Contact centers, also referred to as “call centers”, in which agents are assigned to queues based on skills and customer requirements are well known.
The agents 120 may be remote from the contact center 150 and handle communications (also referred to as “interactions” or “calls” herein) with customers 110 on behalf of an enterprise. The agents 120 may utilize devices, such as but not limited to, workstations, desktop computers, laptops, telephones, a mobile smartphone and/or a tablet. Similarly, customers 110 may communicate using a plurality of devices, including but not limited to, a telephone, a mobile smartphone, a tablet, a laptop, a desktop computer, or other. For example, telephone communication may traverse networks such as a public switched telephone networks (PSTN), Voice over Internet Protocol (VoIP) telephony (via the Internet), a Wide Area Network (WAN) or a Large Area Network (LAN). The network types are provided by way of example and are not intended to limit types of networks used for communications.
The agents 120 may be assigned to one or more queues representing call categories and/or agent skill levels. The agents 120 assigned to a queue may handle communications that are placed in the queue by the contact routing system 153. For example, there may be queues associated with a language (e.g., English or Chinese), topic (e.g., technical support or billing), or a particular country of origin. When a communication is received by the contact routing system 153, the communication may be placed in a relevant queue, and one of the agents 120 associated with the relevant queue may handle the communication.
Agents may be assigned to one or more entities using the cloud-based contact center. Therefore, it is possible that agents, on any given day or shift, are providing support/service for customers of various entities. For example, an agent may handle a communication from a customer of a computer supplier and then immediately thereafter handle a communication from a customer of an automobile company. Accordingly, agents might not be trained in all aspects of customer service for each entity. The term “customer”, as used herein, refers to the party contacting the call center for support or other information and includes actual customers, potential customers, or any other party contacting the call center. Further, agents may be employees of the call center provider, employees of the entity using the call center service, contractors, or freelancers. Therefore, in order to provide a high level of service, it is important to be able to take various actions in real time, such as provide the agent with information or notify a supervisor, based on the content and sentiment of a communication from a customer. Known call center systems are not able to reliably detect the need for taking actions in an automated manner in real-time.
The disclosed implementations can leverage combined data streams relating to communications, historical data, Natural Language Processing (NLP), and future looking Machine Learning (ML) models to predict possible events and/or automatically take actions in a call center. A first aspect of the invention is a method for executing actions related to communications received in a call center, the method comprising: aggregating data from multiple data sources into a combined data stream, at least a first data source of the multiple data sources being a source of data corresponding to at least one communication processed by the call center and at least a second data source of the multiple data sources being a source of external data, wherein the external data is data representing activity that is external to the call center; processing the combined data stream into successive batches of data corresponding to one or more communications between a call center agent and a communicating party received by the call center; applying a sensor data structure defining at least one rule to the batches of data, wherein the at least one rule includes a machine learning model and a configuration data structure based on historical data from the multiple data sources; determining that at least one of the batches satisfies the at least one rule and generating a notification message relating to the one or more communications in response to the determining; and the call center executing an action based on the notification message, wherein the action addresses a situation corresponding the at least one communication.
The foregoing summary, as well as the following detailed description of the invention, will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the invention, there are shown in the appended drawings various illustrative embodiments. It should be understood, however, that the invention is not limited to the precise arrangements and instrumentalities shown. In the drawings:
Sensor configuration data 202 includes conditions under which an event is to be generated, such as key words, sentiment, frequency of key words, and the like. Sensor configuration data can also include an account identification or other identification data of the customer, entity, and/or call center. Sensor configuration data can also include a sensor name, applicable time ranges, and a time stamp. An example schema for sensor data is set forth below.
SensorConfigurations(
),
)
Utterance data 204 is collected and derived from the content of communications between customers and agents. For example, a transcription of the communication (a phone call for example) can be created by recording the communication and using known speech to text processing. The text can then be parsed to identify keywords and/or keyphrases (referred to collectively herein as “keywords”). The utterance data can also include sentiment data of a communication and other identifying metadata, as described further below. An example schema for sensor data is set forth below.
Utterance(
)
Sensor configuration data 204 is delivered into pipeline 200 as a stream and can be stored in a Delta Table, such as a Databricks delta table created in Delta Lake. A delta table maintains an entry for each “sensor” describing the state of the sensor, including whether it is active or inactive. Pipeline 200 continuously receives utterance information, such as transcriptions of communications, as data streams, as they are generated, by a speech-to-text service for example. Transcriptions can be processed in Spark Structured Streaming micro batches. In each batch, the sensor configuration table retrieves the configurations for active sensors. Active configurations are then joined with the transcription stream at 206 in
Pipeline 200 matches a transcription and a configuration, at 208, when the transcription data contains at least one word specified as a key word in the sensor configuration. Additional filters can also be defined in the sensor. For example the filters can include:
This matching process yields a stream of transcription-configuration matches or hits at 210. These hits can be published to a Kafka topic, enabling a hit count of the sensor. The pipeline also keeps, for each active configuration, a count of the hits that fall within a user-defined time window, at 212, to trigger a notification event when hits fall within a user-defined maximum frequency. This count can be kept using a Spark state store functionality on top of a RocksDB, using the flatMapGroups. Pipeline 200 can publish a notification event to Kafka when the hit count for a sensor is greater or equal to the value set in the sensor's configuration within the intended time window (time_range), when the maximum frequency (frequency) is not violated, enabling a notification to the user or any other event/signal to be generated in order to take a desired action.
The pipeline outputs, hit events and notification events can be published to Kafka in json format with the following data structures:
SensorHits
)
SensorNotification
)
The disclosed implementations can leverage Spark Structured Streaming, the Apache Spark API that allows expression of computation on streaming data in the same way and in batch computation on static data. The data is treated as never ending tables on which queries or other processing can be performed. Queries and other processes can be performed on the tables continuously on new data as it arrives. Stateful transformations are also possible. This allows an SQL Engine to operate on data streams with a high throughput, high fault-tolerance, and high scalability. Each new record in a data stream can be stored as a new row in the corresponding table.
Predictive models can be used to predict a trigger condition based on past data. For example, if a customer was browsing kidney disease on a website for re predetermined time or number of visits over a period of time, has an overdue kidney prescription, and just called with intent matched “insurance bill”, it can be predicted that there will be a sudden increase in medical payments for the customer (the “supervised event”). Once sensor conditions are triggered, then an action can be taken at 506. For example, the action can include a notification to an appropriate person or a call to a specified API. As an example, the API call could cause the customer to be adder to a “call immediately” list.
It will be appreciated by those skilled in the art that changes could be made to the embodiments described above without departing from the broad inventive concept thereof. It is understood, therefore, that this invention is not limited to the particular implementations disclosed, but it is intended to cover modifications within the spirit and scope of the present invention as defined by the appended claims.
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