business Machines Corporation Scheduling calendar entries via an instant messaging interface
US7885395B2 (en) * 2006-01-27 2011-02-08 Microsoft Corporation Telephone call routing
US9129290B2 (en) 2006-02-22 2015-09-08 24/7 Customer, Inc. Apparatus and method for predicting customer behavior
US7761321B2 (en) * 2006-02-22 2010-07-20 24/7 Customer, Inc. System and method for customer requests and contact management
US8396741B2 (en) 2006-02-22 2013-03-12 24/7 Customer, Inc. Mining interactions to manage customer experience throughout a customer service lifecycle
US7599861B2 (en) 2006-03-02 2009-10-06 Convergys Customer Management Group, Inc. System and method for closed loop decisionmaking in an automated care system
US7809663B1 (en) 2006-05-22 2010-10-05 Convergys Cmg Utah, Inc. System and method for supporting the utilization of machine language
US8379830B1 (en) 2006-05-22 2013-02-19 Convergys Customer Management Delaware Llc System and method for automated customer service with contingent live interaction
US8068601B2 (en) * 2006-07-26 2011-11-29 Cisco Technology, Inc. Queuing and routing telephone calls
US8175255B2 (en) * 2006-08-31 2012-05-08 At&T Intellectual Property I, L.P. Methods, systems and computer-readable media for managing customer service requests
US7707130B2 (en) * 2006-10-23 2010-04-27 Health Care Information Services Llc Real-time predictive computer program, model, and method
JP4855984B2 (en) * 2007-03-20 2012-01-18 株式会社日立製作所 IP telephone system, IP exchange, IP terminal, IP exchange backup method, and IP terminal login method
US8340276B1 (en) 2007-04-30 2012-12-25 United Services Automobile Association (Usaa) System and method for providing customer service
US20090006229A1 (en) * 2007-06-28 2009-01-01 Embarq Holdings Company, Llc System and method for telephony billing codes
US20090003579A1 (en) * 2007-06-29 2009-01-01 Verizon Data Services Inc. Apparatus and method for providing call deflection
US8355486B2 (en) 2007-10-31 2013-01-15 Centurylink Intellectual Property Llc System and method for inbound call billing
US10708431B2 (en) 2008-01-28 2020-07-07 Afiniti Europe Technologies Limited Techniques for hybrid behavioral pairing in a contact center system
US8781100B2 (en) 2008-01-28 2014-07-15 Satmap International Holdings Limited Probability multiplier process for call center routing
US8718271B2 (en) 2008-01-28 2014-05-06 Satmap International Holdings Limited Call routing methods and systems based on multiple variable standardized scoring
US8903079B2 (en) 2008-01-28 2014-12-02 Satmap International Holdings Limited Routing callers from a set of callers based on caller data
US10750023B2 (en) 2008-01-28 2020-08-18 Afiniti Europe Technologies Limited Techniques for hybrid behavioral pairing in a contact center system
US10567586B2 (en) 2008-11-06 2020-02-18 Afiniti Europe Technologies Limited Pooling callers for matching to agents based on pattern matching algorithms
US10708430B2 (en) 2008-01-28 2020-07-07 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a contact center system
US20090190745A1 (en) * 2008-01-28 2009-07-30 The Resource Group International Ltd Pooling callers for a call center routing system
US8670548B2 (en) 2008-01-28 2014-03-11 Satmap International Holdings Limited Jumping callers held in queue for a call center routing system
US9654641B1 (en) 2008-01-28 2017-05-16 Afiniti International Holdings, Ltd. Systems and methods for routing callers to an agent in a contact center
US20090190750A1 (en) * 2008-01-28 2009-07-30 The Resource Group International Ltd Routing callers out of queue order for a call center routing system
US9712676B1 (en) 2008-01-28 2017-07-18 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a contact center system
US9787841B2 (en) 2008-01-28 2017-10-10 Afiniti Europe Technologies Limited Techniques for hybrid behavioral pairing in a contact center system
US9300802B1 (en) 2008-01-28 2016-03-29 Satmap International Holdings Limited Techniques for behavioral pairing in a contact center system
US8879715B2 (en) 2012-03-26 2014-11-04 Satmap International Holdings Limited Call mapping systems and methods using variance algorithm (VA) and/or distribution compensation
US8824658B2 (en) 2008-11-06 2014-09-02 Satmap International Holdings Limited Selective mapping of callers in a call center routing system
US9774740B2 (en) 2008-01-28 2017-09-26 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a contact center system
US9692898B1 (en) 2008-01-28 2017-06-27 Afiniti Europe Technologies Limited Techniques for benchmarking paring strategies in a contact center system
US9781269B2 (en) 2008-01-28 2017-10-03 Afiniti Europe Technologies Limited Techniques for hybrid behavioral pairing in a contact center system
US20090232294A1 (en) * 2008-01-28 2009-09-17 Qiaobing Xie Skipping a caller in queue for a call routing center
US9712679B2 (en) 2008-01-28 2017-07-18 Afiniti International Holdings, Ltd. Systems and methods for routing callers to an agent in a contact center
US20100020959A1 (en) * 2008-07-28 2010-01-28 The Resource Group International Ltd Routing callers to agents based on personality data of agents
