Patent Application
20090012826
1. Field of the Invention
The present invention relates to interaction analysis in general and to retrieving insight and trends from categorized interactions in particular.
2. Discussion of the Related Art
Within organizations or organizations' units that handle interactions with customers, suppliers, employees, colleagues or the like, it is often required to extract information from the interactions in an automated and efficient manner. The organization can be for example a call center, a customer relations center, a trade floor, a law enforcements agency, a homeland security office, or the like. The interactions may be of various types, including phone calls using all types of phone systems, recorded audio events, walk-in center events, video conferences, e-mails, chats, captured web sessions, captured screen activity sessions, instant messaging, access through a web site, audio segments downloaded from the internet, audio files or streams, the audio part of video files or streams or the like.
The interactions received or handled by an organization constitute a rich source of customer related information, product-related information, or any other type of information which is significant for the organization. However, retrieving the information in an efficient manner is typically a problem. A call center or another organization unit handling interactions receives a large amount of interactions, mainly depending on the number of employed agents. Listening, reading or otherwise relating to a significant percentage of the interactions would require time and manpower of the same order of magnitude that was required for the initial handling of the interaction, which is apparently impractical. In order to extract useful information from the interactions, the interactions are preferably classified into one or more hierarchical category structure, wherein each hierarchy consists of one or more categories. The hierarchies and the categories within each hierarchy may be disjoint, partly or filly overlap, contain each other, or the like. However, solely classifying the interactions into categories may not yield practical information. For example, categorizing the interactions incoming into a commercial call center into “content customers” and “disappointed customers” would not assist the organization in understanding why customers are unhappy or what can be done to improve the situation.
There is therefore a need in the art for a system and method for extracting information from categorized interactions in an efficient manner. The method and apparatus should be efficient so as to handle large volumes of interactions, and to be versatile to be used by organizations of commercial or any other nature, and for interactions of multiple types, including audio interactions, textual interactions or the like.
The disclosed method and apparatus provide for revealing business or organizational aspects of an organization from interactions, broadcasts or other sources. The method and apparatus classify the interactions into predefined categories. Then additional processing is performed on interactions within one or more categories, and analysis is executed for revealing insights, trends, problems, and other characteristics within such categories.
In accordance with the disclosure, there is thus provided a method for detecting one or more aspects related to an organization from one or more captured interactions, the method comprising the steps of receiving the captured interactions, classifying the captured interactions into one or more predefined categories, according to whether the each interaction complies with one or more criteria associated with each category; performing additional processing on the at captured interaction assigned to the categories to extract further data; and analyzing one or more results of performing the additional processing or of the classifying, to detect the one or more aspects. The method can further comprise a category definition step for defining the categories and the criteria associated with the categories. Alternatively, the method can further comprise a category receiving step for receiving the categories and the criteria associated with the categories. Optionally, the method comprises a presentation step for presenting to a user the aspects. Within the method, the presentation step can relate to presentation selected from the group consisting of: a graphic presentation; a textual presentation; a table-like presentation; a presentation using a third party tool; and a presentation using a third party portal. The method optionally comprises a preprocessing step for enhancing the captured interactions. Optionally, the method further comprises a step of capturing or receiving additional data related to the captured interactions. The additional data is optionally selected from the group consisting of: Computer Telephony Integration data; Customer Relationship Management data; billing data; screen event; a web session event; a document; and demographic data. Within the method, the categorization or the additional processing steps include activating one or more engines from the group consisting of: word spotting engine; phonetic search engine; transcription engine; emotion analysis engine; call flow analysis engine; web activity analysis engine; and textual analysis engine. Within the method, the analyzing step optionally includes activating one or more engines from the group consisting of: data mining; text mining; root cause analysis; link analysis; contextual analysis; text clustering, pattern recognition; hidden pattern recognition; a prediction algorithm; and OLAP cube analysis. Within the method, any of the captured interactions is optionally selected from the group consisting of: a phone conversation; a voice over IP conversation; a message; a walk-in center recording; a microphone recording; an audio part of a video recording; an e-mail message; a chat session; a captured web session; a captured screen activity session; and a text file. The predefined category can be parts of a hierarchical category structure. Within the method, each of the criteria optionally relates to the captured interactions or to the additional data.
Another aspect of the disclosure relates to a computing platform for detecting one or more aspects related to an organization from one or more captured interactions, the computing platform executing: a categorization component for classifying the captured interactions into one or more predefined categories, according to whether each interaction complies with one or more criteria associated with each category; an additional processing component for performing additional processing on the captured interactions assigned to the at least one of the predetermined categories to extract further data; and a modeling and analysis component for analyzing the further data or results produced by the classification component, to detect the aspects. The computing platform can further comprise a category definition component for defining the categories, and the criteria associated with each category. Optionally, the computing platform comprises a presentation component for presenting the aspects. The presentation component optionally enables to present the aspects in a manner selected from the group consisting of: a graphic presentation; a textual presentation; a table-like presentation; and a presentation using a third party tool or portal. The computing platform optionally comprises a logging or capturing component for logging or capturing the captured interactions. The computing platform can further comprise a logging or capturing component for logging or capturing additional data related to the captured interactions. Within the computing platform, the additional data is optionally selected from the group consisting of: Computer Telephony Integration data; Customer Relationship Management data; billing data; screen event; a web session event; a document; and demographic data. Within the computing platform, the categorization component or the additional processing component optionally include activating one or more engines from the group consisting of: word spotting engine; phonetic search engine; transcription engine; emotion analysis engine; call flow analysis engine; web activity analysis engine; and textual analysis. Within the computing platform, the modeling and analysis component optionally activates one or more engines from the group consisting of: data mining; text mining; root cause analysis; link analysis; contextual analysis; text clustering, pattern recognition; hidden pattern recognition; a prediction algorithm; and OLAP cube analysis. Within the computing platform, the captured interactions are optionally selected from the group consisting of: a phone conversation; a voice over IP conversation; a message; a walk-in center recording; a microphone recording; an audio part of a video recording; an e-mail message; a chat session; a captured web session; a captured screen activity session; and a text file. The computing platform can firer comprise a storage device for storing the categories, or the at least one criteria, or the categorization. The computing platform can further comprise a quality monitoring component for monitoring one or more quality parameters associated with the captured interactions.
The present invention will be understood and appreciated more fully from the following detailed description taken in conjunction with the drawings in which corresponding or like numerals or characters indicate corresponding or like components. In the drawings:
The disclosed subject matter provides a method and apparatus for extracting and presenting information, such as reasoning, insights, or other aspects related to an organization from interactions received or handled by the organization.
In accordance with a preferred embodiment of the disclosed subject matter, interactions are captured and optionally logged in an interaction-rich organization or organizational unit. The organization can be for example a call center, a trade floor, a service center, an emergency center, a lawfill interception, or any other location that receives and handles a multiplicity of interactions. The interactions can be of any type, such as vocal interactions including for example phone calls, audio parts of video interactions, microphone-captured interactions and others, e-mails, chats, web sessions, screen events sessions, faxes, and any other interaction type. The interactions can be between any two parties, such as a member of the organization for example an agent, and a customer, a client, an associate or the like. Alternatively the interactions can be intra-organization, for example between a service-providing department and other departments, or between two entities unrelated to the organization, such as an interaction between two targets captured in a lawful interception center. The user, such as an administrator, a content expert or the like defines categories and criteria for an interaction to be classified into each category. Alternatively, categories can be received from an external source, or defined upon a statistical model or by an automatic tool. Further, the categorization of a corpus of interactions can be received, and criteria for interactions can be deduced, for example by neural networks. Each interaction is matched using initial analysis against some or all the criteria associated with the categories. The interaction is assigned to one or more categories whose criteria are matched by the interaction. The categories can relate to different products, to customer satisfaction levels, to problem reported or the like. Further, each interaction can be tested against multiple categorizations. For example, an interaction can be assigned to a category related to “unhappy customers, to a category related to “product X”, and to a category related to “technical problems”. The categorization is preferably performed by efficient processing in order to categorize as many interactions as possible.
After the initial analysis and classification, the interactions in one or more categories are further processed by targeted analysis. For example, it may be reasonable for a business with limited resources to further analyze interactions assigned to an “unhappy customer” category and not to analyze the “content customer category”. In another example, the company may prefer to further analyze categories related to new products over analyzing other categories.
The analysis of the interactions in a category is preferably targeted, i.e. consists of analysis types that match the interactions. For example, emotion analysis is more likely to be performed on interactions related to an “unhappy customer” category than on interactions related to “technical problems” category. The products of the targeted analysis are preferably stored, in a storage device.
Preferably, the initial analysis used for classification uses fast algorithms, such as phonetic search, emotion analysis, word spotting, call flow analysis, i.e., analyzing the silence periods, cross over periods, number and length of hold periods, number of transfers or the like, web flow analysis, i.e. tracking the activity of one or more users in a web site and analyzing their activities, or others. The advanced analysis optionally uses more resource-consuming analysis, such as speech-to-text, intensive audio analysis algorithms, data mining, text mining, root cause analysis being analysis aimed at revealing the reason or the cause for a problem or an event from a collection of interactions, link analysis, being a process that finds related concepts related to the target concept such as a word or a phrase, contextual analysis which is a process that extracts sentences that include a target concept out of texts, text clustering, pattern recognition, hidden pattern recognition, a prediction algorithm, OLAP cube analysis, or others. Third party engines, such as Enterprise Miner™ manufactured by SAS (www.sas.com), can be used as well for advanced analysis. Both the initial analysis and the advanced analysis may use data from external sources, including Computer-Telephony-Integration (CTI) information, billing information, Customer-Relationship-Management (CRM) data, demographic data related to the participants, or the like.
Once the further analysis is done, optionally modeling is farther performed on the results. The modeling preferably includes analysis of the data of the initial analysis upon which the interaction was classified, and the advanced analysis. The advanced extraction may include root cause analysis, data mining, clustering, modeling, topic extraction, context analysis or other processing, which preferably involves two or more information types gathered during the initial analysis or the advanced analysis. The advanced extraction may further include link analysis, relating to extracting phrases that have a high co-appearance frequency within one or more analyzed phrases, paragraphs or other segments.
The results of the initial analysis, advanced analysis and modeling are presented to a user in one or more ways, including graphic representation, table representation, textual representation, issued alarms or alerts, or the like. The results can be further fed back and change or affect the classification criteria, the advanced analysis, or the modeling techniques.
Referring now to
All components of the system, including capturing/logging components 132, the engines activated by categorization component 138 additional processing component 142, modeling and analysis component 144 and presentation component 146 are preferably collections of instruction codes designed to be executed by one or more computing platforms, such as a personal computer, a mainframe computer, or any other type of computing platform that is provisioned with a memory device (not shown), a CPU or microprocessor device, and several I/O ports (not shown). Alternatively, each component can be implemented as firmware ported for a specific processor such as digital signal processor (DSP) or microcontrollers, or can be implemented as hardware or configurable hardware such as field programmable gate array (FPGA) or application specific integrated circuit (ASIC). Each component can further include a storage device (not shown), storing the relevant applications and data required for processing. Each software component or application executed by each computing platform, such as the capturing applications or the classification component is preferably a set of logically inter-related computer instructions, programs, modules, or other units and associated data structures that interact to perform one or more specific tasks. All applications and software components can be co-located and executed by the same one or more computing platforms, or on different platforms. In yet another alternative, the information sources and capturing platforms can be located on each site of a multi-site organization, and one or more of the processing or analysis components can be remotely located, and analyze segments captured at one or more sites and store the results in a local, central, distributed or any other storage.
Referring now to
The user can further select to see only the results associated with specific interactions, such as the interactions captured in a specific time frame as shown in area 240, to indicate analysis parameters, such as on which sides of the interaction the analysis is to be performed, or any other filter or parameter. It will be apparent to a person skilled in the art that the types of the information shown for category 1 are determined according to the way category 1 was defined, as well as the interactions classified into category 1. Alternatively, the analysis and information types defined for category 1 can be common and defined at once for multiple categories and not specifically to category 1. Additional analysis results, if such were produced, can be seen when switching to other screens, for example by using any one or more of buttons 244 or by changing the default display parameters of the system.
It will be appreciated that the screenshot of
Referring now to
Components 315, 325, and 335 are preferably collections of computer instructions, arranged in modules, static libraries, dynamic link libraries or other components. The components are executed serially or in parallel, by one or more computing platforms, such as a general purpose computer including a personal computer, or a mainframe computer. Alternatively, the components can be implemented as firmware ported for a specific processor such as digital signal processor (DSP) or microcontrollers, or hardware or configurable hardware such as field programmable gate array (FPGA) or application specific integrated circuit (ASIC).
Referring now to
The disclosed method and apparatus provide a user with a systematic way of discovering important business aspects and insights relevant to interactions classified to one or more categories. The method and apparatus enable processing of a large amount of interactions, by performing the more resource-consuming processes only on a part of the interactions, rather than on all of them.
It will be appreciated by a person skilled in the art that the disclosed method and apparatus can be activated on a gathered corpus of interactions every predetermined period of time, once a sufficiently large corpus is collected, or once a certain threshold, peak or trend is detected, or according to any other criteria. Alternatively, the classification and additional processing can be performed in a continuous manner on every captured interaction, while modeling and analysis step 415 can be performed more seldom.
The method and apparatus can be performed over a corpus of interactions gathered over a long period of time, even if earlier collected interactions have already been processed in the past. Alternatively, the process can be performed periodically for newly gathered interactions only, thus ignoring past interactions and information deduced thereof.
It will be appreciated by a person skilled in the art that many alternatives and embodiments exist to the disclosed method and apparatus. For example, an additional preprocessing engines and steps can be used by the disclosed apparatus and method for enhancing the audio segments so that better results are achieved.
While preferred embodiments of the disclosed subject matter have been described, so as to enable one of skill in the art to practice the disclosed subject matter. The preceding description is intended to be exemplary only and not to limit the scope of the disclosure to what has been particularly shown and described hereinabove. The scope of the disclosure should be determined by reference to the following claims.
