CA2646835A1 - Method and system for analyzing voice data - Google Patents

Abstract

A computer readable medium for analyzing a telephone call between a customer and a call center is provided. The computer readable medium comprises a code segment for analyzing a telephonic communication by applying a pre-determined retention attrition criteria to the telephonic communication to calculate an attrition probability, a code segment for receiving customer value data associated with the customer, a code segment for comparing the attrition probability with the customer value data, and a code segment for generating a retention strategy based on comparing the attrition probability with the customer value data.

Description

Method and System for Analyzing Voice data DESCRIPTION
TECHNICAL FIELD
[0001] The invention relates to a metliod and system for analyzing an electronic conlmunication, more particularly, to analyzing a telephone conununication between a customer and a contact center by applying a psychological behavioral niodel tliereto.
BACKGROUND OF THE INVENTION

[0002] It is lalown to utilize telephone call centers to facilitate the receipt, response an.d routing of incoming telephone calls relating to customer service, retention, and sales.
Generally, a customer is in contact with a customer service representative ("CSR") or call center agent who is responsible for answering the customer's inquiries and/or directing the customer to the appropriate individual, department, information source, or seivice as required to satisfy the customer's needs.

[0003] It is also well laiown to monitor calls between a customer and a call center agent.
Accordingly, call centers typically employ individuals responsible for listening to the conversation between the customer and the agent. Many companies have in-house call centers to respond to customers complaints and inquiries. In many case, however, it has been found to be cost effective for a coinpany to hire third party telephone call centers to handle such inquiries. As such, the call centers may be located thousands of miles away from the actual sought manufacturer or individual. This often results in use of inconsistent and subjective methods of monitoring, training and evaluating call center agents.
These methods also may vary widely froin call center to call center.
[00041 While monitoring such calls may occur in real time, it is often more efficient and useful to record the call for later review. Information gatliered from the calls is typically used to monitor the performance of the call center agents to identify possible training needs. Based on the review and analysis of the conversation, a monitor will make suggestions or recommendations to improve the quality of the customer interaction.
[0005] Accordingly, there is a need in customer relationship nianagement ("CRM") for an objective tool useful in improving the quality of customer interactions witli agents and ultimately customer relationships. In particular, a need exists for an objective monitoring and analysis tool which provides information about a custonier's perception of an interaction during a call. In the past, post-call data collection methods have been used to survey callers for feedback. This feedback may be subsequently used by a supervisor or trainer to evaluate an agent. Although such surveys have enjoyed some degree of success, their usefuhless is directly tied to a customer's willingness to provide post-call data.
[0006] More "passive" methods have also been employed to collect data relating to a customer's in-call experience. For example, U.S. Patent No. 6,724,887 to Eilbacher et al. is directed to a method and system for analyzing a customer connnunication with a contact center. According to Eilbacher, a contact center may include a monitoring system which records customer connnunications and a customer experience analyzing unit which reviews the customer connnttnications. The customer experience analyzing unit identifies at least one parameter of the customer connnunications and automatically determines whetlier the identified paraineter of the customer communications indicates a negative or unsatisfactory experience. According to Eilbacher, a stress analysis may be perfonned on audio telephone calls to detennine a stress paraineter by processing the audio portions of the telephone calls.
From tllis, it can then be determined whether the customer experience of the caller was satisfactory or unsatisfactory.
[0007] While the method of EilUacher provides some benefit witli respect to reacliing an ultimate conclusion as to whether a customer's experience was satisfactory or tinsatisfactory, the method provides little insight into the reasons for an experiential outcome. As such, the method of Eilbacher provides only limited value in training agents for future customer communications. Accordingly, there exists a need for a system that analyzes the underlying behavioral characteristics of a customer and agent so that data relating to these behavioral characteristics can be used for subsequent analysis and training.
[0008] Systems such as stress analysis systems, spectral analysis models and word-spotting models also exist for determining certain characteristics of audible sounds associated with a communication. For exainple, systems such as those disclosed in U.S.
Patent No.
6,480,826 to Pertnishin provide a system and method for determining emotions in a voice signal. However, lilce Eilbacher, these systems also provide only limited value in training customer service agents for future customer interactions. Moreover, such methods have limited statistical accuracy in detennining stiinuli for events occurring throughout an interaction.
[0009] It is well known that certain psychological behavioral models have been developed as tools to evaluate and understand how and/or why one person or a group of people interacts with another person or group of people. There exists a need for a system and ,. ,.. . _. _ method that analyzes the underlying behavioral cliaracteristics of a customer and agent communication by automatically applying a psychological behavioral model to the coimnunication.
[0010] Devices and software for recording and logging calls to a call center are well laiown. However, application of word-spotting analytical tools to recorded audio comnlunications can pose problems. Devices and software that convert recorded or unrecorded audio signals to text files are also laiown the art. But, translation of audio signals to text files often results in lost voice data due to necessary conditioning and/or compression of the audio signal. Accordingly, a need also exists to provide a system that allows a contact center to capture audio signals and telephony events with sufficient clarity to accurately apply a linguistic-based psychological behavioral analytic tool to a telephonic coinmtinication.
[0011] The present invention is provided to solve the problems discussed above and other problems, and to provide advantages and aspects not previously provided. A
full discussion of the features and advantages of the present invention is defei.-red to the following detailed description, which proceeds with reference to the accompanying drawings.