US8855615B2 (en) * 2008-08-25 2014-10-07 International Business Machines Corporation Short messaging service for extending customer service delivery channels
US8644490B2 (en) 2008-08-29 2014-02-04 Satmap International Holdings Limited Shadow queue for callers in a performance/pattern matching based call routing system
US8781106B2 (en) 2008-08-29 2014-07-15 Satmap International Holdings Limited Agent satisfaction data for call routing based on pattern matching algorithm
US9047124B2 (en) * 2008-10-14 2015-06-02 Hewlett-Packard Development Company, L.P. Query scheduler
US8472611B2 (en) * 2008-11-06 2013-06-25 The Resource Group International Ltd. Balancing multiple computer models in a call center routing system
USRE48412E1 (en) 2008-11-06 2021-01-26 Afiniti, Ltd. Balancing multiple computer models in a call center routing system
US20100111288A1 (en) * 2008-11-06 2010-05-06 Afzal Hassan Time to answer selector and advisor for call routing center
US8634540B1 (en) 2008-11-13 2014-01-21 United Services Automobile Association (Usaa) Systems and methods for providing telephone prompts to a client based on web site activities of the client
US8634542B2 (en) * 2008-12-09 2014-01-21 Satmap International Holdings Limited Separate pattern matching algorithms and computer models based on available caller data
US8295471B2 (en) * 2009-01-16 2012-10-23 The Resource Group International Selective mapping of callers in a call-center routing system based on individual agent settings
US8515049B2 (en) * 2009-03-26 2013-08-20 Avaya Inc. Social network urgent communication monitor and real-time call launch system
US8792632B2 (en) * 2009-08-13 2014-07-29 Genesys Telecommunications Laboratories, Inc. System and methods for scheduling and optimizing inbound call flow to a call center
US8731182B2 (en) * 2009-06-23 2014-05-20 Avaya Inc. Data store for assessing accuracy of call center agent service time estimates
US8565412B2 (en) * 2009-06-23 2013-10-22 Avaya Inc. Servicing calls in call centers based on estimated call value
US20100332286A1 (en) * 2009-06-24 2010-12-30 At&T Intellectual Property I, L.P., Predicting communication outcome based on a regression model
US9468755B2 (en) * 2009-09-30 2016-10-18 Respicardia, Inc. Medical lead with preformed bias
US20110125697A1 (en) * 2009-11-20 2011-05-26 Avaya Inc. Social media contact center dialog system
US20110125793A1 (en) * 2009-11-20 2011-05-26 Avaya Inc. Method for determining response channel for a contact center from historic social media postings
KR20150042876A (en) * 2009-12-23 2015-04-21 아브 이니티오 테크놀로지 엘엘시 Managing queries
US9537924B2 (en) 2010-01-28 2017-01-03 Genesys Telecommunications Laboratories, Inc. Interaction management system and methods of use
US8306212B2 (en) * 2010-02-19 2012-11-06 Avaya Inc. Time-based work assignments in automated contact distribution
US9378505B2 (en) 2010-07-26 2016-06-28 Revguard, Llc Automated multivariate testing technique for optimized customer outcome
US8724797B2 (en) 2010-08-26 2014-05-13 Satmap International Holdings Limited Estimating agent performance in a call routing center system
US8699694B2 (en) 2010-08-26 2014-04-15 Satmap International Holdings Limited Precalculated caller-agent pairs for a call center routing system
US8750488B2 (en) 2010-08-31 2014-06-10 Satmap International Holdings Limited Predicted call time as routing variable in a call routing center system
US9043220B2 (en) * 2010-10-19 2015-05-26 International Business Machines Corporation Defining marketing strategies through derived E-commerce patterns
US9288320B2 (en) * 2011-12-15 2016-03-15 Nuance Communications, Inc. System and method for servicing a call
US9699239B1 (en) 2012-01-12 2017-07-04 Televoice, Inc. Systems and methods for contact management
US8565410B2 (en) 2012-03-26 2013-10-22 The Resource Group International, Ltd. Call mapping systems and methods using variance algorithm (VA) and/or distribution compensation
US8792630B2 (en) 2012-09-24 2014-07-29 Satmap International Holdings Limited Use of abstracted data in pattern matching system
US20140149177A1 (en) * 2012-11-23 2014-05-29 Ari M. Frank Responding to uncertainty of a user regarding an experience by presenting a prior experience
US9137372B2 (en) * 2013-03-14 2015-09-15 Mattersight Corporation Real-time predictive routing
US11514379B2 (en) * 2013-03-15 2022-11-29 Bmc Software, Inc. Work assignment queue elimination
US8948369B2 (en) * 2013-06-24 2015-02-03 Avaya Inc. Method and system for optimizing performance within a contact center
US10262268B2 (en) 2013-10-04 2019-04-16 Mattersight Corporation Predictive analytic systems and methods
US10572880B2 (en) * 2014-07-30 2020-02-25 Visa International Service Association Integrated merchant purchase inquiry and dispute resolution system
US9641680B1 (en) * 2015-04-21 2017-05-02 Eric Wold Cross-linking call metadata
US9787840B1 (en) 2015-06-11 2017-10-10 Noble Systems Corporation Forecasting and scheduling campaigns involving different channels of communication
US9426291B1 (en) * 2015-10-16 2016-08-23 Noble Systems Corporation Forecasting and scheduling campaigns involving sending outbound communications that generate inbound communications
MX2018006523A (en) 2015-12-01 2018-08-15 Afiniti Europe Tech Ltd Techniques for case allocation.
