Method and system for omnichannel supervision

In one embodiment described herein, an omnichannel supervision interface system and method includes a hardware processor, and a graphics engine executed by the processor for displaying a first portion to display, for one contact center agent among a plurality of contact center agents, a near-real time transcript of the contact center agent's conversation with one customer over a plurality of channels, and a second portion to display a first media category of a session of the contact center agent and the customer, wherein the omnichannel supervision interface is operative to display a plurality of channels for the plurality of contact center agents. Related methods, apparatus, and systems are also described.

TECHNICAL FIELD

The present disclosure generally relates to methods and systems for use in supervision of omnichannel systems for monitoring customer to business support interactions.

BACKGROUND

Contact centers are typically busy places where service agents interact with customer calls over a variety of channels, including, but not necessarily limited to: chats, emails, and so forth, while supervisors monitor their progress and manage the volume of issues coming in across all channels. It is important for the work of service agents to be monitored carefully and to be strategically thought-out, since every interaction with a customer costs the business money. Service agents can be co-located together with their supervisor. Service agents can also work as remote employees, a model which is becoming more commonplace as costs are often lower.

Although contact center supervisors serve a managerial role, they often function as mentors, motivators, managers, as well as service agents, in a task requiring supervisors to function as multitaskers as well. They often lead teams of 10-20 agents, and must manage volume across all service channels that are coming into the contact center. At any point, supervisors can be called upon to manage and coach agents, handle tough customers, adjust staff, prepare reports for management, all while ensuring service level and quality objectives are maintained.

Traditionally, supervisors monitor how agents are performing at their jobs to make sure they are productive and working efficiently on all the issues coming in. This often includes looking to standard call center metrics such as an agent's average handle time (AHT) or average speed to answer (ASA), but there is a host of other data the supervisor can look into to get a sense of an agent's workload and performance. Supervisors often walk around the floor to get a sense for how things are going by watching the agents and listening to conversations, while maintaining visibility for agents to ask them questions as well. However, it can be difficult to get that same feel for how an agent is doing when the supervisor or agent is remote.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

An omnichannel supervision interface system and method is described. The omnichannel supervision interface system and method includes a hardware processor, and a graphics engine executed by the processor for displaying a first portion to display, for one contact center agent among a plurality of contact center agents, a near-real time transcript of the contact center agent's conversation with one customer over a plurality of channels, and a second portion to display a first media category of a session of the contact center agent and the customer, wherein the omnichannel supervision interface is operative to display a plurality of channels for the plurality of contact center agents. Related methods, apparatus, and systems are also described.

Reference is now made toFIG. 1, which is a simplified pictorial illustration of an interface100for monitoring and time lapse recording of multiple omnichannel sessions constructed and operative in accordance with an embodiment of the present disclosure. In a typical omnichannel contact center, supervisors may want access to real-time views of which agents are working, which customers are waiting to be served, and what work is in progress in order to monitor performance and proactively take informed action to resolve issues. These issues may include bottlenecks or over-saturated queues for example, that cause customers to suffer long wait times, and it's often up to the supervisor to adjust staff or shift priorities in such situations. It is appreciated that the term “customer” is used herein throughout to refer to a consumer of the services provided by the omnichannel contact center. Other terms may be used as well to refer to a consumer, depending on context. For example, a customer executing a voice call may be referred to as a “caller”, or a customer who has executed a chat may be referred to as a “chatter”. Other terms may be used as well in their appropriate context, as will be appreciated.

Often there is a need for supervisory monitoring either for compliance, coaching, or for quality reasons.FIG. 1depicts an interface100which is designed to enable monitoring and recording multiple interactions—i.e. interactions of multiple agents110A-110F. The interface100also provides for monitoring multiple channels. In the depiction ofFIG. 1, columns for video120, voice130, co-browsing140(i.e., both the customer and the service agent see the same browser, typically enabled by using screen sharing software), chat150, and feeds from Internet of Things (IoT)160enabled devices are shown. Other channels, including, but not limited to screen sharing may also be displayed in other columns. All of the above are displayed on the interface100simultaneously in real-time or near-real-time fashion.

In addition to being displayed on the interface100as described above, the information presently displayed on the interface100is also stored for retrieval and analysis at a later time. A first set of controls170is available to enable viewing information and events displayed in the interface100as a whole, and a set of controls180A-180F is also provided on a per agent basis for each of the multiple agents110A-110F, enabling viewing information and events for each individual agent of the multiple agents110A-110F. The controls are depicted as being the standard symbols used according to IEC 60417—Graphical Symbols for Use on Equipment. These controls enable viewing the stored information in a time lapse fashion, where the information can be viewed on the interface at a speed faster or slower than the original capture rate of the information.