SUMMARY OF THE INVENTION
[0012] According to the present invention, a method for analyzing a telephonic communication between a customer and a contact center is provided. According to the method, a telephonic communication is separated into at least first constituent voice data and second constituent voice data. One of the first and second constituent voice data is analyzed by mining the voice data and applying a predetennined linguistic-based psychological behavioral model to one of the separated first and second constittient voice data. Behavioral assessment data is generated which corresponds to the analyzed voice data.
[0013] According to another aspect of the present invention, the telephonic connnunication is received in digital fonnat. The step of separating the communication into at least a first and second constituent voice data comprises the steps of identifying a communication protocol associated with the telephonic connnunication, and recording the telephonic communication to a first electronic data file. The first electronic data file is comprised of a first and second audio track. The first constituent voice data is automatically recorded on the first audio track based on the identified commtinication protocol, and the second constituent voice data is automatically recorded on the second audio track based on the identified communication protocol. At least one of the first and second constitttent voice data recorded on the corresponding first and second track is separated from the first electronic data file. It is also contemplated that two first data files can be created, wlierein the first audio track is recorded to one of the first data file and the second audio track is recorded to the other first data file.
[0014] According to another aspect of the present invention, the method described above fiu-ther comprises the step of generating a text file before the analyzing step. The text file includes a textual translation of eitlier or hoth of the first and second constituent voice data.
The analysis is then performed on the translated constituent voice data in the text file.
[0015] According to another aspect of the present invention, the predetennined linguistic-based psychological behavioral model is adapted to assess distiess levels in a connnunication.
Accordingly, the metliod further comprises the step of generating distress assessment data corresponding to the analyzed second constituent voice data.
[0016] According to yet anotller aspect of the present invention event data is generated.
The event data corresponds to at least one identifying indicia and time interval. The event data includes at least one of behavioral assessment data or distress assessment data. It is also contemplated that both behavioral assessment data and distress assessment data are included in the event data.
[0017] According to still another aspect of the present invention, the telephonic communication is one of a plurality of telephonic communications. Accordingly, the metliod further comprises the step of categorizing the telephonic conimunication as one of a plurality of call types and/or customer categories. The telephonic communication to be analyzed is selected from the plurality of telephonic communications based upon the call type and/or the customer category in which the telephonic comimtnication is categorized.
[0018] According to still another aspect of the present invention, a responsive communication to the telephonic cominunication is automatically generated based on the event data generated as result of the analysis.
[0019] According to another aspect of the present invention, a coinputer program for analyzing a telephonic connnunication is provided. The computer prograni is einhodied on a coinputer readable storage mediuin adapted to control a conlputer. The computer program comprises a plurality of code segments for perfonning the analysis of the telephonic comniunication. h1 particular, a code segment separates a telephonic communication into first constituent voice data and second constituent voice data. The coinputer program also has a code segnient that analyzes one of the first and second voice data by applying a predetennined psychological behavioral model to one of the separated first and second constituent voice data. And, a code segment is provided for generating behavioral assessment data corresponding to the analyzed constituent voice data.
[0020] According to yet anotlier aspect of the present invention, the computer program comprises a code seginent for receiving a telephonic comn7unication in digital format. The telephonic conlmunication is comprised of a first constituent voice data and a second constitLient voice data. A code segment identifies a communication protocol associated with the telephonic cominunication. A code segment is provided for separating the first and second constituent voice data one from the other by recording the telephonic communication in stereo fonnat to a first electronic data file. The first electYonic data file includes a first and second audio track. The first constittient voice data is automatically recorded on the first audio track based on the identified cominunication protocol, and the second constituent voice data is automatically recorded on the second audio track based on the identified communication protocol.
[0021] A code segnlent applies a non-linguistic based analytic tool to the separated first constituent voice data and generates phone event data corresponding to the analyzed first constituent voice data. A code segment is provided for translating the first constituent voice data into text fonnat and storing the translated first voice data in a first text file. A code segment analyzes the first text file by mining the text file and applying a predetermined linguistic-based psychological behavioral model to the text file. Either or both of behavioral assessment data and distress assessment data corresponding to the analyzed first voice data is generated therefrom.
[0022] According to another aspect of the present invention, the above analysis is performed on the second constituent voice data. Additionally, a code segment is provided for generating call assessment data by comparatively analyzing the behavioral assessment data and distress assessment data corresponding to the analyzed first voice data and the behavioral assessment data and distress assessment data corresponding to the analyzed second voice data. The computer prograin has a code segment for outputting event data which is comprised of call assessment data corresponding to at least one identifying indicia and at least one predetermined time interval.
[0023] According to still another aspect of the present invention, a metliod for analyzing an electronic communication is provided. The metliod comprises the step of receiving an electronic cominunication in digital format. The electronic communication includes communication data. The communication data is analyzed by applying a predetennined linguistic-based psychological behavioral model thereto. Sehavioral assessment data corresponding to the analyzed communication data is generated tlierefrom.
[0024] The method described can be embodied in a coniputer program stored on a computer readable media. The a computer program would include code segments or routines to enable all of the fiinctional aspects of the interface described or shown herein [0025] According to another aspect of the invention, a coinputer program for training a customer service representative by analyzing a telephonic communication between a customer and a contact center is provided. A code seginent selects at least one identifying criteria. A code segment identifies a pre-recorded first telephonic connnunication corresponding to the selected identifying criteria. The first telephonic connnunication has first event data associated therewith. A code segment generates coaching assessinent data corresponding to the identified pre-recorded first telephonic coinmunication.
A code segment identifies a pre-recorded second teleplionic communication coiresponding to the selected identifying criteria. The second telephonic coinmunication has second event data associated therewith. A code segment compares the identified pre-recorded second telephonic communication to the identified first telephonic communication within the coaching assessment data. A code seginent generates a notification based on the comparison of the identified pre-recorded second telepllonic communication witll the identified first telephonic commtinication within the coaching assessment data.
[0026] According to yet another aspect of the present invention, a code segment generates a first performance score for the coaching assessnlent. A code seginent generates a second perfomlance score for the pre-recorded second telephonic communication.
The notification is generated based on a comparison of first perfoi7nance score with the second perfonnance score.
[0027] According to still another aspect of the present invention, a code segment identifies a plurality of pre-recorded first telephonic connnunications based on at least one identifying criteria. Each of the first telephonic communications has first event data associated tllerewith. A code segment for identifies a plurality of pre-recorded second telephonic cominunications based on at least one identif-ying criteria. Each of the second telephonic communications having second event data associated therewitli. A
code segment generates a first perfonnance score for each of the plurality of prerecorded first telephonic communications and a code segment for generates a second perfonnance score for each of the plurality of prerecorded second telephonic comnninications. A code seginent generates a notification if a predetemlined nuinber of second perfonnance scores are at least one of less 7' tnan a preaetennnieu trnresllold of the first performance scores aiid greater than a predeterniined threshold of the first perforinance scores.
[0028] According to another aspect of the present invention, a computer program for training a customer service representative by analyzing a telephonic communication between a customer and a contact center is provided. A code segment selects at least one identifying criteria. A code segment identifies a pre-recorded first telephonic communications corresponding to the selected identifying criteria. The first telephonic conimunication having first event data associated therewith. A code segment generates coaching assessnlent data corresponding to the identified pre-recorded first telephonic coimiunication.
A code segnlent coinpares the identified first telephonic communication within the coaching assessment data with a predetennined identifying criteria value tlireshold. A code segment generates a notification based on the comparison of the identified first telephonic communication with the coaching assessment data with a predetennined identifying criteria value tllresllold.
[0029] According to yet another aspect of the invention, a code segment generates a first performance score for the coaching assessment data. A code seginent generates a second perfoimance for the identifying criteria value tlireshold. A code segment for generates a notification. The notification is generated based on a coniparison of first perfonnance score and the second perfonnance score.
According to anotller aspect of the invention, a code segment identifies a plurality of pre-recorded first telephonic coinmunications based on at least one identifying criteria. Each of the first telephonic cominunications lzaving first event data associated therewith. A code segment generates a first perfonnance score for each of the plurality of prerecorded first telephonic communications based on the at least one identifying criteria. A
code segznent generates a second performance score based on the identifying criteria value tllreshold. A
code segnlent generates a notification. The notification is generated if a predetennined threshold of first perfonnance scores are at least one of less than the second performance score and greater than the second performance scores.
[0030) According to another aspect of the present invention a computer program for analyzing a telephone call between a customer and a call center is provided. A
code seginent analyzes a telephonic conlmunication by applying a pre-detennined retention attrition criteria to the telephonic connnunication to calculate an attrition probability. A code seginent receives customer value data associated with the customer and a code segment coznpares the attrition probability with the customer value data. A code segment generates a retention strategy based on comparing the attrition probability witli the customer value data. The retention strategy can be generated based on event data, such as behavioral assessment data, distress assessment data and phone event data.
[0031] According to another aspect of the present invention, the computer program coinprises a code segment for separating a telephonic communication into at least a first constituent voice data and a second constituent voice data wherein in the code segment for analyzing the telephonic communication. At least one of the first constituent voice data and the second constituent voice data is analyzed by mining the respective voice data and applying a pre-deterinined linguist model to the voice data to calculate the attrition probability.
[0032] According to yet another aspect of the present invention, the computer program comprises a code segment for generating a notification. The notification can be a responsive cominunication generated based on the retention strategy wlzerein the responsive comniunication is at least one of an email, a voice communication, and a written communication.
[0033] According to another aspect of the invention, the coinputer program coinprises a code segment for generating an attrition probability score based on the attrition probability, wherein in the code segment for generating the retention strategy, the attrition probability score is compared with the customer value data.
[0034] According to still another aspect of the present invention, the computer progranl further comprises a code seginent for generating a graphical user interface ("GUI"). The GUI
is adapted to display a first field for enabling identification of customer interaction event information on a display. The customer interaction event inforination includes call assessment data based on the psychological Uehavioral model applied to the analyzed constituent voice data of each customer interaction event. The computer program also includes a code segment for receiving input from a user for identifying at least a first customer interaction event. A code segment is also provided for displaying the customer interaction event information for the first customer interaction event.
[0035] According to one aspect of the present invention, the GUI enables a user of the systein to locate one or more caller interaction events (i.e., calls between a caller and the call center), and to display information relating to the event. In particular, the graphical user interface provides a visual field showing the results of the psychological behavioral model that was applied to a separated voice data from the caller interaction event.
Moreover, the interface can include a Iinlc to an audio file of a selected caller interaction event, and a visual representation that tracks the portion of the caller interaction that is currently heard as the audio file is being played.
[0036] According to one aspect of the invention, the graphical user interface is incorporated in a system for identifying one or more caller interaction events and displaying a psychological behavioral model applied to a separated voice data of a customer interaction event. The system coinprises a computer eoupled to a display and to a database of caller ihteraction event information. The caller interaction event infonnation includes data resulting from application of a psychological behavioral model to a first voice data separated from an audio wave fonn of a caller interaction event. Additionally, the caller event information can also include additional inforniation conceixiing each call, such as statistical data relating to the caller interaction event (e.g., time, date and length of call, caller identification, agent identification, hold times, transfers, etc.), and a recording of the caller interaction event.
[0037] The system also includes a processor, either at the user's coniputer or at another computer, such as a central server available over a networlc connection, for generating a grapllical user interface on the display. The graphical user interface comprises a selection visual field for enaUling user input of caller interaction event parameters for selection of at least a first caller interaction event and/or a ph.irality of caller interaction events. The caller interaction event paraineters can include one or more caller i tlteraction event identifying characteristic. These characteristics can include, for example, the caller's name or other identification infonnation, a date range, the agent's name, the call center identification, a supeivisor identifier, etc. For exanlple, the graphical user interface can enable a user to select all caller interaction events for a particular caller; or all calls handled by a particular agent.
Both examples can be narrowed to cover a specified time period or interval.
The interface will display a selected caller interaction event field which provides identification of caller interaction events corresponding to the user input of caller interaction event paraineters..
[0038] The graphical user interface also incltides a conversation visual field for displaying a time-based representation of characteristics of the caller interaction event(s) based on the psychological behavioral inodel. These characteristics were generated by the application of a psychological beliavioral model to a first voice data separated from an audio wave form of a caller interaction event which is stored as part of the caller interaction event infonnation.
[0039] The conversation visual field can inchide a visual lii-ilc to an audio file of the caller interaction event(s). Additionally, it may also iilclude a graphical representation of the progress of the first caller interaction event that corresponds to a portion of the audio file Ueing played. For example, the interface may show a line representing the call and a nloving pointer marking the position on the line corresponding to the portion of the event Ueing played. Additionally, the time-based representation of characteristics of the caller interaction event can inclttde graphical or visual characteristic elements which are also displayed in the conversation visual field. Moreover, the characteristic elements are located, or have pointers to, specific locations of the graphical representation of the progress of the event corresponding to where the element was generated by the analysis.
[0040] The graphical user interface fizrtlier includes a call statistics visual field selectable by a user for displaying statistics pertaining to the caller interaction events. The statistics in the call statistics visual field can include, for example: call duration, caller tallc tinze, agent tallc time, a caller satisfaction score, an indication of the number of silences greater than a pr.edetennined time period, and an agent satisfaction score.
[0041] The grapliical user interface can also include a nunlUer of other visual fields. For example, the graphical user interface can include a caller satisfaction report field for displaying one or more caller satisfaction reports, or a user note field for enabling a user of the system to place a note witl-i the first caller interaction event.
[0042] In accordance with anotller enibodinient of the invention, a method for identifying one or more caller interaction events and displaying an analysis of a psychological behavioral model applied to a separated voice data from the caller interaction everit comprises providing a graphical user interface for displaying a first field for enabling identification of caller interaetion event infonnation on a display, the caller interaction event infonnation including analysis data based on a psychological behavioral model applied to a first separated voice data of each caller interaction event; receiving input from a user for identifying at least a first caller interaction event; and, displaying the caller interaction event infonnation for the first caller interaction event on the display. The step of receiving input from a user can include receiving at least one or more of a caller identifier, a call center identifier, an agent identifier, a supervisor identifier, and a date range.
[0043] The step of displaying the caller interaction event inforiiiation for the first caller interaction event on the display can include displaying a time-based representation of characteristics of the first caller interaction event based on the psychological Uehavioral model. The method cau also include providing an audio file of tlie first caller interaction event. In this regard, the displaying of the time-based representation of characteristics of the first caller event based on the psycliological behavioral model can include displaying a graphical representation of the progress of the first caller interaction event that coiTesponds to a portion of the audio file Ueing played.
[0044] The graphical user interface can be generated by a user's local computer, or from a reniote server coupled to the user's computer via a network connection. In this latter instance, the method can fiirtlier include creating a web page containing the graphical user interface that is downloadable to a user's conlputer, and downloading the page via the networlc comlection.
[0045] The method can include providing other visual fields for enabling other fiulctions of the system. For exanlple, the method can include providing a field in the graphical user interface for enabling a user to place a note with the infoniiation for the first caller interaction event.
[0046] The graplzical user interface described can be embodied in a computer program stored on a computer readable media. The a computer program would include code seginents or routines to enaUle all of the fiinctional aspects of the interface described or shown herein.
[0047] Other features and advantages of the invention will be apparent from the following specification taken in conjunction with tlie following drawings.

BRIEF DESCRIPTION OF THE DRAWINGS
[0048] To understand the present invention, it will now be described by way of example, with reference to the accompanying drawings in wlzich:
FIG. 1 is a block diagram of call ceiiter;
FIG. 2 is a block diagrain of the recording engine and behavioral analysis engine according to the present invention;
FIG. 3 is a block diagram of a computer used in coiuzection with the present invention;
FIG. 4 is a flow chart illustrating the process of analyzing a telephonic communication in accordance with the present invention;
FIG. 5 is a flow chart illustrating the process of analyzing a telephonic communication in accordance with the present invention;
FIG. 6 is a flow chart illustrating the process of analyzing a telephonic communication in accordance witli the present invention;
FIG. 7 is a block diagram of a telephonic communication system according to the present invention;

FIG. 8 is a block diagram of a telephonic commtmication system according to the present invention;
FIG. 9 is a bloclc diagram of a telephonic conununication system with a nnilti-port PSTN module according to the present invention;
FIG. 10 is a flow chart illustrating the process of recording and separating a telephonic cominunication in accordance with the present invention;
FIG. 11 is a flow chai-t illustrating the process of recording and separating a telephonic comnzunication in accordance with the present invention;
FIG. 12 is a flow chart illustrating the process of analyzing separated constitttent voice data of a telephonic coinmunication in accordance with the present invention;
FIG. 13 is a flow chart illustrating the process of analyzing separated constituent voice data of a telepllonic communication in accordance with the present invention;
FIGS. 14-32 are graphical user interface screens of the resultant output from the process of analyzing voice data of a telephonic communication in accordance with the present invention;
FIG. 33 is a flow chart illustrating the process the training the call center agent by analyzing a telephonic communication;
FIGS. 34-36 are graphical user interface screens of the resultant output fiom tl-ie process of analyzing voice data of a telephonic communication in accordance with the present invention; and FIG. 37 is a flow chart illustrating the process of generating a retention strategy in accordance with the present invention.