US10142473B1 (en) 2016-06-08 2018-11-27 Afiniti Europe Technologies Limited Techniques for benchmarking performance in a contact center system
US10270610B2 (en) * 2016-06-12 2019-04-23 Apple Inc. Selection of a coordinator device for an automated environment
US9692899B1 (en) 2016-08-30 2017-06-27 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a contact center system
US9888121B1 (en) 2016-12-13 2018-02-06 Afiniti Europe Technologies Limited Techniques for behavioral pairing model evaluation in a contact center system
WO2018118983A1 (en) * 2016-12-19 2018-06-28 Interactive Intelligence Group, Inc. System and method for managing contact center system
US10257354B2 (en) 2016-12-30 2019-04-09 Afiniti Europe Technologies Limited Techniques for L3 pairing in a contact center system
US9955013B1 (en) 2016-12-30 2018-04-24 Afiniti Europe Technologies Limited Techniques for L3 pairing in a contact center system
US10320984B2 (en) 2016-12-30 2019-06-11 Afiniti Europe Technologies Limited Techniques for L3 pairing in a contact center system
US11831808B2 (en) 2016-12-30 2023-11-28 Afiniti, Ltd. Contact center system
US10326882B2 (en) 2016-12-30 2019-06-18 Afiniti Europe Technologies Limited Techniques for workforce management in a contact center system
US10135986B1 (en) 2017-02-21 2018-11-20 Afiniti International Holdings, Ltd. Techniques for behavioral pairing model evaluation in a contact center system
US10970658B2 (en) 2017-04-05 2021-04-06 Afiniti, Ltd. Techniques for behavioral pairing in a dispatch center system
US9930180B1 (en) 2017-04-28 2018-03-27 Afiniti, Ltd. Techniques for behavioral pairing in a contact center system
US10721202B2 (en) * 2017-05-29 2020-07-21 International Business Machines Corporation Broadcast response prioritization and engagements
US10122860B1 (en) 2017-07-10 2018-11-06 Afiniti Europe Technologies Limited Techniques for estimating expected performance in a task assignment system
US10235628B1 (en) * 2017-08-29 2019-03-19 Massachusetts Mutual Life Insurance Company System and method for managing routing of customer calls to agents
US11176461B1 (en) 2017-08-29 2021-11-16 Massachusetts Mutual Life Insurance Company System and method for managing routing of customer calls to agents
US10509669B2 (en) 2017-11-08 2019-12-17 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a task assignment system
US10110746B1 (en) 2017-11-08 2018-10-23 Afiniti Europe Technologies Limited Techniques for benchmarking pairing strategies in a task assignment system
US11399096B2 (en) 2017-11-29 2022-07-26 Afiniti, Ltd. Techniques for data matching in a contact center system
US10509671B2 (en) 2017-12-11 2019-12-17 Afiniti Europe Technologies Limited Techniques for behavioral pairing in a task assignment system
US10091361B1 (en) 2018-01-19 2018-10-02 Noble Systems Corporation Queueing communications for a contact center
US10623565B2 (en) 2018-02-09 2020-04-14 Afiniti Europe Technologies Limited Techniques for behavioral pairing in a contact center system
US11250359B2 (en) 2018-05-30 2022-02-15 Afiniti, Ltd. Techniques for workforce management in a task assignment system
US11509770B2 (en) 2018-09-25 2022-11-22 International Business Machines Corporation Live agent recommendation for a human-robot symbiosis conversation system
US10496438B1 (en) 2018-09-28 2019-12-03 Afiniti, Ltd. Techniques for adapting behavioral pairing to runtime conditions in a task assignment system
US10867263B2 (en) 2018-12-04 2020-12-15 Afiniti, Ltd. Techniques for behavioral pairing in a multistage task assignment system
US10348904B1 (en) 2018-12-11 2019-07-09 Noble Systems Corporation Queueing multi-channel communications for a contact center
US11144344B2 (en) 2019-01-17 2021-10-12 Afiniti, Ltd. Techniques for behavioral pairing in a task assignment system
US11948153B1 (en) 2019-07-29 2024-04-02 Massachusetts Mutual Life Insurance Company System and method for managing customer call-backs
US10757261B1 (en) 2019-08-12 2020-08-25 Afiniti, Ltd. Techniques for pairing contacts and agents in a contact center system