The information displayed is captured is across channels (e.g., voice/video/chat/screen capture etc.) for a particular user to support expert interaction. The retrieval or playback is assembled and displayed in a seamless way such that if the interaction started with a text chat, the text chat is displayed first, and an ensuing video call is then displayed. A session may be played back in a complete fashion, enabling fast forwarding or rewinding during the playback, using the controls180A-180F. Key words may be highlighted during playback as will be described below with reference toFIG. 2.

It is appreciated that the interface100events as they unfold across all channels of the displayed omnichannels. Additionally, the interface enables time lapse recording of these events, and at a later time, playback of the time lapse recording may be performed at the same time for many of the channels or all of the channels. In that a supervisor may need to monitor a large number of agents, such as the multiple agents110A-110F, and follow their interactions over a large number of channels, corresponding to the various columns, e.g. video120, voice130, co-browse140, chat150, etc.), the interface100provides a system which may help the supervisor perform his or her job. Additionally, the interface100may comprise a machine learning sub-system which, over time, will improve at detecting and isolating areas for the supervisor to focus on. The interface100may then bring these areas to the attention of the supervisor.

The machine learning sub-system may be used both in real time, in order to detect a potential problem, and then to send a notification to the supervisor, or, alternatively, in reviewing interactions in an off-line, non-real time fashion, to spot interactions where the supervisor's intervention should have occurred, or where additional follow-up may be necessary on the part of the supervisor (whether in the form of agent training or in the form of contact with a customer). In that there may be a large volume of customer interactions handled by the omnichannel contact center, it may not be possible to review each interaction individually in its entirety in order to isolate problem areas. The machine learning sub-system (and its associated processing unit) enables off-line location and identification of such problem areas. The machine learning sub-system may also prioritize the problematic interactions for which are to be reviewed.

Although not depicted, the interface100might also display a timer for each ongoing interaction between an Agent and a customer. A conversation that goes beyond a limit/threshold from average talk time will be flagged for further attention by the supervisor. The flagging will typically be executed by a rule engine (described below, with reference toFIG. 4). During omnichannel conversation, it is important to note the total time for resolution from start to end, and this time may include time spent engaged in offline conversations like email or SMS. Total time of resolution and number of contacts before resolution is an important parameter that gets tracked and linked directly to customer satisfaction. Information about timing may be performed by the omnichannel system so that supervisors can, if needed, take actions in near real time.

Exemplary detailed descriptions of each monitored channel will be provided in the following discussions.

Reference is now made toFIG. 2, which is a detail200of the interface100ofFIG. 1, focusing on the video column120. Focusing on the video column120grid of Agent Sally110A, a customer video210A is seen. Agent Sally's video220A is also seen, as a small video display, in addition to the customer video210A. A transcript230is also provided of the conversation between the customer, displayed by customer video210A and Agent Sally's video220A. The transcript230show a highlighted word240, depicted as a rectangle around a word, “plugged”. The highlighted word240is a keyword which is highlighted as part of the transcription routine.

Similarly, Agent Tom's video210is displayed, in this case, it is Agent Tom110B himself shown, and the customer's video220B which appears as the smaller video image. In either of the cases of the two video displays,210A and210B, it is the active member of the video session whose video image210A,210B is displayed in the larger display, while the listening member of the video session whose video image220A,220B is displayed in the smaller display. At present, the transcript250of the conversation between Agent Tom110B and a customer receiving help from Agent Tom110B is not showing any highlighted keywords. In some embodiments, tagging might also be implemented. For example, if the call center agent is discussing (regardless of the channel) a refrigerator, then the tag “refrigerator” might be automatically applied to the conversation.

Agent Liz110C is indicated as being ready270to receive a call from the next customer to the call center. By contrast, Agent Rick110D is indicated as not being ready270to receive a call. This might be because Agent Rick110D is on a break or not at his desk at present.

As can be seen in the above description ofFIG. 2, any individual can monitor and record multiple interaction sessions with multiple media types or channel types.FIG. 2depicted the ability to monitor multiple interaction sessions for video. The depiction ofFIG. 2illustrates how the omnichannel support center supervisor can monitor multiple video sessions at one time.