DETAILED DESCRIPTION
[0049] While this invention is susceptible of einbodiments in many different fonns, there is shown in the drawings and will herein be described in detail preferred embodiments of the invention with the understanding that the present disclosure is to be considered as an exemplification of the principles of the invention and is not intended to limit the broad aspect of the invention to the embodiments illustrated.
[0050] Referring to FIGS. 1-32, a method and system for analyzing an electronic cominunication between a customer and a contact center is provided. A "contact center" as used herein can include any facility or system server suitable for receiving and recording electronic comintinications from customers. Such communications can include, for example, telephone calls, facsimile transmissions, e-mails, web interactions, voice over IP ("VoIP") and video. It is conteinplated that these comnninications may be transmitted by and througli any type of teleconnnunication device and over any medium suitable for carrying data. For exainple, the communications may be transmitted by or through telephone lines, cable or wireless coinmunications. As shown in FIG. 1, The contact center 10 of the present invention is adapted to receive and record varying electronic communications 11 and data fonnats that represent an interaction that may occur between a customer (or caller) 7 and a contact center agent 9 during fulfilhnent of a customer/agent transaction.
[0051] As shown in FIG. 2, the present niethod and system for analyzing an electronic communication between a customer 7 and a contact center 10 comprises a recording engine 2 and an behavioral analysis engine 3. As will be described in fi.trther detail, an audio communication signal is recorded, separated into constituent audio data, and analyzed in accordance with the methods described below. It is contemplated that the niethod for analyzing an electronic connnunication between a customer 7 and a contact center 10 of the present invention can be implemented by a computer program. Now is described in more specific tenns, the computer hardware associated with operating the computer program that may be used in connection with the present invention.
[0052] Process descriptions or blocks in figures should be understood as representing modules, segments, or portions of code wliich include one or more executable instructions for implementing specific logical fiinctions or steps in the process. Alternate implementations are included within the scope of the embodiments of the present invention in which fiulctions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the fiinctionality involved, as would be understood by those having ordinary slcill in the art.
[0053] FIG. 3 is a block diagram of a conzputer or seiver 12. For purposes of understanding the hardware as described herein, the tenns "computer" and "server" have identical meaniiigs and are interchangeably used. Computer 12 includes coaztrol system 14.
The control system 14 of the invention can be iinplemented in software (e.g., firmware), hardware, or a combination thereof. In the currently contemplated best mode, the control system 14 is implemented in software, as an executable prograni, and is executed by one or more special or general purpose digital computer(s), such as a personal computer (PC; IBM-conZpatible, Apple-compatible, or otherwise), personal digital assistant, workstation, minicomputer, or mainfralne coniputer. An example of a general purpose computer that can implement the control system 14 of the present invention is shown in FIG. 3.
The control system 14 may reside in, or have portions residing in, any computer such as, but not limited to, a general purpose personal conlputer. Tlierefore, computer 12 of FIG. 3 may be representative of any computer in wllich the control system 14 resides or partially resides.
[0054] Generally, in tenns of hardware architecture, as shown in FIG. 3, the computer 12 includes a processor 16, memory 18, and one or more input and/or output (UO) devices 20 (or peripherals) that are communicatively coupled via a local interface 22. The local interface 22 can be, for example, but not liniited to, one or more buses or other wired or wireless connections, as is luiown in the art. The local interface 22 may have additional elements, whicll are omitted for sinlplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the local interface may include address, control, and/or data connections to enable appropriate cominunications ainong the otlier computer conlponents.
[0055] The processor 16 is a hardware device for executing software, particularly software stored in nlemory 18. The processor 16 can be any custom made or coinnlercially available processor, a central processing unit (CPU), an auxiliaiy processor among several processors associated witli the computer 12, a semiconductor based microprocessor (in the fonn of a microchip or chip set), a macroprocessor, or generally any device for execiiting software instructions. Examples of suitable conimercially available microprocessors are as follows: a PA-RISC series microprocessor from Hewlett-Packard Coinpany, an 80x8 or Pentiunl series microprocessor from Intel Coiporation, a PowerPC
microprocessor from IBM, a Sparc microprocessor from Sun Microsystenis, Ine., or a 8xxx series microprocessor from Motorola Corporation.
[0056] The memory 18 can include any one or a coinUination of volatile memory elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile memory elements (e.g., ROM, hard drive, tape, CDROM, etc.).
Moreover, memory 18 may incorporate electronic, magnetic, optical, and/or other types of storage media. The memory 18 can have a distributed arcliitecture where various components are situated remote from one another, but can be accessed by the processor 16.
[0057] The software in memory 18 may include one or more separate programs, each of which conlprises an ordered listing of executable instructions for implementing logical functions. In the exainple of FIG. 3, the software in the memory 18 includes the control system 14 in accordance with the present invention and a suitable operating system (O/S) 24.
A non-exhaustive list of examples of suitable commercially available operating systems 24 is as follows: (a) a Windows operating system available from Microsoft Corporation; (b) a Netware operating system available from Novell, Inc.; (c) a Macintosh operating system available from Apple Coniputer, Inc.; (d) a UNIX operating systeni, which is available for purchase from many vendors, such as the Hewlett-Packard Conipany, SYUi Microsystems, Inc., and AT&T Corporation; (e) a LINUX operating system, which is freeware that is readily available on the Internet; (f) a nrn time Vxworks operating system from WindRiver Systenis, Inc.; or (g) an appliance-Uased operating system, such as that implemented in handheld computers or personal digital assistants (PDAs) (e.g., PalmOS available fiom Palm Coinputing, hic., and Windows CE available from Microsoft Corporation). The operating system 24 essentially controls the execution of other conlputer progranls, such as the control system 14, and provides scheduling, nlput-output control, file an.d data iuanagement, memory inanagenient, and comnnuiication control and related services.
[0058] The control systeni 14 may be a source program, executable program (object code), script, or any other entity comprising a set of instructions to be perfoiln.ed. When a source prograni, the program needs to be translated via a compiler, assembler, interpreter, or the lilce, which may or may not be included within the memory 18, so as to operate properly in connection with the O/S 24. Furthermore, the control systei-n 14 can be written as (a) an object oriented programming language, which has classes of data and methods, or (b) a procedure programming language, wllich has routines, subroutines, and/or fitnctions, for example but not linlited to, C, C++, Pascal, Basic, Fortran, Cobol, Perl, Java, and Ada. In one emUodiment, the conti-ol system 14 is written in C++. The I/O devices 20 may include input devices, for exanlple but not limited to, a keyboard, mouse, scanner, microphone, touch screens, interfaces for various medical devices, bar code readers, stylus, laser readers, radio-frequency device readers, etc. Furtherniore, the I/O devices 20 may also include output devices, for exainple but not limited to, a printer, bar code printers, displays, etc. Finally, the UO devices 20 may fiuther include devices that communicate botli inputs and outputs, for instance but not Iiinited to, a modulator/demodulator (modem; for accessing another device, system, or networlc), a radio frequency (RF) or other transceiver, a telephonic interface, a bridge, a router, etc.
[0059] If the computer 12 is a PC, workstation, PDA, or the lil{e, the software in the memory 18 may further include a basic input output system (BIOS) (not shoixn-i in FIG. 3).
The BIOS is a set of software rotitines that initialize and test hardware at startup, start the O/S
24, and support the transfer of data among the hardware devices. The BIOS is stored in ROM so that the BIOS can be executed wllen the computer 12 is activated.
[0060] When the conlputer 12 is in operation, the processor 16 is configured to execute software stored witliin the memory 18, to communicate data to and from the memory 18, and to generally control operations of the computer 12 pursuant to the software.
The control system 14 and the O/S 24, in whole or in part, but typically the latter, are read by the processor 16, perhaps buffered within the processor 16, and then executed.
[0061) When the control system 14 is implemented in software, as is shown in FIG. 3, it should be noted that the control system 14 can be stored on any computer readable medium for use by or in comiection with any conlputer related system or method. In the context of this docuinent, a"coinputer-readaUle mediuin" can be any means that can store, communicate, propagate, or transport the prograin for use by or in connection with the instniction execution system, apparatus, or device. The computer readable mediuin can be for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the coinputer-readable lnedium would include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). The control system 14 can be embodied in any computer-readable mediuni for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
[0062] In another enibodiment, where the control systeni 14 is iniplemented in hardware, the control system 14 can be implemented with any or a combination of the following technologies, which are each well lcliown in the art: a discrete logic circuit(s) having logic gates for implementing logic fiinctions upon data signals, an application specific integrated circuit (ASIC) having appropriate comUinational logic gates, a progrannnaUle gate array(s) (PGA), a field programnzable gate array (FPGA), etc.
[0063] FIG. 4 illustrates the general flow of one emUodiment of the method of analyzing voice data according to the present invention. As shown, an tuzconzpressed digital stereo audio wavefomi of a conversation between a customer and a call center agent is recorded and separated into customer voice data and call center agent voice data 26. The voice data associated witli the audio waveform is tlien rnined and analyzed using multi-stage linguistic and non-linguistic analytic tools 28. The analysis data is stored 30 and can be accessed by a user 31 (e.g., CSR supervisor) through an interface portal 32 for subsequent review 32. The digital stereo audio waveform is compressed 34 and stored 36 in an audio file which is held on a media selver 38 for subsequent access througli the interface porta132.
[0064] The nlethod of the present invention is configured to postpone audio compression until analysis of the audio data is complete. This delay allows the system to apply the analytic tools to a truer and clearer hi-fidelity signal. The system employed in comiection with the present invention also nlinimizes audio distortion, iiicreases fidelity, eliminates gain control and requires no additional filtering of the signal.
[0065] As shown in FIG. 6, according to one elnbodiment, the method of the present invention more specifically comprises the step of separating a telephonic comnzunication 2 into first constituent voice data and second constituent voice data 40. One of the first or second constittiient voice data is then separately alialyzed by applying a predetermined psychological behavioral model thereto 42 to generate behavioral assessment data 44. In one embodiment discussed in detail below, linguistic-based behavioral models are adapted to assess behavior based on behavioral signifiers within a connnunications are employed. It is conteinplated that one or more psychological behavioral models may be applied to tlie voice data to generate behavioral assessznent data therefrom.
[0066] The telephonic communication 2 being analyzed can be one of numerous calls stored within a contact center server 12, or communicated to a contact center during a given time period. Accordingly, the present method contemplates that the telephonic communication 2 being subjected to analysis is selected from the plurality of telephonic communications. The selection criteria for determining whicli comniunication should be analyzed may vary. For example, the cominunications coming into a contact center can be automatically categorized iuto a plurality of call types using an appropriate algoritlun. For example, the system may employ a word-spotting algorithm that categorizes comrnunications 2 into particular types or categories based on words used in the connnunication. In one embodiment, each communication 2 is automatically categorized as a service call type (e.g., a caller requesting assistance for servicing a previously purchased product), a retention call type (e.g., a caller expressing indignation, or having a significant life change event), or a sales call type (e.g., a caller purchasing an item offered by a seller). In one scenario, it may be desirable to analyze all of the "sales call type" communications received by a colltact center during a predetennined tirne frame. In that case, the user would analyze each of the sales call type communications from that time period by applying the predetennined psychological behavioral model to each such coinnitulication.