US11445062B2 (en) 2019-08-26 2022-09-13 Afiniti, Ltd. Techniques for behavioral pairing in a task assignment system
US10616415B1 (en) 2019-08-27 2020-04-07 Noble Systems Corporation Queueing multi-channel communications for a contact center
US10757262B1 (en) 2019-09-19 2020-08-25 Afiniti, Ltd. Techniques for decisioning behavioral pairing in a task assignment system
US11611659B2 (en) 2020-02-03 2023-03-21 Afiniti, Ltd. Techniques for behavioral pairing in a task assignment system
EP4213022A1 (en) 2020-02-04 2023-07-19 Afiniti, Ltd. Techniques for error handling in a task assignment system with an external pairing system
CN115244554A (en) 2020-02-05 2022-10-25 阿菲尼帝有限公司 Techniques for sharing control of distributed tasks between an external pairing system and a task distribution system having an internal pairing system
CN115280340A (en) 2020-02-05 2022-11-01 阿菲尼帝有限公司 Techniques for behavioral pairing in a task distribution system having an external pairing system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4881261A (en) * 1988-06-29 1989-11-14 Rockwell International Corporation Method for predictive pacing of calls in a calling system
US5335269A (en) * 1992-03-12 1994-08-02 Rockwell International Corporation Two dimensional routing apparatus in an automatic call director-type system
US5838682A (en) * 1995-11-28 1998-11-17 Bell Atlantic Network Services, Inc. Method and apparatus for establishing communications with a remote node on a switched network based on hypertext dialing information received from a packet network
US5946386A (en) * 1996-03-11 1999-08-31 Xantel Corporation Call management system with call control from user workstation computers
US6002760A (en) * 1998-02-17 1999-12-14 Genesys Telecommunications Laboratories, Inc. Intelligent virtual queue
US6088444A (en) * 1997-04-11 2000-07-11 Walker Asset Management Limited Partnership Method and apparatus for value-based queuing of telephone calls
US6154530A (en) * 1997-04-22 2000-11-28 U.S. Philips Corporation Telecommunication equipment, system and method comprising management means for managing subscriber call-back lists

Family Cites Families (130)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5793846A (en) * 1985-07-10 1998-08-11 Ronald A. Katz Technology Licensing, Lp Telephonic-interface game control system
US5040208A (en) * 1989-11-03 1991-08-13 International Business Machines Corporation Coordinated voice and data display having temporary storage of transaction data
US5291550A (en) * 1990-12-26 1994-03-01 At&T Bell Laboratories Dynamic network call distributor
US5185782A (en) * 1991-02-08 1993-02-09 A&T Bell Laboratories ACD arrangement for automatically returning a call at a time specified by the original caller
US5444774A (en) 1992-06-26 1995-08-22 At&T Corp. Interactive queuing sytem for call centers
US5499291A (en) 1993-01-14 1996-03-12 At&T Corp. Arrangement for automating call-center agent-schedule-notification and schedule-adherence functions
US5479487A (en) 1993-02-11 1995-12-26 Intervoice Limited Partnership Calling center employing unified control system
US6385312B1 (en) * 1993-02-22 2002-05-07 Murex Securities, Ltd. Automatic routing and information system for telephonic services
US5537436A (en) 1993-06-14 1996-07-16 At&T Corp. Simultaneous analog and digital communication applications
US5440585A (en) 1993-06-14 1995-08-08 At&T Corp. Applications of simultaneous analog and digital communication
US5448555A (en) 1993-06-14 1995-09-05 At&T Corp. Simultaneous analog and digital communication
US5509055A (en) 1993-06-30 1996-04-16 At&T Corp. Inbound telecommunications services resources management system
US5467388A (en) * 1994-01-31 1995-11-14 Bell Atlantic Network Services, Inc. Method and apparatus for selectively blocking incoming telephone calls
US5533108A (en) 1994-03-18 1996-07-02 At&T Corp. Method and system for routing phone calls based on voice and data transport capability
US5499289A (en) 1994-12-06 1996-03-12 At&T Corp. Systems, methods and articles of manufacture for performing distributed telecommunications
US5574781A (en) 1994-12-08 1996-11-12 At&T Translation indicator for database-queried communications services
US5546452A (en) 1995-03-02 1996-08-13 Geotel Communications Corp. Communications system using a central controller to control at least one network and agent system
US5696809A (en) * 1995-06-22 1997-12-09 Bell Atlantic Network Services, Inc. Advanced intelligent network based computer architecture for concurrent delivery of voice and text data using failure management system
US5627884A (en) * 1995-06-26 1997-05-06 Williams; Mark J. Method for returning inbound calls
US5684872A (en) * 1995-07-21 1997-11-04 Lucent Technologies Inc. Prediction of a caller's motivation as a basis for selecting treatment of an incoming call
US5751795A (en) 1995-08-11 1998-05-12 Lucent Technologies Inc. Broadcasting of information through telephone switching system display messages
US6597685B2 (en) * 1995-10-25 2003-07-22 Genesys Telecommunications Laboratories, Inc. Method and apparatus for determining and using multiple object states in an intelligent internet protocol telephony network
US5825870A (en) 1996-04-05 1998-10-20 Genesys Telecommunications Laboratories Methods and apparatus for implementing a network call center
US5740238A (en) 1995-11-03 1998-04-14 Lucent Technologies Inc. Method and apparatus for queuing a call to the best backup split
US5754639A (en) 1995-11-03 1998-05-19 Lucent Technologies Method and apparatus for queuing a call to the best split
WO1997018661A1 (en) * 1995-11-13 1997-05-22 Answersoft, Inc. Intelligent information routing system and method
US5742674A (en) * 1995-12-22 1998-04-21 At&T Corp. Automatic call-back system and method using data indicating best time to call
US5757904A (en) 1996-02-05 1998-05-26 Lucent Technologies Inc. Context-sensitive presentation of information to call-center agents
US5867559A (en) 1996-02-20 1999-02-02 Eis International, Inc. Real-time, on-line, call verification system
US5717747A (en) 1996-05-31 1998-02-10 Lucent Technologies Inc. Arrangement for facilitating plug-and-play call features
US6064730A (en) 1996-06-18 2000-05-16 Lucent Technologies Inc. Customer-self routing call center
US5721770A (en) 1996-07-02 1998-02-24 Lucent Technologies Inc. Agent vectoring programmably conditionally assigning agents to various tasks including tasks other than handling of waiting calls
US5757644A (en) 1996-07-25 1998-05-26 Eis International, Inc. Voice interactive call center training method using actual screens and screen logic
US6385646B1 (en) * 1996-08-23 2002-05-07 At&T Corp. Method and system for establishing voice communications in an internet environment
US5903877A (en) 1996-09-30 1999-05-11 Lucent Technologies Inc. Transaction center for processing customer transaction requests from alternative media sources
US6091808A (en) 1996-10-17 2000-07-18 Nortel Networks Corporation Methods of and apparatus for providing telephone call control and information
US6009162A (en) 1996-10-31 1999-12-28 Lucent Technologies Inc. Telecommunication feature for exchange of translation information between a computer and a telecommunication switching system
US6385191B1 (en) 1996-11-14 2002-05-07 Avaya Technology Corp. Extending internet calls to a telephony call center
US5987116A (en) 1996-12-03 1999-11-16 Northern Telecom Limited Call center integration with operator services databases
US5732218A (en) 1997-01-02 1998-03-24 Lucent Technologies Inc. Management-data-gathering system for gathering on clients and servers data regarding interactions between the servers, the clients, and users of the clients during real use of a network of clients and servers
US5828747A (en) 1997-01-28 1998-10-27 Lucent Technologies Inc. Call distribution based on agent occupancy
US5903641A (en) 1997-01-28 1999-05-11 Lucent Technologies Inc. Automatic dynamic changing of agents' call-handling assignments
US5930337A (en) 1997-02-04 1999-07-27 Lucent Technologies Inc. Dynamic message-mailbox size variation
US6044146A (en) * 1998-02-17 2000-03-28 Genesys Telecommunications Laboratories, Inc. Method and apparatus for call distribution and override with priority
US5926539A (en) 1997-09-12 1999-07-20 Genesys Telecommunications Laboratories, Inc. Method and apparatus for determining agent availability based on level of uncompleted tasks
US6205412B1 (en) 1997-07-09 2001-03-20 Genesys Telecommunications Laboratories, Inc. Methods in computer simulation of telephony systems
US6061442A (en) 1997-03-07 2000-05-09 Lucent Technologies Inc. Method and apparatus for improved call control scheduling in a distributed system with dissimilar call processors
US5905793A (en) 1997-03-07 1999-05-18 Lucent Technologies Inc. Waiting-call selection based on anticipated wait times
US5982873A (en) 1997-03-07 1999-11-09 Lucent Technologies Inc. Waiting-call selection based on objectives
US6014439A (en) 1997-04-08 2000-01-11 Walker Asset Management Limited Partnership Method and apparatus for entertaining callers in a queue
US6567787B1 (en) * 1998-08-17 2003-05-20 Walker Digital, Llc Method and apparatus for determining whether a verbal message was spoken during a transaction at a point-of-sale terminal
US5991293A (en) 1997-05-23 1999-11-23 Nortel Networks Corporation Circuit arrangement for providing internet connectivity to a computer in a key telephone system
US5898772A (en) 1997-05-29 1999-04-27 Lucent Technologies Inc. Logical PC agent
US5940475A (en) 1997-05-30 1999-08-17 Northern Telecom Limited Telephone system integrated text based communication apparatus and system to enhance access for TDD and/or TTY devices
US6078650A (en) 1997-05-30 2000-06-20 Nortel Networks Corporation Telephone system integrated text based communication processes to enhance access for TDD and/or TTY devices
US5933476A (en) 1997-05-30 1999-08-03 Northern Telecom Limited TTY telephone display and related processes systems and apparatus
US5943395A (en) 1997-05-30 1999-08-24 Northern Telecom Limited Telephone apparatus, systems, and processes to enhance access for TDD and/or TTY devices
US6002749A (en) 1997-05-30 1999-12-14 Nortel Networks Corporation Telephone system integrated text based communication apparatus and systems to establish communication links to TDD and/or TTY devices and other telephone and text server systems
US5960382A (en) 1997-07-07 1999-09-28 Lucent Technologies Inc. Translation of an initially-unknown message
US6118861A (en) 1997-08-14 2000-09-12 Nortel Networks Corporation Calling party invoked held call monitoring
US6192050B1 (en) 1997-08-29 2001-02-20 Nortel Networks Limited Method and apparatus for inquiry response via internet
US6188673B1 (en) 1997-09-02 2001-02-13 Avaya Technology Corp. Using web page hit statistics to anticipate call center traffic
US6373836B1 (en) * 1997-09-15 2002-04-16 Genesys Telecommunications Laboratories, Inc. Apparatus and methods in routing internet protocol network telephony calls in a centrally-managed call center system
US6985943B2 (en) * 1998-09-11 2006-01-10 Genesys Telecommunications Laboratories, Inc. Method and apparatus for extended management of state and interaction of a remote knowledge worker from a contact center
US6337858B1 (en) 1997-10-10 2002-01-08 Nortel Networks Limited Method and apparatus for originating voice calls from a data network
US6188762B1 (en) 1997-12-01 2001-02-13 Stephen Shooster Web call center/PSTN to TCPIP internet network
US6122364A (en) 1997-12-02 2000-09-19 Nortel Networks Corporation Internet network call center
US6052460A (en) 1997-12-17 2000-04-18 Lucent Technologies Inc. Arrangement for equalizing levels of service among skills
US6088441A (en) 1997-12-17 2000-07-11 Lucent Technologies Inc. Arrangement for equalizing levels of service among skills
US6181776B1 (en) 1997-12-24 2001-01-30 Nortel Networks Limited Network management of automatic call distributor resources
US6215784B1 (en) 1997-12-24 2001-04-10 Nortel Networks Limited Method and system for voice call completion using information retrieved from an open application on a computing machine
JP3335898B2 (en) * 1998-01-08 2002-10-21 株式会社東芝 Private branch exchange system and its private branch exchange.
US6192122B1 (en) 1998-02-12 2001-02-20 Avaya Technology Corp. Call center agent selection that optimizes call wait times
US6226377B1 (en) 1998-03-06 2001-05-01 Avaya Technology Corp. Prioritized transaction server allocation
US6088442A (en) 1998-03-16 2000-07-11 Lucent Technologies Inc. Automatic wireless alerting on an automatic call distribution center
US6038302A (en) 1998-04-02 2000-03-14 Lucent Technologies Inc. Methods and apparatus for processing phantom calls placed via computer-telephony integration (CTI)
US6173053B1 (en) 1998-04-09 2001-01-09 Avaya Technology Corp. Optimizing call-center performance by using predictive data to distribute calls among agents
US6256299B1 (en) 1998-04-30 2001-07-03 Avaya Technology Corp. Automatic service provider notification of unauthorized terminal activity
US6070012A (en) 1998-05-22 2000-05-30 Nortel Networks Corporation Method and apparatus for upgrading software subsystems without interrupting service
US6292550B1 (en) 1998-06-01 2001-09-18 Avaya Technology Corp. Dynamic call vectoring
US6272216B1 (en) 1998-06-01 2001-08-07 Avaya Technology Corp Customer self routing call center
US6404747B1 (en) * 1998-06-02 2002-06-11 Avaya Technology Corp. Integrated audio and video agent system in an automatic call distribution environment
US6233332B1 (en) 1998-06-03 2001-05-15 Avaya Technology Corp. System for context based media independent communications processing
US6526397B2 (en) * 1998-06-19 2003-02-25 Nortel Networks Limited Resource management facilitation
US6563916B1 (en) * 1998-07-08 2003-05-13 Lucent Technologies Inc. System for transmitting a change in call queued/hold state across a communications network
US6298127B1 (en) 1998-07-13 2001-10-02 Nortel Networks Limited Call transfer and conference with separate billing records per leg
US6535601B1 (en) * 1998-08-27 2003-03-18 Avaya Technology Corp. Skill-value queuing in a call center
US6353667B1 (en) 1998-08-27 2002-03-05 Avaya Technology Corp. Minimum interruption cycle time threshold for reserve call center agents
US6272544B1 (en) 1998-09-08 2001-08-07 Avaya Technology Corp Dynamically assigning priorities for the allocation of server resources to completing classes of work based upon achievement of server level goals
US6163606A (en) 1998-09-16 2000-12-19 Lucent Technologies Inc. System for providing virtual called party identification in a voice mail system
US6539090B1 (en) * 1998-10-06 2003-03-25 Lucent Technologies, Inc. Generalized arrangement for routing telecommunications calls
US6295353B1 (en) 1998-10-07 2001-09-25 Avaya Technology Corp. Arrangement for efficiently updating status information of a network call-routing system
US6064731A (en) 1998-10-29 2000-05-16 Lucent Technologies Inc. Arrangement for improving retention of call center's customers
US6256381B1 (en) 1998-10-30 2001-07-03 Avaya Technology Corp. System and method for identifying a data record associated with a transferred telephone call
EP1003117A3 (en) * 1998-11-17 2003-07-23 Citibank, N.A. Method and system for strategic services enterprise workload management
US6327362B1 (en) 1998-11-23 2001-12-04 Lucent Technologies Inc. System and method including dynamic differential treatment in workflows and contact flow
US6377944B1 (en) 1998-12-11 2002-04-23 Avaya Technology Corp. Web response unit including computer network based communication
US6366666B2 (en) 1998-12-16 2002-04-02 Avaya Technology Corp. Adjustment of call selection to achieve target values for interval-based performance metrics in a call center
US6581205B1 (en) * 1998-12-17 2003-06-17 International Business Machines Corporation Intelligent compilation of materialized view maintenance for query processing systems
US6314177B1 (en) 1998-12-22 2001-11-06 Nortel Networks Limited Communications handling center and communications forwarding method using agent attributes
US6353851B1 (en) 1998-12-28 2002-03-05 Lucent Technologies Inc. Method and apparatus for sharing asymmetric information and services in simultaneously viewed documents on a communication system
US6356632B1 (en) 1998-12-31 2002-03-12 Avaya Technology Corp. Call selection and agent selection in a call center based on agent staffing schedule
EP1018689A3 (en) * 1999-01-08 2001-01-24 Lucent Technologies Inc. Methods and apparatus for enabling shared web-based interaction in stateful servers
US6359982B1 (en) 1999-01-12 2002-03-19 Avaya Technologies Corp. Methods and apparatus for determining measures of agent-related occupancy in a call center
US6208721B1 (en) 1999-01-22 2001-03-27 Lucent Technologies Inc. Method and apparatus for identifying telephone callers who have been unsuccessful in reaching a called destination
US6434230B1 (en) * 1999-02-02 2002-08-13 Avaya Technology Corp. Rules-based queuing of calls to call-handling resources
US6505183B1 (en) * 1999-02-04 2003-01-07 Authoria, Inc. Human resource knowledge modeling and delivery system
US20030033382A1 (en) * 1999-02-05 2003-02-13 Bogolea Steven C. Interactive communication system
US6560649B1 (en) * 1999-02-10 2003-05-06 Avaya Technology Corp. Hierarchical service level remediation for competing classes based upon achievement of service level goals
US7055098B2 (en) * 1999-02-19 2006-05-30 Lucent Technologies Inc. Dynamic display of data item evaluation
US6366668B1 (en) 1999-03-11 2002-04-02 Avaya Technology Corp. Method of routing calls in an automatic call distribution network
US6349205B1 (en) 1999-04-15 2002-02-19 Lucent Technologies Inc. Method for converting an existing subscriber to a wireless communications system
US6584439B1 (en) * 1999-05-21 2003-06-24 Winbond Electronics Corporation Method and apparatus for controlling voice controlled devices
US6240391B1 (en) 1999-05-25 2001-05-29 Lucent Technologies Inc. Method and apparatus for assembling and presenting structured voicemail messages
US6392666B1 (en) * 1999-07-21 2002-05-21 Avaya Technology Corp. Telephone call center monitoring system allowing real-time display of summary views and interactively defined detailed views
US6542156B1 (en) * 1999-07-21 2003-04-01 Avaya Technology Corp. Telephone call center monitoring system with integrated three-dimensional display of multiple split activity data
US6389132B1 (en) * 1999-10-13 2002-05-14 Avaya Technology Corp. Multi-tasking, web-based call center
US6549769B1 (en) * 1999-10-29 2003-04-15 Concerto Software, Inc. System and method for integrating text messaging to an outbound call system
US6563920B1 (en) * 1999-12-15 2003-05-13 Avaya Technology Corp. Methods and apparatus for processing of communications in a call center based on variable rest period determinations
US6408066B1 (en) * 1999-12-15 2002-06-18 Lucent Technologies Inc. ACD skill-based routing
US6577720B1 (en) * 1999-12-29 2003-06-10 Nortel Networks Corporation System and method for providing high-speed communications using a public terminal
US6571240B1 (en) * 2000-02-02 2003-05-27 Chi Fai Ho Information processing for searching categorizing information in a document based on a categorization hierarchy and extracted phrases
US6415018B1 (en) 2000-02-08 2002-07-02 Lucent Technologies Inc. Telecommunication system and method for handling special number calls having geographic sensitivity
US6377975B1 (en) * 2000-03-01 2002-04-23 Interactive Intelligence, Inc. Methods and systems to distribute client software tasks among a number of servers
US20020010645A1 (en) * 2000-07-12 2002-01-24 David Hagen Backend commerce engine
US6825696B2 (en) * 2001-06-27 2004-11-30 Intel Corporation Dual-stage comparator unit
US20030013438A1 (en) * 2001-07-12 2003-01-16 Darby George Eugene Pocket concierge system and method
US7065201B2 (en) * 2001-07-31 2006-06-20 Sbc Technology Resources, Inc. Telephone call processing in an interactive voice response call management system
US6850605B2 (en) * 2001-11-28 2005-02-01 Ameritech Corporation Method of billing in an abbreviated dialing service
US6687587B2 (en) * 2001-12-21 2004-02-03 General Motors Corporation Method and system for managing vehicle control modules through telematics

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4881261A (en) * 1988-06-29 1989-11-14 Rockwell International Corporation Method for predictive pacing of calls in a calling system
US5335269A (en) * 1992-03-12 1994-08-02 Rockwell International Corporation Two dimensional routing apparatus in an automatic call director-type system
US5838682A (en) * 1995-11-28 1998-11-17 Bell Atlantic Network Services, Inc. Method and apparatus for establishing communications with a remote node on a switched network based on hypertext dialing information received from a packet network
US5946386A (en) * 1996-03-11 1999-08-31 Xantel Corporation Call management system with call control from user workstation computers
US6088444A (en) * 1997-04-11 2000-07-11 Walker Asset Management Limited Partnership Method and apparatus for value-based queuing of telephone calls
US6154530A (en) * 1997-04-22 2000-11-28 U.S. Philips Corporation Telecommunication equipment, system and method comprising management means for managing subscriber call-back lists
US6002760A (en) * 1998-02-17 1999-12-14 Genesys Telecommunications Laboratories, Inc. Intelligent virtual queue

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1459189A1 (en) * 2001-12-28 2004-09-22 Simdesk Technologies, Inc. Instant messaging system
EP1459189A4 (en) * 2001-12-28 2006-08-30 Simdesk Technologies Inc Instant messaging system
WO2014053017A1 (en) * 2012-10-03 2014-04-10 Iselect Ltd Systems and methods for use in marketing
AU2013327396B2 (en) * 2012-10-03 2017-01-05 Iselect Ltd Systems and methods for use in marketing
US9674354B2 (en) 2012-10-03 2017-06-06 ISelect Ltd. Systems and methods for use in marketing
US10089642B2 (en) 2012-10-03 2018-10-02 Iselect Ltd Systems and methods for use in marketing

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