Reference is now made toFIG. 3, which is a detail300of the interface ofFIG. 1, focusing on the row of one agent, in this particular instance, and by way of example, Agent Sally110A. Similar to what was discussed above aboutFIG. 2,FIG. 3depicts how multiple other channels besides video (the video column is omitted fromFIG. 3, for ease of depiction) may be displayed for monitoring at one time. As with video, in the voice column, a live transcript appears, showing several keywords320, depicted with rectangles around them, for emphasis. One repeated keyword, multiple times, might serve as a flag to the supervisor that immediate attention or intervention is called for. For example, repeated use of profanity might be flagged (possibly in a red font, or in bold letters, or some other manner, as is known in the art). Similarly, if the customer were to repeatedly use the same keyword, e.g. “Cloud . . . cloud . . . cloud . . . ”, might serve as a flag to the supervisor that supervisory attention might be called for. The chat window might also have a tag sub-window330, where tags (i.e. a displaying of metadata which might help to describe the subject of the voice (or video) session) are automatically applied. For example, tags might include topics of conversation, such as “Fridge” or “Noise”.

The Co-browse column enables displaying a screen from the Agent's monitor and/or the a screen from customer's monitor340. The Chat column150might function similarly to the video transcripts230,250displayed for the video column120(FIG. 2) and the voice transcript310displayed for the voice column130. That is to say that live chat keywords and tags350might appear in the chat column150.

Tags can be added to a tagging system (not depicted) by system administrators. At a later time, when a chat or a transcript appears in the appropriate location in the interface100(i.e. the chat columns150, the transcript230, etc., the system would then highlight keywords. Additionally, the system might display, for each tagged word, a number of times the word was spoken or mentioned in the conversation. The system can use a rule engine (described below with reference toFIG. 4), and monitor if a specific word is repeated too many times based on a threshold, and then start tracking the conversation as an interesting event in timeline and time lapse recording. As such, the system is able to create static tags but may then learn tags, via the machine learning sub-system and automatically adjust the administrator list of tags in a dynamic manner. In some embodiments, body language, gestures, and integrated machine learning and video analytics can trigger events and mark them in a timeline for either one or both of time lapse recording and/or real-time monitoring.

The IoT Channel column160may provide emails or other information made available by IoT devices in the possession of the customer. In keeping with the refrigerator example provided earlier (e.g. the tag “Fridge” in tag sub-window330), if the refrigerator were an IoT enabled device (or “thing”), then IoT messages (e.g. error messages, etc.) may be routed to the call center agent, i.e. Agent Sally110A. Such IoT messages would then also appear in the interface100(FIG. 1), in the IoT message window360.

In that real time monitoring of multiple agents is typically difficult for one supervisor, the interface100also enables monitoring call duration, which is easier to monitor in real time. If a session with a customer is going on for what may be deemed a long time (e.g. longer than a given threshold, which may vary for different products), the supervisor may engage in more active monitoring or intervention.

Reference is now made toFIG. 4, which is a simplified block diagram400of the flow of data through the system ofFIG. 1. As noted above, there are several types of feeds which may be input into the system ofFIG. 1. Referring, for example, toFIG. 1, Video120, Voice130, Co-Browsing140, Chat150, and IoT Channels160have already been discussed as “channels”. It is appreciated, however, that “Channels” of communication are also feeds of data. The data flowing through the system ofFIG. 1is now discussed.

Some feeds410, such as Video120, Voice130, Co-Browsing140, Chat150, may be input directly for processing by a rule engine420. As is known in the art, a rule engine, such as rule engine420, is a software system which executes one or more rules in a runtime production environment. Rules engines, such as rule engine420, may be acquired, by way of example, as a ready-made product, or alternatively a custom made rules engine may be implemented in some embodiments.

Some feeds, however, may need preprocessing prior to being input into the rule engine420. An indirect feed430, by way of example, but not limited to an IoT feed, may require preprocessing by a processor440prior to being input to the rule engine420. The processor440may be an IFTTT (“If This Then That”) gateway, which is operating like a processor. As persons of skill in the art will appreciate, IFTTT is a web-based service that allows users to create chains of conditional statements which may be triggered based changes to some other web based service. So, by way of a non-limiting example, if customer contacts a call center about a refrigerator problem, and the call center agent runs diagnostics via the refrigerator's IoT interface, the results of the diagnostics may then be forwarded by the IFTTT gateway to the customer service agent and/or to the rule engine420.