[0067] AlteilZatively, the communications 2 may be grouped according to custoiner categories, and the user may desire to analyze the communications 2 between the call center and communicants within a particular customer category. For example, it may be desirable for a user to perfonzl an analysis oarlly of a"platinum customers" category, consisting of high end investors, or a "high volume distributors" category comprised of a user's best distributors.
[0068] In one enlUodinient the teleplionic communication 2 is telephone call in wliich a telephonic sigiial is transmitted. As many be seen in FIGS. 7 and 8, a customer sending a telephonic signal may access a contact center 10 through the public switched telephone networlc (PSTN) 203 and an automatic call distribution system (PBX/ACD) 205 directs the communication to one of a plurality of agent worlc stations 211, 213. Each agent worlc station 211, 213 includes, for example, a computer 215 and a telephone 213.
[0069] When analyzing voice data, it is preferable to worlc from a true and clear hi-fidelity signal. This is tn.le botli in instances in which the voice data is being translated into a text format for analysis using a linguistic-based psychological behavioral model thereto, or in instance in which a linguistic-based psychological behavioral model is being applied directly to an audio waveforni, audio stream or file containing voice data.
[0070] FIG. 7 illustrates a telephonic comiilunication system 201, such as a distributed private branch exchange (PBX), having a public switched telephone network (PSTN) 203 connected to the PBX through a PBX switch 205.
[0071] The PBX switch 205 provides an interface between the PSTN 203 and a local networlc. Preferably, the interface is controlled by software stored on a telephoizy server 207 coupled to the PBX switch 205. The PBX switch 205, using interface software, connects tnink and line station interfaces of the puUlic switch telephone networlc 203 to stations of a local network or other peripheral devices contemplated by one slcilled in the art. Further, in another embodiment, the PBX switch may be integrated within telephony server 207. The stations may include various types of cominunication devices connected to the networlc, including the telephony server 207, a recording seiver 209, telephone stations 211, and client personal computers 213 equipped witli telephone stations 215. The local network may further include fax machines and modems.
[0072] Generally, in tenns of hardware architecture, the teleplrony server 207 includes a processor, memory, and one or more input and/or output (I/O) devices (or peripherals) that are communicatively coupled via a local interface. The processor can be any custom-nlade or commercially available processor, a central processing unit (CPU), an auxiliary processor among several processors associated witll the telephony seiver 207, a senliconductor based inicroprocessor (in the foi-m of a microcliip or chip set), a macroprocessor, or generally any device for executing software instnictions. The memory of the telephony seiver 207 can include any one or a comUination of volatile memoiy elements (e.g., random access memory (RAM, such as DRAM, SRAM, SDRAM, etc.)) and iionvolatile meinory elements (e.g., ROM, hard drive, tape, CDROM, etc.). The telephony server 207 may fiu-ther include a keyboard and a mouse for control puiposes, and an attached graphic monitor for obseivation of software operation.
[0073] The telephony seiver 207 incotporates PBX control software to control the initiation and terniination of connections between stations and via outside trunk connections to the PSTN 203. In addition, the software may monitor the status of all telephone stations 211 in real-time on the network and may be capable of responding to telephony events to provide traditional telephone service. This may include the control and generation of the conventional signaling tones such as dial tones, busy tones, ring back tones, as well as the coniiection and tenlzination of media streams between telephones on the local network.
Further, the PBX control software may use a multi-port module 223 and PCs to implement standard PBX functions such as the initiation and termination of telephone calls, either across the network or to outside trunlc lines, the ability to pttt calls on hold, to transfer, parlc and pick up calls, to conference multiple callers, and to provide caller ID
information. Telephony applications such as voice mail and auto attendant may be implemented by application software using tlie PBX as a network telephony services provider.
[0074] Referring to FIG. 9, in one embodiment, the telephony seiver 207 is equipped witli inulti-port PSTN module 223 having circuitry and software to inipleinent a trunlc interface 217 and a local network interface 219. The PSTN module 223 conZprises a control processor 221 to manage the transmission and reception of network messages between the PBX switch 205 and the telephony networlc server 207. The control processor 221 is also capable of directing networlc messages between the PBX switch 205, the local networlc interface 291, the telephony networlc serNier 207, and the trunk interface 217. hl the one embodiment, the local networlc uses Transmission Control Protocol/hlternet Protocol (TCP/IP). The networlc messages may contain con-iputer data, telephony transniission stipervision, signaling and various media streams, such as audio data and video data. The control processor 221 directs network messages containing computer data fronl the PBX
switch 205 to the telephony networlc server 207 directly tlirougll the inulti-port PSTN module 223.

[0075] The control processor 221 may include buffer storage and control logic to convert inedia streains from one fornlat to anotlier, if necessary, between the tLluik interface 217 and local networlc. The trunk interface 217 provides interconnection with the trunlc circuits of the PSTN 203. The local networlc interface 219 provides conventional software and circuitry to enable the telephony server 207 to access the local networlc. The buffer RAM
and control logic implenzent efficient transfer of inedia streams between the tiLulk interface 217, the telephony server 207, the digital signal processor 225, and the local networlc interface 219.
[0076] The trunlc interface 217 utilizes conventional telephony trunlc transmission supervision and signaling protocols required to interface with the outside trunlc circuits from the PSTN 203. The trunlc lines carry various types of telephony signals such as transnlission supervision and signaling, audio, fax, or modem data to provide plain old telephone service (POTS). In addition, the trunlc lines may carry other communication forniats such T1, ISDN
or fiber service to provide telephony or inultiinedia data images, video, text or audio.
[0077] The control processor 221 manages real-time telephony event handling pertaining to the telephone tiunk line interfaces, inchiding managing the efficient use of digital signal processor resources for the detection of caller ID, DTMF, call progress aiid other conventional fonns of signaling found on trunk lines. The control processor 221 also manages the generafiion of telephony tones for dialing and other puiposes, and controls the connection state, impedance matching, and echo cancellation of individual trunk line interfaces on the multi-port PSTN module 223.
[0078] Preferably, conventional PBX signaling is utilized between trunk and station, or station and station, such that data is translated into networlc messages that convey information relating to real-time telephony events on the networlc, or instructions to the networlc adapters of the stations to generate the appropriate signals and behavior to support normal voice communication, or instn.ictions to comiect voice media streanls using standard coiulections and signaling protocols. Networlc messages are sent from the control processor 221 to the telephony server 207 to notify the PBX software in the telephony server 207 of real-time telephony events on the attached trunlc Iines. Network niessages are received from the PBX
Switch 205 to implement telephone call supervision and may control the set-up and elimination of media streams for voice transmission.
[0079] The local networlc interface 219 includes conventional circuitry to interface with the local networlc. The specific circuitry is dependent on the signal protocol utilized in the local networlc. In one embodiment, the local networlc may be a local area networlc (LAN) utilizing IP telephony. IP telephony integrates audio and video stream control with legacy telephony functions and may be supported through the II.323 protocol. H.323 is an International Telecommunication Union-Telecommunications protocol used to provide voice and video services over data networlcs. H.323 permits users to make point-to-point audio and video phone calls over a local area networlc. IP telephony systems caii be integrated witll the public telephone systeni through a local networlc interface 219, such as an IP/PBX-PSTN
gateway, tliereby allowing a user to place telephone calls from an enabled coniputer. For exainple, a call from aii IP telephony client to a conventional telephone would be routed on the LAN to the IP/PBX-PSTN gateway. The IP/PBX-PSTN gateway translates H.323 protocol to conventional telephone protocol and routes the call over the conventional telephone networlc to its destination. Conversely, an incoming call from the PSTN 203 is routed to the IP/PBX-PSTN gateway and translates the conventional telephone protocol to H.323 protocol.
[0080] As noted above, PBX trunk control messages are transniitted from the telephony server 207 to the control processor 221 of the multi-port PSTN. In contrast, network messages containing media streams of digital representations of real-time voice are transmitted between the tilink interface 217 and local network interface 219 using the digital signal processor 225. The digital signal processor 225 may include buffer storage and control logic. Preferably, the buffer storage and control logic implement a first-in-first-out (FIFO) data buffering schenle for transmitting digital representations of voice audio between the local network to the trunk interface 217. It is noted that tlie digital signal processor 225 may be integrated with the control processor 221 on a single microprocessor.
[0081] The digital signal processor 225 may include a coder/decoder (CODEC) connected to the control processor 221. The CODEC may be a type TCM29e13 integrated circuit made by Texas Iiistruments, hic. In one embodiment, the digital signal processor 225 receives an analog or digital voice signal from a station within the network or from the trunlc lines of the PSTN 203. The CODEC converts the analog voice signal into in a digital from, such as digital data packets. It should be noted that the CODEC is not used when connection is made to digital lines and devices. From the CODEC, the digital data is transmitted to the digital signal processor 225 where telephone fiinctions talce place. The digital data is then passed to the control processor 221 which accumulates the data bytes from the digital signal processor 225. It is prefer-red that the data bytes are stored in a first-in-first-out (FIFO) memory buffer until there is sufficient data for one data packet to be sent according to the particular network protocol of the local network. The specific number of bytes transmitted per data packet depends on network latency requirements as selected by one of ordinary slcill in the art. Once a data packet is created, the data packet is sent to the appropriate destination on the local networlc through the local network interface 219. Among other inforination, the data paclcet contains a source address, a destination address, and audio data.
The source address identifies the location the audio data originated from and the destination address identifies the location the audio data is to be sent.
[0082] The system perinits bi-directional connnunication by implementing a return patll allowing data fioni the local network, through the local networlc interface 219, to be sent to the PSTN 203 through the multi-Iine PSTN truillc interface 217. Data streams fiom the local networlc are received by the local network interface 219 and i-ranslated fiom the protocol utilized on the local networlc to the protocol utilized on the PSTN 203. The conversion of data may be perforined as the inverse operation of the conversion described above relating to the IP/PBX-PSTN gateway. The data stream is restored in appropriate fonn suitable for transmission througlz to either a coinlected telephone 211, 215 or an interface trunlc 217 of the PSTN module 223, or a digital interface such as a T1 line or ISDN. In addition, digital data may be converted to analog data for transmission over the PSTN 203.
[0083] Generally, the PBX switch of the present invention may be implemented with hardware or virlually. A hardware PBX has equipment located local to the user of the PBX
system. The PBX switch 205 utilized may be a standard PBX manufacttired by Avaya, Siemens AG, NEC, Nortel, Toshiba, Fujitsu, Vodavi, Mitel, Ericsson, Panasonic, or InterTel.
In contrast, a virtual PBX has equipment located at a central telephone service provider and delivers the PBX as a service over the PSTN 203.
[0084] As illustrated in FIG. 1, the systenl includes a recording server 209 for recording and separating networlc messages transniitted witliin the system. The recording server 209 may be comiected to a port on the local network, as seen in FIG. 1.
Altematively, the recording server 209 may be connected to the PSTN tn.ulk liile as illustrated in FIG. 1A. The recording server 209 includes a control system software, such as recording software. The recording software of the invention can be inlplemeizted in software (e.g., firmware), hardware, or a combination thereof. In the currently contemplated best mode, the recording software is inlplemented in software, as an executable program, and is executed by one or more special or general puipose digital coniputer(s), such as a personal computer (PC; IBM-compatible, Apple-coinpatible, or otherwise), personal digital assistant, workstation, minicoinputer, or mainfraine computer. An exanlple of a general purpose computer that can implement the recording software of the present invention is shown in FIG. 3.
The recording software may reside in, or have portions residing in, any computer such as, but not liunited to, a general purpose personal computer. Therefore, recording server 209 of FIG. 3 may be representative of any type of computer in which the recording software resides or partially resides.
[0085] Generally, hardware arcllitecture is the same as that discussed above and shown in FIG. 3. Specifically, the recording server 209 includes a processor, memoiy, and one or more input and/or output (UO) devices (or peripherals) that are communicatively coupled via a local interface as previously described. The local interface can be, for example, but not limited to, one or more buses or other wired or wireless connections, as is lmown in the art.
The local interface may have additional elenlents, which are omitted for simplicity, sucli as controllers, buffers (caches), drivers, repeaters, and receivers, to enable coinnninications.
Furtlier, the local interface may include address, control, and/or data connections to enaUle appropriate communications among the other computer components.
[0086] As noted above, the recording server 209 incorporates recording software for recording and separating a signal based on the source address and/or destination address of the signal. The method utilized by the recording server 209 depends on the connnunication protocol utilized on the comniunication lines to which the recording server 209 is coupled. In tlie communication system conteinplated by the present invention, the signal carrying audio data of a communication Uetween at least two users niay be an analog signal or a digital signal in the fornl of a networlc message. h7 one embodiment, the signal is an audio data transmitted according to a signaling protocol, for example the H.323 protocol described above.
[0087] An example of a communication between an outside caller and a call center agent utilizing the present systeni 200 is illustrated in FIG. 10 and described herein. In the emUodiment of FIG. 10, when an outside caller reaches the system through the multi-line interface trunk 217, their voice signal is digitized (if needed) in the manner described above, and converted into digital data packets 235 aceording to the cominunication protocol utilized on the local networlc of the system. The data packet 235 comprises a souree address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and first constittient audio data comprising at least a portion of the outside callers voice. The data paclcet 235 can fiirther coinprise routing data identifying how the data packet 235 should be routed tluough the system and other relevant data. Once the data paclcet 235 is created, the data packet 235 is sent to the appropriate destination on the local networlc, such as to a call center agent, through the local networlc interface 219. The PBX and/or an automatic call distributor (ACD) can determine the initial communication setup, such as the connection state, impedance matching, and eclio cancellation, according to predetei-mined criteria.
[0088] Similar to the process described above, when the call center agent speaks, their voice is digitized (if needed) and converted into digital data packet 235 according to the communication protocol utilized on the local network. The data packet 235 conlprises a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constittient audio data coinprising at least a portion of the call eenter agent's voice. The data paclcet 235 is received by the local networlc interface 219 and translated from the conmiunication protocol utilized on the local networlc to the comintinication protocol utilized on the PSTN 203. The conversion of data can be perfornzed as described above. The data paclcet 235 is restored in appropriate form suitable for transmission through to eitlier a connected telephone 211, 215 or a interface triuAc 217 of the PSTN niodule 223, or a digital interface such as a Tl line or ISDN.
In addition, digital data can be converted to analog data for transmission through the PSTN
203.
[0089] The recording server 209 receives either a data packet 235 coinprising:
the source address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and the first constituent audio data comprising at least a portion of the outside callers voice; or a data packet 235 comprising a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constituent audio data comprising at least a portion of the customer's agent voice. It is understood by one of ordinary skill in the art that the recording server 209 is programnled to identify the comnzunication protocol utilized by the local network and extract the audio data within the data paclcet 235. In one enlbodiinent, the recording server 209 can atitomatically identify the utilized coimnunication protocol from a plurality of communication protocols. The plurality of communication protocols can be stored in local memory or accessed from a remote database.
[0090] The recording server 209 comprises recording software to record the communieation session between the outside caller and the call center agent in a single data file in a stereo format. The first data file 241 has at least a first atidio track 237 and a second audio track 237. Once a telephone connection is establislled between an outside caller and a call center agent, the recording software creates a first data file 241 to record the communication between the outside caller and the call center agent. It is contemplated that the entire communication session or a portion of tlie comnunication session can be recorded.

[00911 Upon receiving the data packet 235, the recording seiver 209 detemlines wllether to record the audio data contained in the data packet 235 in either the first audio traclc 237 or the second audio track 239 of the first data file 241 as determined by the source address, destination address, and/or the audio data contained within the received data paclcet 235.
Alternatively, two first data files can be created, wherein the first audio traclc is recorded to the one of the first data file and the second audio track is recorded to the second first data file.
In one einbodiment, if the data paclcet 235 comprises a source address identifying the address of the outside caller, a destination address identifying the address of the call center agent, and first constituent audio data, the first constittient audio data is recorded on the first atidio track 237 of the first data file 241. Similarly, if the data packet 235 comprises a source address identifying the address of the call center agent, a destination address identifying the address of the outside caller, and second constittient audio data, the second constituent audio data is recorded on the second audio track 239 of the first data file 241. It shotild be noted the first and second constituent audio data can be a digital or analog audio waveforin or a texttial translation of the digital or analog waveforin. The recording process is repeated until the commtinication link between the outside caller and call center agent is teminated.
[00921 As noted above, the recording server 209 can be connected to the trunlc lines of the PSTN 203 as seen in FIG. 8. The PSTN 203 can utilize a different protocol and therefore, the recording server 209 is configured to identify the conuiiunication protocol utilized by the PSTN 203, recognize the source and destination address of a signal and extract the audio data from the PSTN 203. The recording server 209 is programmed in a manner as lalown to one of ordinary skill in the art.
[00931 As shown in FIG. 10, once the communication link is terininated, the recording server 209 ends the recording session and stores the single data file haviilg the recorded cominunication session in memoiy. After the first data file is stored in memory, the recording server 209 can extract either or Uoth of the first constituent audio data from the first audio track of the first data file or the second constituent audio data from the second audio track of the first data file. hi one embodiment, the first constituent audio data extracted from the first audio track is stored in a first constituent data file 243.
Similarly, the second constituent audio data extracted from the second audio track can be stored in a second constituent data file 245. The first and second constituent data files 243, 245 can be compressed before being stored in memory. The extracted data can be in the form of a digital or analog audio waveform or can be a texttial translation of the first or second constituent audio data. It is contemplated tha.t eitller or botll of the first constittient data file 243 or the second constituent data file 245 can be fiirtb.er analyzed or processed. For example, among other processes and analyses, filtering techniques can be applied to the first constituent data file and/or the second constituent data file. Moreover, event data, such as silence periods or over-talking, can be identified through analysis tecluiiques lalown to those slcilled in the art.
[00941 Further, as illustrated in FIG. 10, the first constituent data file 243 and second constituent data file 245 can be merged together into a single second data file 247. The first and second constituent data files can be merged in a stereo fonnat where the first constituent audio data from the first constitLielit data file 243 is stored on a first audio track of the second data file 247 and the second constituent audio data from the second constittient data file 245 is stored on a second audio track of the second data file 247. Alteniatively, the first and second constittient data files can be merged in a mono format where the first coiistituent audio data from the first constituent data file 243 and the second constituent audio data from the second constituent data file 245 are stored on a first audio t7aclf of the second data file 247. Additionally, the first and second constittient atidio data can be merged into a docunZent having a textual translation of the audio data. In such a case, identifiers can be associated with each of the merged first and second constittient audio data in order to associate the merged first constituent audio data with the outside caller, and associate the merged second constituent audio data with the call center agent. The second data file 247 can be compressed before being stored in memory.
[0095] It is lcriown in the art that "cradle-to-grave" recording may be used to record all infornnation related to a particular telephone call fiom the time the call enters the contact center to the later of: the caller hanging up or the agent completing the transaction. All of the interactions during the call are recorded, including iilteraction with an IVR
system, time spent on hold, data keyed througlz the caller's lcey pad, conversations with the agent, and screens displayed by the agent at his/her station during the transaction.
[0096] As shown in FIGS. 11-13, once the first and second constituent voice data are separated one from the other, each of the first and second constituent voice data can be independently mined and analyzed. It will be understood that "mining" as referenced herein is to be considered part of the process of analyzing the constituent voice data. It is also contemplated by the present invention that the mining and bellavioral analysis be conducted on either or Uoth of the constittient voice data.
[0097] Even with conventional audio mining technology, application of linguistic-based psychological behavioral models directly to an audio file can be very difficult. In particular, disparities in dialect, phonemes, accents and inflections can irnpede or render Uurdensome accurate ietentitication of words. And wliile it is contemplated by the present invention that mining and analysis in accordance with the present invention can be applied directly to voice data configured in audio format, in a preferred embodiment of the present invention, the voice data to be mined and analyzed is first translated into a text file. It will be understood by those of slcill that the translation of audio to text and subsequent data mining may be accomplished by systenis luiown in the art.
[0098J As showii in FIGS. 11-13, the separated voice data is mined for behavioral signifiers associated with a linguistic-based psychological behavioral model.
In particular, the method of the present invention searches for and identifies text-based keywords (i.e., behavioral signifiers) relevant to a predeterrnined psychological Uehavioral model.
[0100] In one embodinient, the behavioral assessment data 55 inalttdes sales effectiveness data. According to such an enzbodiment, the voice data is mined for linguist indicators to determine situations in which the call center agent made a sale or failed at an opportunity to malce a sale. The failed opporti.inities may include failure to make an offer for a sale, making an offer and failure in colnpleting the sale, or failure to make a cross-sale.
[0101J The resultant behavioral assessment data 55 is stored in a database so that it may subsequently be used to comparatively analyze against bellavioral assessment data derived from analysis of the other of the first and second constituent voice data 56.
The software considers the speech segment patterns of all parties in the dialog as a whole to refine the behavioral and distress assessment data of each party, making sure that the final distress and behavioral results are consistent with patterns that occur in human interaction. Altenlatively, the raw behavioral assessment data 55 derived from the analysis of the single voice data may be used to evaluate qualities of a single cominunicant (e.g., the customer or agent Ueliavioral type, etc.). The results generated by analyzing voice data through application of a psychological behavioral nlodel to one or both of the first and second constituent voice data can be grapliically illustrated as discussed in fiirther detail below.
[01021 It should be noted that it is contemplated that any laiown linguistic-based psychological behavioral model be employed without departing fronl the present invention.
It is also contemplated that more than one linguistic-based psychological behavioral model be used to analyze one or Uoth of the first and second constiti.ient voice data.
[0103J In addition to the behavioral assessment of voice data, the method of the present invention may also einploy distress analysis to voice data. As may be seen in FIG. 2, linguistic-based distress analysis is preferably conducted on both the textual translation of the voice data and the audio file containing voice data. Accordingly, linguistic-based analytic tools as well as non-linguistic analytic tools may be applied to the audio file. For exaniple, one of skill in the art may apply spectral analysis to the audio file voice data wliile applying a word spotting analytical tool to the text file. Linguistic-based word spotting analysis and algoritlnns for identifying distYess can be applied to the textual translation of the cominunication. Preferably, the resultant distress data is stored in a database for subsequent analysis of the coinmunication.
[0104] As shown in FIGS. 2, it is also often desirable to analyze non-linguistic phone events occurring during the course of a conversation such as hold times, transfers, "dead-air,"
overtalk, etc. Accordingly, in one einbodiment of the present invention, phoue event data resulting from analysis of these non-linguistic events is generated.
Preferably, the phone event data is generated by analyzing non-linguistic infornzation from both the separated constituent voice data, or from the subsequently generated audio file containing at least some of the remerged audio data of the original audio wavefonn. It is also contenlplated that the phone event data can be generated before the audio wavefoi7n is separated.
[0105] According to a preferred einbodiment of the invention as shown in FIG.
13, both the first and second constitLient voice data are mined and analyzed as discussed above 64, 66.
The resulting behavioral assessment data 55, phone event data 70 and distress assessment data 72 from each of the analyzed first and second constituent voice data are comparatively analyzed in view of the parameters of the psychological behavioral model to provide an assessment of a given comnzunication. From this comparative analysis, call assessment data relating to the totality of the call may be generated 56.
[0106] Generally, call assessment data is comprised of behavioral assessment data, phoile event data and distress assessment data. The resultant call assessment data may be subsequently viewed to provide an objective assessnlent or rating of the quality, satisfaction or appropriateness of the interaction between an agent and a customer. In the instance in which the first and second constituent voice data are comparatively analyzed, the call assessment data may generate resultant data useful for charaeterizing the success of the interaction between a customer and an agent.
[0107] Thus, as shown in FIGS. 11 and 12, w11en a coniputer program is enlployed according to one embodinient of the present invention, a plurality of code segments are provided. The program con7prises a code seginent for receiving a digital electronic signal carrying an audio waveform 46. In accordance with the voice separation software described above, a code seginent identifies a comrnunication protocol associated with the telephonic signal 47. A code segment is also provided to separate first and second constittient voice data of the cominunication one from the other by recording the audio waveforin in stereo foi7nat to a first electronic data file which has a first and second audio tYaclc 48.
As discussed above, the first constituent voice data is automatically recorded on the first atidio track based on the identified conimunication protocol, and the second constituent voice data is automatically recorded on the second atidio traclc based on the identified coznmunication protocol.
[0108] The software also includes a code segment for separately applying a non-linguistic based analytic tool to each of the separated first and second constituent voice data, and to generate phone event data coiresponding to tlle analyzed voice data 50. A code segment translates each of the separated first and second coiistituent voice data into text format and stores the respective translated first and second constituent voice data in a first and second text file 52. A code segment analyzes the first and second text files by applying a predeterinined linguistic-based psychological beliavioral model thereto 54.
The code seginent generates either or both of Uehavioral assessment data and distress assessment data corresponding to each of the analyzed first and second constituent voice data 54.
[0109] A code seginent is also provided for generating call assessment data 56. The call assessment data is resultant of the comparative analysis of the behavioral assessment data and distress assessment data corresponding to the analyzed first voice data and the behavioral assessment data and distress assessment data corresponding to the analyzed second voice data. A code seginent then transmits an output of event data coi7esponding to at least one identifying indicia (e.g., call type, call time, agent, customer, etc.) 58.
This event data is comprised of a call assessment data corresponding to at least one identifying indicia (e.g., a CSR name, a CSR center identifier, a customer, a customer type, a call type, etc.) and at least one predetemlined time inteival. Now will be described in detail the user interface for accessing and manipulating the event data of an analysis.
[0110] In one embodiment of the preseiit invention shown in FIG. 13, the analysis of the constitLient voice data includes the steps of: translating constituent voice data to be analyzed into a text fonnat 60 and applying a predetermined Iinguistic-based psychological behavioral model to the translated constituent voice data. In applying the psychological behavioral model, the translated voice data is inined 62. In this way at least oarie of a plurality of behavioral signifiers associated with the psychological behavioral nlodel is autonlatically identified in the translated voice data. When the behavioral signifiers are identified, the behavioral signifiers are automatically associated with at least one of a plurality of personality types 68 associated witli tlle psychological behavioral mode164, 66. By applying appropriate algoritlnns behavioral assessnient data coiresponding to the analyzed constituent voice data is generated 55.
[0111] The metliod and systeni of the present invention is usefiil in improving the quality of customer interactions witll agents and ultimately custonier relationships.
In use, a customer wishing to engage in a service call, a retention call or a sales will call into (or be called by) a contact center. When the call enters the contact center it will be routed by appropriate ineans to a call center agent. As the interaction transpires, the voice data will be recorded as described herein. Either contemporaneously with the interaction, or after the call interaction has concluded, the recorded voice data will be analyzed as described herein. The results of the analysis will generate call assessment data comprised of Uehavioral assessment data, distress assessment data and phone event data. This data may be subsequently used by a supervisor or trainer to evaluate or train an agent, or talce otller remedial action such as call back the customer, etc.
[0112] As indicated above, it is often desirable to train call center agents to improve the quality of customer interactions with agents. Tlius, as shown in FIGS. 33-36, the present invention provides a method for training the call center agent by analyzing telephonic communications between the call center agent and the customer. In one emUodiment, a plurality of the pre-recorded first communications between outside callers and a specific call center agent are identified based on an identifying criteria 601. The pre-recorded first communication can be one of the separated constituent voice data or the subsequently generated audio file containing at least some of the remerged audio waveforin of the original audio wavefonn.
[0113] The pre-recorded first connnunications to be used in training the call center agent are identified by coinparatively analyzing the identifying criteria in view of event data 602.
The event data can include behavioral assessment data, plione event data, and/or distress assessment data of the communications. For example, the identifying criteria caii be phone event data such as excessive hold/silence tiine (e.g., caller is placed on hold for greater than predeterniined time - e.g., 90 seconds - or there is a period of silence greater than a predetemiined amount time - e.g., 30 seconds) or long duration for call type (i.e., calls that are a predetermined percentage - e.g., 150% - over the average duration for a given call type).
Additionally, the identifying criteria can be distress assessment data such as upset customer, unresolved issue or program dissatisfaction or an other data associated with distress assessment data. It is contemplated that the system identify potential identifying criteria based on an analysis of the behavioral assessnient data, phone event data, and/or distress assessment data of the cominunications.
[0114] From this coniparative analysis, coaching assessment data is generated.
The coaching assessment data relates to the identified pre-recorded first communications corresponding to the identifying criteria 604. For example, if the identifyiilg criteria is excessive hold/silence time, the coaching assessment data includes pre-recorded first communications having excessive hold/silence time. The resulting coaching assessment data is stored in a database so that it subsequently can be used to evaluate and/or train the call center agent to improve perforniance in view of the identifying criteria.
Tl1us, if the identifying criteria were excessive hold/silence time, the call center agent would be trained to reduce the amount of excessive hold/silence tiune calls.
[0115] The coaching assessment data can fiirtller include first perfoniiance data related to the overall perfonllance of the call center agent with respect to the identifying criteria. The first performance data can be derived from an analysis of the identified pre-recorded first coininunication with respect to all communications - i.e., identified pre-recorded first coinmunication percentage (the percentage of identified pre-recorded first communications out of total number of communications) or identified pre-recorded connmunication (total iiumUer of identified pre-recorded first coinmunications). A first perfonnance score for each identified pre-recorded first coininunication may be generated by analyzing each identified pre-recorded first comnlunication and the corresponding first perfoi7nance data. A composite first performance score may be generated corresponding to the aggregate of the first performance scores of the plurality of identified pre-recorded first communications.
[0116] The coaching assessment data can be comparatively analyzed against a predetermined criteria value threshold to evaluate the call center agent's perforinance or against event data derived from a plurality of identified second pre-recorded coinmunications to determine if training was effective 606. As discussed above, the threshold may be a predetermined criteria set by the call center, the customer, or otlier objective or subjective criteria. Altenlatively, the threshold may set by the performance score.
[0117] In order to evaluate a call center agent, the eoaching assessment data is comparatively analyzed against a predeternlined identifying criteria value threshold. In one embodiment, the first perforn-iance data related to the identified pre-recorded first comniunication is comparatively analyzed witli the predetei711ined identifying criteria value threshold 614. Based on the resultant comparative analysis, a notification is generated 616.
For example, the percentage of excessive hold/silence calls in the pre-recorded first cominunications is compared with the identifying criteria value tllreshold. If the percentage of excessive hold/silence calls in the pre-recorded first connnunications is greater than the identifying criteria value tlireshold, the call center agent is undetperforining and a notification is automatically generated 616.
[0118] In one etnbodiment, the coaching assessment data includes sales effectiveness data. The sales effectiveness data related to the identified pre-recorded first communications is coniparatively analyzed against a predetennined identifying criteria value threshold. For example, the percentage of calls that the call center agent failed to nialce an offer for a cross-sale is compared witli the identifying criteria value threshold. If the percentage of calls that the call center agent failed to make an offer for a cross-sale is greater than the identifying criteria value threshold, the call center agent is undeiperfortning, and a notification is generated.
[0119] In another etnbodiinent, the first perfoi.-tnance score for each identified pre-recorded first communication is compared with the second perfomlance score for the identifying criteria value threshold. In this case, if a predetermined number of first performance scores are less than (or greater than) the identifying criteria value tlireshold, a notification is generated. In another embodinzent, the composite first perfortnance score for the identified pre-recorded first communications is compared with the second performance score for the identifying criteria value tlireshold. If the first composite performance score is less than (or greater than) the second composite performance score, a notification is gerierated.
[0120] Preferably, the notification is an electronic communication, such as an email transmitted to a supervisor or trainer indicating that the call center ageilt is underperfroming.
The notification may be any other type of conlinunication, such as a letter, a telephone call, or an automatically generated message on a website The notification pertnits the supervisor or trainer to take remedial action, such as set up a training session for the call center agent. In one embodiment, the coaching assessment data related to an identifying criteria can be comparatively analyzed against the identifying criteria value threshold for a plurality call center agents. Based on the collective comparative analysis, a notification is generated if a predetermined number or percentage of call center agents are undezperfonning.
hl this manner, the trainer or supervisor is notified that nniltiple call center agents need to be trained with respect to the same criteria.
[0121] As noted above, the identifying eriteria of tl-te coaching assessment data can also be used to train a call center agent. In order to detennine if the call center agent training was effective, the coacliing assessment data can be comparatively analyzed against event data derived from a plurality of identified second pre-recorded connnunications. To detennine if the training was effective, the second pre-recorded communications should have taken place after the call center agent training session. The pre-recorded second connnunications are identified according to the same identifying criteria used to identify the pre-recorded first coinmunications in the coaching assessment data 608. Similar to the pre-recorded first communications, the pre-recorded second communications can be one of the separated constituent voiced data or the subsequently generated audio file containing at least some of the remerged audio wavefonn of the original audio wavefonn.
[0122] Second perforinance data related to the overall perforinance of the call center agent with respect to the pre-recorded second communications can be generated.
As with the first performance data, the second performance data can be derived from an analysis of the identified pre-recorded second coinmunication with respect to all communications - i.e., identified pre-recorded second communication percentage (the percentage of identified pre-recorded second conimunications out of total nunlber of con7munications) or identified pre-recorded coniinunication (total number of identified pre-recorded second communications).
A second performance score for each identified pre-recorded second cominunication nlay be generated by analyzing each identified pre-recorded second conununication and the corresponding second performance data. A composite second perforinance score may be generated corresponding to the aggregate second performance score for each of the plurality of identified pre-recorded second coininunications.
[0123] The second perfonnance data related to the identified pre-recorded second communications is comparatively analyzed with the first perfonnance data of the coaehing assessment data 610. Based on the resultant coinparative analysis, a notification is generated 612.
[0124] In one embodiment, the identified pre-recorded second communication percentage is compared with the identified pre-recorded first comnlunication percentage.
For example, the percentage of excessive hold/silence calls in the pre-recorded first conununications that took place before the training session is compared with the percentage of excessive hold/silence calls in the pre-recorded second comintulications that took place after the training session 610. If the percentage of excessive hold/silence calls in the pre-recorded second coinmunications is less than the percentage of excessive hold/silence calls in the pre-recorded first communications, the training session was successfiil.
Conversely, if the percentage of excessive hold/silence calls in the pre-recorded second communications is greater tlian the percentage of excessive hold/silence calls in the pre-recorded first connnunications, the training session was unsuccessfitl and a notification is autoniatically generated 612.
[0125] In another einbodiment, the first performance score for each identified pre-recorded first coinnlunication is compared witli the second perfoi7nance score for each identified pre-recorded second conlmunication. In this case, if a predeterznined number of second perfonnance scores are less tl2an (or greater tl7an) a predeterinined ntunber of first perfonnance scores, a notification is generated. In another embodinlent, the composite first performance score for the identified pre-recorded first communications is compared with the composite second performance score for the identified pre-recorded second communications.
If the second coniposite perforznance score is less than (or greater than) the first coniposite performance score, a notification is generated.
[0126] Preferably, the notification is an electronic communication, such as an email transinitted to a supervisor or trainer indicating that the training session for the call center agent was unsuccessfiil. The notification perniits the supervisor or trainer to take reniedial action, such as set up anotlier training session for the call center agent. In one embodiment, the coacliing assessment data related to an identifying criteria can be coinparatively analyzed against event data derived from a plurality of identified second pre-recorded connnunications for a plurality of call center agents. Based on the collective conlparative analysis, a notification is generated if a predetemiined nuinber or percentage of call center agents have unsuccessfiil training sessions. In this manner, the trainer or supervisor is notified that inultiple call center agents need to be trained with respect to the same criteria.
[0127] As indicated above, analysis of all or portions of the call assessnlent data may be used to talce remedial action, such as call back the customer, etc. This analysis and resulting responsive coinmunication is usefizl in reducing the attrition of customers who call the contact center. Thus, as shown in FIG. 37, the present invention the present invention provides a method for generating a retention strategy by analyzing a telephonic conrmunication between a customer and a call center agent.
[0128] In one embodiment, the present method analyzes a telephonic communication by applying a pre-detennined retention attrition analysis to the telephonic coinn-iunication.
Preferably, the pre-detemlined retention attrition analysis mines for significant words within one or both of the separated first and second constitlient voice data 62, and applies a linguist-based model to identify words 650. It is conten-iplated that the linguist-based model is the pre-detennined Iingl.iist-based psychological bellavioral model. The linguist-based model mines for words associated with potential attrition of the customer. In another embodiment, the present method mines for such significant words within the merged second data file 247 described above, and applies the linguist-based psychological model to the identified words.
Alteniatively, only the customer's voice data file can be nlined for significant words. In yet anotlier enibodiment, the pre-determined retention attxition analysis analyzes event data to generate a retention strategy. The event data can include behavioral assessment data, distress assessment data, and/or plione event data.
[01291 When a behavioral signifier is identified within the voice data 62, the identified behavioral signifier is executed against a system database which maintains all of the data related to attrition of a custorner. Based on the behavioral signifiers identified in the analyzed voice data, a predeterinined algoritlun 64 is used to calculate an attrition probability defining the likelihood that a customer will leave the company utilizing the call center 650. In the preferred embodiinent, the attrition probability can also inchide an attrition probability score that is calculated Ua.sed on the attrition probability. Looking at all the speech segments in conjunction with attrition infornlation the software determines the attrition probability by weighing a numUer of factors such as tinling, position, quantity and interaction between the parties in the dialog.
[0130] In anotller enibodinient, when a particular event is identified titllin the behavioral assessment data, distress assessment data and/or phone event data, the event is executed against a system database wliich maintains all of the data related to attrition of a customer based on the particular event. Based on this identification, an atfirition probability is calculated.
[01311 The attrition probability and/or attrition probability score is comparatively analyzed against customer value data associated witll the customer of the telephone call being analyzed 654. Preferably, the customer value data is customer infonnation that is inputted or calculated by the systein 652. The customer value data may include data regarding the length of the customer relationship, the alnount of money the customer has spent during the customer relationsliip, other data that assists a company in valuing a customer or any a designation given to the customer based on the aforementioned data. For example, the customer value data for the customer of the telephone call may indicated that the customer is a"platinum level" member and that the customer has been a customer for twenty years. In the preferred enibodiment, the customer value data can also include a customer value data score that is calculated based on the customer value data.

101321 A customer specific retention strategy is selected from a plurality of retention strategies stored in memory based on the comparison of the attrition probability witli the customer value data. Alteniatively, the customer specific retention strategy can be selected based on the comparison of the attrition probability score with the customer value data score.
The retention strategies may include, for example, a responsive written communication to the customer, a responsive oral communication to the customer, sending a complementary to the customer, and/or any other strategies, as may be dictated by the customer and/or coinpany. In one einbodiment, the responsive written connnunication is autonlatically generated as an email or a letter addressed to the customer.
[0133] It is contemplated that a notification be generated outlining the customer specific retention strategy. As with the metllod of training a call center agent, as described above, preferably, the notification is an electronic communication, such as an email, transmitted to a supervisor. Alternatively, the notification may be any other type of cominunication such as a letter, a telephone call, or an automatically generated message on a website.
The notification permits the supervisor to take remedial action by implementing tlle retention strategy.
[0134] Graphical and pictorial analysis of the call assessment data (and event data) is accessible through a portal by a suUsequent user (e.g., a supervisor, training instructor or monitor) through a grapliical user interface. A user of the system 1 described above interact with the system 1 via a unique graphical user interface ("GUI") 400. The GUI
400 enaUles the user to navigate tlirougli the system 1 to obtain desired reports and infonnation regarding the caller interaction events stored in memory. The GUI 400 can be part of a software program residing in whole or in part in the a computer 12, or it may reside in whole or in part on a server coupled to a coinputer 12 via a networle connection, such as through the Internet or a local or wide area networlc (LAN or WAN). Moreover, a wireless connection can be used to link to the networlc.
[0135] In the embodiment shown in FIGS 14-32, the system 1 is accessed via an Internet connection from a coinputer. Kl1own browser tecluiology on the computer can be implenzented to reach a server hosting the system program. The GUI 400 for the system will appear as Internet web pages on the display of the computer.
[01361 As shown in FIG. 14, the GUI 400 initially provides the user witli a portal or "Log On" page 402 that provides fields for input of a user name 404 and password 406 to gain access to the system. Additionally, the GUI 400 can direct a user to one or more pages for setting up a user name and password if one does not yet exist.

[01371 Referring to FIG. 15, once logged into the system 1, the user can navigate tllrougli the prograin by clicking one of the elements that visually appear as tabs generally on the upper portion of the display screen below any tool bars 408. In the emUodiment shown in FIG. 15, the system 1 includes a PROFILES tab 410, a REVIEW tab 412, a METRICS
tab 414 and a COACHING tab 620. A variety of the other tabs with additional inforination can also be inade available.
[0138] The conlputer program associated with the present invention can be utilized to generate a large variety of reports relating to the recorded call interaction events, the statistical analysis of each event and the analysis of the event from the application of the psychological model. The GUI 400 is configured to facilitate a user's request for a specific reports and to visually display the Reports on the user's display.
[0139] The REVIEW tab 412 enables the user to locate one or more caller iizteraction events (a caller interaction event is also herein referred to as a "call") stored in the memory.
The REVIEW tab 412 includes visual date fields or linlcs 416, 418 for inputting a"frozn" and "to" date range, respectively. Clicking on tlle links 416, 418 will call a pop-up calendar for selecting a date. A drop down meriu or input field for entering the desired date can also be ttsed.
[0140] The caller interaction events are divided into folders and listed by various categories. The folders can be identified or be sorted by the following event types: upset customer/issue unresolved; upset customer/issued resolved; program dissatisfaction; long hold/silence (e.g., caller is placed on hold for greater than a predetennined time - e.g., 90 seconds - or there is a period of silence greater tlian a predetei-mined anlount of time - e.g., 30 seconds); early hold (i.e., customer is placed on hold within a predeterinined amount of time -e.g., 30 seconds - of initiating a call); no authentication (i.e., the agent does not authorize or verify an aceount within a predetennined time - e.g., the first three minutes of the call);
inappropriate response (e.g., the agent exhiUits inappropriate language during the call); absent agent (i.e., incoming calls where the agent does not answer the call); long duration for call type (i.e., calls that are a predetemlined percentage over -e.g., 150% - the average duration for a given call type); and transfers (i.e., calls that end in a transfer).
The categories include:
customers, CSR agents, and customer service events.
[0141] The REVIEW tab 412 includes a visual liiik to a custoniers folder 420.
This includes a list of calls subdivided by customer type. The customer folder 420 may include subfolders for corporate subsidiaries, specific promotional programs, or event types (i.e., upset customer/issue unresolved, etc.).

[0142] The REVIEW tab 412 also includes a visual linlc to call center or CSR
agent folders 422. This includes a list of calls divided by call center or CSR
agents. The initial brealcdown is by location, followed by a list of managers, and then followed by the corresponding list of agents. The REVIEW tab 412 also includes a visual linlc to a customer service folders 424. This includes a list of calls subdivided by caller events, call center or CSR agent, and other relevant events.
[0143) The REVIEW tab 412 also includes a visual SEARCH linlc 426 to enable the user to search for calls based on a user-defined criteria. This include the date range as discussed above. Additionally, the user can input cerfiain call characteristics or identifying criteria. For exainple, the user can input a specific call ID nuniUer and click the SEARCH
linlc 426. This rettirns only the desired call regardless of the date of the call. The user could choose an agent from a drop down menu or list of available agents. This retui7ls all calls from the selected agent in the date range specified. The user could also choose a caller (again fiom a drop down menu or list of available callers). This returns all calls from the selected caller(s) within the date range.
[0144] The results from the search are visually depicted as a list of calls 428 as shown in FIG. 16. Cliclcing on any call 430 in the list 4281inks the user to a call page 432 (as shown in FIG. 17) that provides call data and linlcs to an ai.idio file of the call which can be played on spealcers connected to the user's computer.
[0145] The call page 432 also includes a conversation visual field 434 for displaying a time-based representation of characteristics of the call based on the psychological beliavioral model. The call page 432 displays a progress bar 436 that illustrates call events n7arlced with event data shown as, for example, colored points and colored line segments. A
key 440 is provided explaining the color -coding.
[0146] The call page 432 fiirther includes visual control elements for playing the recorded call. These inch.ide: BACK TO CALL LIST 442; PLAY 444; PAUSE 446; STOP 448;
RELOAD 450; REFRESH DATA 452 and START/STOP/DURATION 454. the START/STOP/DURATION 454 reports the start, stop and duration of distinct call seginents occurring in the call. The distinct call segments occur when there is a transition from a caller led conversation to an agent led conversation - or visa versa - and/or the nattire of the discussion shifts to a different topic).
[0147] The REVIEW tab 412 also provides a visual statistics linlc 456 for displaying call statistics as shown in FIG. 18. The statistics can include inforniation such as call duration, average duration for call type, caller talk tiine, number of holds over predetennined tinie periods (e.g., 90 seconds), number of silences, customer satisfaction score, etc.
[0148] The REVIEW tab 412 also provides a comments linlc 458. This will provide a supervisor with the ability to docunient comments for each call that can be used in follow-up discussions with the appropriate agent.
[0149] The METRICS tab 414 allows the user to generate and access Reports of caller interaction event information. The METRICS tab 414 includes two folders: a standard Reports folder 460 and an on-demand Reports folder. The standard reports folder 460 includes pre-defined call perforinance reports generated by the analytics engine for daily, weekly, monthly, quarterly, or annual tinie intezvals. These Reports are organized around two key dimensions: caller satisfaction and agent performance. The on-denzand reports folder 462 includes pre-defiiled call performance reports for any time interval based around two lcey dimensions: caller and agent.
[0150] The GUI 400 facilitates generating summaiy or detailed Reports as shown in FIG.
19. The user can select a Report time range via a pop-up calendar. For summaiy Reports, the user can select from: client satisfaction; summary by call type; and non-analyzed calls.
For detailed Reports, the user can indicate the type of Repo~.~t requested and click the Open Reports linlc 464. Additionally, the user can generate Program Reports. The user selects a client and filters the client by departments or divisions.
[0151] A CLIENT SATISFACTION REPORT 466 is showil in FIG. 20. The client satisfaction Report 466 is a summary level report that identifies analysis results by client for a specified time intezval. The CLIENT SATISFACTION REPORT 466 contains a composite Satisfaction Score 468 that ranks relative call satisfaction across event filter criteria. The CLIENT SATISFACTION REPORT 466 is also available in pre-defined time inteivals (for example, daily, weekly, monthly, quarterly, or amlually).
[0152] The CLIENT SATISFACTION REPORT 466 includes a number of calls column 470 (total nun2ber of calls analyzed for the associated client dtiring the specified reporting interval), an average duration coluinn 472 (total aiialyzed talk time for all calls analyzed for the associated client divided by tlie total nuiilber of calls analyzed for the client), a greater than (">") 150% duration coh.uiu1474 (percentage of calls for a client that exceed 150% of the average duration for all calls per call type), a greater than 90 second liold column 476 (percentage of calls for a client where the call center agent places the client on hold for greater than 90 seconds), a greater than 30 second silence coluini1478 (percentage of calls for a client where there is a period of continuous silence within a call greater than 30 seconds), a customer dissatisfaction column 480 (percentage of calls for a client where the caller exliibits dissatisfaction or distress - these calls are in the dissatisfied caller and upset caller/issue unresolved folders), a program dissatisfaction column 482 (percentage of calls where the caller exhibits dissatisfaction with the program), and a caller satisfaction column 484 (a composite score that represents overall caller satisfacfiion for all calls for the associated client).
[0153] The caller satisfaction column 484 is defined by a weighted percentage of the following criteria as shown in FIG. 21: >150% duration (weight 20%), >90 second hold (10%), >30 second silence (10%), caller distress (20%), and program dissatisfaction (20%).
All weighted values are subtracted from a starting point of 100.
[0154] The user can generate a suminary by CALL TYPE REPORT 486 as shown in FIG. 22. The CALL TYPE REPORTS 486 identify analysis results by call type for the specified interval. The sununary by call type Report 486 contains a composite satisfaction score 488 that ranlcs relative client satisfaction across event filter criteria. The CALL TYPE
REPORT 488 includes a call type column 490, as well as the other columns described above.
[0155] The user can generate a NON-ANALYZED CALLS REPORT 492 as sliown in FIG. 23. The NON-ANALYZED CALLS REPORT 492 provides a summary level report that identifies non-analyzed calls for the specified time interval.
[0156] As shown in FIG. 24, tlle user can generate a DETAIL LEVEL REPORT 494.
The detail level Report 494 identifies analysis results by client and call type for the specified time interval. The DETAIL LEVEL REPORT 494 contain a coniposite satisfaction score 496 that ranks relative client satisfaction for each call type across event filter criteria.
[0157] A PROGRAM REPORT 498 is shown in FIG. 25. Tliis is a detail level report that identifies analysis results by client departments or divisions for the specified time interval.
THE PROGRAM REPORT 498 contain a composite satisfaction score 500 that ranlfs relative client satisfaction for each call type across event filter criteria.
[0158] The user can also generate a number of CALL CENTER or CSR AGENT
REPORTS. These include the following summary reports: coiporate summaiy by location;
CSR agent perfoiznance; and non-analyzed calls. Additionally, the tiser can generate team reports. The team Reports can be broken down by location, teani or agent.
[0159] A CORPORATE SUMMARY BY LOCATION REPORT 502 is shown in FIG.
26. This detail level Report 502 identifies analysis results by location for the specified time inteival, and contains a composite score that rai-Ac relative elient perfonnance for each call type across event filter criteria. The CORPORATE SUMMARY BY LOCATION REPORT

_ _ 502 includes a location coltunn 504 (tliis identifies the call center location that received the call), a nutnber of calls column 506 (total number of calls received by the associated call center location during the specified reporting interval, an average duration column 508 (total analyzed tallc time for all calls analyzed for the associated CSR agent divided by the total nunzber of calls analyzed for the agent), a greater than 150% duration colunni (percentage of calls for a CSR agent that exceed 150% of the average duration for all calls, a greater tllan 90 second hold coluinn 512 (percentage of calls for a CSR agent wlzere the CSR
places the caller on hold for greater than 90 seconds), a greater than 30 second silence column 514 (percentage of calls for a CSR agent where there is a period of continuous silence within a call greater than 30 seconds), a call transfer column 516 (percentage of calls for a CSR agent that result in the caller Ueing transferred), an inappropriate response coltinni 518 (percentage of calls where the CSR agent exliibits inappropriate behavior or langl.iage), an appropriate response column 520 (percentage of calls where the CSR agent exhibits appropriate beliavior or language that result in the dissipation of caller distress - these calls ean be found in the upset caller/issue resolved folder), a no authentication column 522 (percentage of calls where the CSR agent does not authenticate the caller's identity to prevent fraud), and a score column 524 (a composite score that represents overall call center perfonnance for all calls in the associated call center location) [0160] The values 526 in the score column 524 are based on the weigllted criteria shown in FIG. 27. All weighted values are subtracted from a starting point of 100 except for "appropriate response," which is an additive value.
[0161] A CSR PERFORMANCE REPORT 528 is shown in FIG. 28. This is a detail level report that identifies analysis results by CSR for the specified time interval. This Report 528 contains a composite score that rafflcs relative CSR perfoi7nance for each call type across event filter criteria.
[0162] FIG. 29 shows a NON-ANALYZED CALLS REPORT 530. Thi.s is a detail level report that identifies analysis results by non-analyzed CSR calls for a specified time interval.
[0163] A LOCATION BY TEAM REPORT 532 is shown in FIG. 30. This is a summary level report that identifies analysis results by location and team for the specified time interval.
This Report 532 contains a composite score that ranks relative CSR
perforinance across event filter criteria by team.
[0164] FIG. 31 shows a TEAM BY AGENT REPORT 534. This is a stunmary level report that identifies analysis results by team and agent for the specified time interval. These Keports 5-14 contani a composite perfonnance score that ranks relative CSR
perforinance across event filter criteria by agent.
[0165] FIG. 32 shows a CSR BY CALL TYPE REPORT 536. This is detail level report that identifies analysis results by CSR and call type for the specified time interval. These Reports 536 contain a composite perforinance score that ranks relative CSR
perfonnance across event filter criteria by call type.
[0166] The COACHING tab 620 enables a user to locate one of more caller interaction events to evaluate and train call center agents to improve the quality of customer interactions with the agents. The COACHING tab 620 includes visual date fields 622, 624 for inputting a "from" and "to date", respectively. Clicking on the links 416, 418 will call a pop-up calendar for selecting a date. A drop down menu or input field for entering the desired date can also be used.
[0167] The COACHING tab 620 displays caller interaction event infonnation. The caller interaction event inforination includes check boxes for selecting the caller iuteraction event inforination as the identifying criteria 626. Based on the selection of the identifying criteria 626, a plurality of pre-recorded first cominunications between outside caller and a specific call center agent are identified 628. Infonnation relating to the identified criteria is also displayed 630. A value may be entered in visual call field 632 to specify the number of pre-identified calls to display.
[0165] The COACHING tab 620 includes a coaching page 634 to train the call center agent to iinprove perfornnance in view of the identifying criteria, as illustrated in FIG. 35.
The coaching page 634 displays a progress bar 636 that illustrates call events marked with event data shown as, for example, colored points and colored line segments.
The coaching page 634 includes a comment box 640 for the call agent to indicate areas to be trained.
Comments from others may also be displayed. The coaching page 634 includes a check-box 638 for requesting additional training on the selected identifying criteria.
[0169] Referring to FIG. 36, the COACHING tab 620 includes a graphical representation 642 of the number of calls that are identified based on the identifying criteria 644. In one embodiment, the graphical representation displays the percentage of calls identified based on the identifying criteria 644 for each week during an identified time period.
In this maimer, it can be determined if the training session for the call center agent was successful.
[0170] While the specific einbodiments have been illustrated and described, numerous modifications come to mind witliout significantly departing fionz the spirit of the invention, and the scope of protection is only limited by the scope of the accompanying Clainzs.

Claims (18)

1. A computer readable medium adapted to control a computer and comprising a plurality of code segments for analyzing a telephone call between a customer and a call center, the computer readable medium comprising:
a code segment for analyzing voice data of a customer by mining the voice data and applying a pre-determined linguist model to the voice data to calculate an attrition probability;
a code segment for receiving customer value data associated with the customer;
a code segment for comparing the attrition probability with the customer value data;
and, a code segment for generating a retention strategy based on comparing the attrition probability with the customer value data.

2. The computer readable medium of claim 1 further comprising a code segment for separating a telephonic communication into at least a first constituent voice data and a second constituent voice data wherein in the code segment for analyzing voice data of a customer by mining the voice data, the first constituent voice data is analyzed.

3. The computer readable medium of claim 1 further comprising a code segment for generating a notification.

4. The computer readable medium of claim 1 further comprising a code segment for automatically generating a responsive communication based on the retention strategy wherein the responsive communication is at least one of an email, a voice communication, and a written communication.

5. The computer readable medium of claim 4 wherein the type responsive communication generated is based on at least one of behavioral assessment data, distress assessment data and phone event data.

6. The computer readable medium of claim 1, further comprising a code segment for generating event data corresponding to at least one identifying indicia and time interval, the event data comprising at least one of behavioral assessment data, distress assessment data and phone event data.

7. The computer readable medium of claim 6 further comprising a code segment for analyzing event data and a code segment for generating the retention strategy based on the analysis of the event data.

8. The computer readable medium of claim 1, further comprising a code segment for generating an attrition probability score based on the attrition probability, wherein in the code segment for generating the retention strategy, the attrition probability score is compared with the customer value data.

9. The method of claim 1 wherein the pre-determined linguist model is at least a pre-determined linguist-based psychological behavioral model.

10. A computer readable medium adapted to control a computer and comprising a plurality of code segments for analyzing a telephone call between a customer and a call center, the computer readable medium comprising:
a code segment for analyzing a telephonic communication by applying a pre-determined retention attrition criteria to the telephonic communication to calculate an attrition probability;
a code segment for receiving customer value data associated with the customer;
a code segment for comparing the attrition probability with the customer value data;
and, a code segment for generating a retention strategy based on comparing the attrition probability with the customer value data.

11. The computer readable medium of claim 10 further comprising a code segment for separating a telephonic communication into at least a first constituent voice data and a second constituent voice data wherein in the code segment for analyzing the telephonic communication, at least one of the first constituent voice data and the second constituent voice data is analyzed by mining the respective voice data and applying a pre-determined linguist model to the voice data to calculate the attrition probability

12. The computer readable medium of claim 10 further comprising a code segment for generating a notification.

13. The computer readable medium of claim 10 further comprising a code segment for automatically generating a responsive communication based on the retention strategy wherein the responsive communication is at least one of an email, a voice communication, and a written communication.

14. The computer readable medium of claim 13 wherein the type responsive communication generated is based on at least one of behavioral assessment data, distress assessment data and phone event data.

15. The computer readable medium of claim 10, further comprising a code segment for generating event data corresponding to at least one identifying indicia and time interval, the event data comprising at least one of behavioral assessment data, distress assessment data and phone event data.

16. The computer readable medium of claim 15 wherein in the code segment for generating the retention strategy, the retention strategy is generated by at least analyzing the event data.

17. The computer readable medium of claim 10, further comprising a code segment for generating an attrition probability score based on the attrition probability, wherein in the code segment for generating the retention strategy, the attrition probability score is compared with the customer value data.

18. The computer readable medium of claim 10 wherein the pre-determined linguist model is at least a pre-determined linguist-based psychological behavioral model.