The rule engine420may operate as a general purpose engine which may also operate like a SQL (standard query language) engine. However, instead of acting on database data, the rule engine420may act upon incoming data from the feed410and processor440. The rule engine420would output events which are relevant for display on the interface100(FIG. 1), such as IoT data, customer call information, and so forth. The rule engine420also dynamically evaluates incoming metadata in order to perform filtering of events which are associated with the metadata. For example, if there are certain keywords to be detected, then metadata for events which have strings matching those keywords may invoke the rules of the rule engine420. Rules may also be adjusted dynamically based on pattern recognition and machine learning (as will be discussed below).

Once incoming events (i.e., events in the direct feed410and in the non-direct feed430) are filtered by the rule engine420, then the flow of events are further processed by a work flow engine450. The work flow engine450is, as is known in the art, an application which manages and monitors the state of activities in a workflow. Work flow engines, such as work flow engine450, may be acquired, by way of example, as a ready-made product, or alternatively a custom made work flow engine may be implemented in some embodiments.

The work flow engine450processes its input events into different streams, utilizing triggers, conditions (such as: “choose between this and that”) and then outputs an outcome and/or an action. An outcome from the work flow engine450would either effect the action by an automated bot460based on pre-defined patterns determined by a pattern recognition system470, or it would pass it on to a router engine480to be handled by the supervisor.

The pattern recognition system470, enabling machine learning475, may be situated in the system ofFIG. 1to receive an output from the work flow engine450. The pattern recognition system470, as is known in the art, executes machine learning that focuses on the recognition of patterns and regularities in data output by the work flow engine450. The machine learning475result generated by the pattern recognition system470is input to the rule engine420in order to refine the rules used by the rule engine420, so that data from the feeds410,430may be processed by the rule engine420more efficiently going forward. Machine learning may be used to analyze the traditionally ignored rich data stream of interactions. Such events and triggers can then be used in order to have automated bots take an action, or to route the interaction to supervisors managing the team or, alternatively, managing certain types of events (e.g. there might be a manager who specializes in handling interactions with irate customers).

As stated above, the goal of the system ofFIG. 1for supervisors is to filter out noise (i.e., normal conversations) and pay attention to problems that are detected and assisted by the system to handle multiple channels (i.e., Video120, Voice130, Co-Browsing140, and so forth), and multiple agents (i.e.,110A-110F) at the same time, all while the supervisor is managing the conversations and the agents from a single work station. Accordingly, the system will route a typical, “successful” session between a customer and an agent to the bot460. As noted above, the bot460effects the actions resulting from outcomes of the workflow engine. A session in which a problem develops will, as explained below, be routed by router480to a supervisor490.

Conversations which are not judged by the work flow engine450as being “normal” or “successful” conversations, but rather are deemed to require a supervisor's attention, will be routed by the system ofFIG. 1, by the router480to a supervisor490. It is appreciated that the router480will determine which supervisor490is the appropriate supervisor to forward such a conversation to. By way of example, the metadata of a given feed410,430may include the name (or other identifying information) of its associated support agent. The router480may, for example, use a lookup table, and route conversations to the supervisor associated with the support agent associated with the conversation being forwarded.

The time lapse view supported by the interface100is the view of the targeted events across all channels that are filtered through the work flow engine450and allows the supervisor490to zoom into relevant areas without full review of an entire recording.

Reference is now made toFIG. 5, which is a simplified flowchart of one method for implementing the system ofFIG. 1. At step510, a graphics engine (not shown) and driver (not shown) displays on a first portion of a display for one contact center agent among a plurality of contact center agents, a near-real time (i.e. subject to a time delay introduced by network and processing of data) transcript of the contact center agent's conversation with one customer over a plurality of channels. At step520, the graphics engine displays, on a second portion of the display, a co-browsing session of the contact center agent and the customer. At step530, the graphics engine displays, on a third portion of the display, IoT events for an IoT thing associated with the customer, wherein the omnichannel supervision interface displays a plurality of channels for the plurality of contact center agents.

It is appreciated that the graphics engine is typically disposed in a computer at the omnichannel customer support center and enables displaying the interface100, as described herein above.

It is appreciated that software components of the present disclosure may, if desired, be implemented in ROM (read only memory) form. The software components may, generally, be implemented in hardware, if desired, using conventional techniques. It is further appreciated that the software components may be instantiated, for example: as a computer program product or on a tangible medium. In some cases, it may be possible to instantiate the software components as a signal interpretable by an appropriate computer, although such an instantiation may be excluded in certain embodiments of the present disclosure.

It will be appreciated by persons skilled in the art that the present disclosure is not limited by what has been particularly shown and described hereinabove. Rather the scope of the disclosure is defined by the appended claims and equivalents thereof: