Genesys Chatbots (CE31) for Genesys Engage on premises

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This topic is part of the manual Genesys Engage On-premises Use Cases for version Current of Genesys Use Cases.
Use chatbots to automate customer conversations and seamlessly hand over to a chat agent when needed.

What's the challenge?

Many customer service, sales or support conversations with customers are repetitive — frustrating both to customers and to employees. If you could insert better automation, many conversations may well be taken care of in the entry process, saving time while also increasing customer satisfaction.

What's the solution?

Blended AI chatbots automate natural language conversations, even across channels. Genesys blended chatbots look up customer information and activity to answer questions. They can hand over conversations with context to an agent when needed, or even offer a callback1 during or after hours.

1Callback option is available for Genesys Engage only.

Use Case Overview

Story and Business Context

The proliferation of digital channels leads to higher customer expectations and an increased number of interactions that companies deal with when servicing customers. Coupled with increased usage of Artificial Intelligence (AI) for business applications, this change results in organizations implementing chatbots that can interact with customers to automate tasks and assist their queries on channels such as web, mobile, social, SMS, and messaging apps. Chatbots can alleviate strain on contact center employees while improving the customer experience and controlling costs. Chatbots are always on and available, and can be handed over to an agent at any time where needed. While chatbots can also be used by employees and for business optimization purposes, the remainder of this document refers to omnichannel bots in the context of customer engagement. The primary benefits of chatbots are to increase self-service success, deflect interactions from the contact center, and improve the customer experience.

Genesys supports a “design once, deploy anywhere” concept for bots to enable organizations to provide a seamless customer experience across voice and digital channels. This use case focuses on deploying a bot on web chat, mobile chat, Facebook Messenger, Twitter Direct Message, Line Messaging, WhatsApp, or SMS.

Use Case Benefits*

Use Case Benefits Explanation
Improved Customer Experience Reduce the time required to address the customer request, handle off-hour contacts, offer immediate options, and improve outcomes.
Improved First Contact Resolution Present a customer experience that is tailored to the individual based on who they are, why they might be interacting, and the status of the contact center
Reduced Volume of Interactions Increase self-service interactions to reduce agent-assisted interactions for repetitive or common requests
*You can sort all use cases according to their stated benefits here: Sort by benefits

Summary

Genesys Chatbots supports "bring your own technology model" supporting Amazon Lex, Google Dialogflow, our native Genesys Dialog Engine, and third-party bots. As each chatbot and third party has their own specific capabilities, this use case covers broadly available capabilities. The chatbot supports or orchestrates the following capabilities:

  • Personalization – to tailor the experience based on context from the current interaction or from previous interactions
  • Natural Language Understanding – to derive intents and entities
  • Identification & Verification (ID&V) – to identify and verify the customer if required
  • Directed Dialog – to automate relevant business processes or provide information
  • Involve supported third-party NLU/bot platforms, if it specializes in a particular topic
  • Handoff to an agent – to connect the customer to a live person with the full context of the interaction
  • Offer and schedule a callback - if outside of business hours or long wait time then chatbot offers an immediate or scheduled callback
  • Offer a chatbot survey depending on business context


Use Case Definition

Business Flow

When a customer interacts through a supported Genesys digital channel, a chatbot is initiated. The chatbot first attempts to use context to anticipate why the customer may be engaging and in turn provides personalized messages or options to resolve the query. If no personalization options exist, the chatbot asks the customer an open question, such as "How may I help?".

Once the customer responds, the chatbot tries to interpret the request to determine intent and then decide what to do next. For example, if the customer replies with “I want to check my balance”, the chatbot would first identify and verify them before showing their balance.

Once the task is completed, the chatbot asks if the customer needs more help. The customer can respond by asking another question, requesting to chat with an advisor, or replying 'no'. If the customer replies with 'no', the chatbot can offer a survey based on context.

If the customer chooses to speak or chat with an agent and there is a long wait time or it is outside of business hours, then the chatbot can offer a callback option or present a suitable message.

The chatbot continues in this fashion, creating a conversational loop and building up context between itself and the customer to better solve their query.

The following diagram shows the business flow of the use case:

Business Flow Description

  1. A chat interaction is initiated (reactive or proactive) across a supported channel.
  2. The customer receives a standard welcome message from the chatbot.
  3. Customer information and/or context is retrieved from:
    • Customer profile information in UCS
    • Genesys User Data (e.g. Altocloud Segment or from the website passed by Genesys Widgets)
    • API call to third-party data source
  4. The customer receives a personalized message/menu or is handed over to an agent. Examples include:
    • Custom message or update: "Your next order is due to be delivered on Thursday before 12."
    • Most likely contact reason: "Do you want to find out about the loan application you have in progress?"
    • Tailored menu with most likely options: "Main menu: you can choose Balance, Payments, or TopUps."
    • Customer is handed over directly to an agent because they owe an outstanding balance
    • If the customer is not handed over to an agent, the customer could end their chat, confirm the contact reason, or continue.
  5. Assuming the customer has moved on from the Personalization stage, the chatbot asks an open-ended question like: “How may I help you?” to determine intent and capture the customer's response. (BL3)
  6. The customer's response is then sent to Dialog Engine (or, optionally, a third-party NLU engine via API). [BL1-BL4]
    • If intent and entities are returned, the conversation moves to the correct point in the interaction flow, which could be within one of the following sub-flows (or microapps):
      • Identification & Verification
      • Automated business process (such as payment collection microapp)
      • Handoff to live agent
    • If intent and entities are not returned, the chatbot returns a retry message like: "Sorry, we didn’t understand your question. Please ask another question or reply AGENT for live assistance."
  7. Upon completion of a task, the chatbot asks a follow-up question like: "Is there anything else I can help you with?" [BL2-BL3]
    • If the customer responds “yes”, they're brought back to Step 5: "How may I help you?”.
    • If the customer responds “no”, the chatbot decides whether or not to offer them a survey (see step 8).
    • If the customer responds with a more advanced answer, the response is sent to Dialog Engine (or, optionally, a third-party NLU engine via API) to determine intent and entities for further processing.
  8. Customer information and/or context is retrieved to determine whether to offer a survey. [BL5]
    • If a survey is to be offered, the chatbot continues to the next step.
    • If no survey is to be offered, the chatbot shows a goodbye message and ends.
  9. The chatbot asks the customer: "Would you like to participate in our survey?"
    • If the customer answers "yes", then they continue to the next step and engage in a survey.
    • If the customer answers "no", then they continue to the final step and are shown a goodbye message.
  10. The survey is executed. The survey questions are configurable by the customer on a business-as-usual basis and therefore no dialog flow is defined here. This dialog uses the Genesys Intelligent Automation Questionnaire Builder microapp.
    • The chatbot presents a goodbye message and ends the chat.

Business and Distribution Logic

Business Logic

BL1: Agent Handoff: The customer can ask to be connected to an available agent. At that point, the chatbot is disconnected and the chat transcript (excluding sensitive data) is displayed in the agent desktop. Other context can also be displayed as Case Data.

BL2: Retries: The number of retries for self-service tasks and questions can be configured by a business user. When reaching maximum retries, the dialog can be configured to present a message, hand off to an agent, or offer a callback if busy or outside business hours.

BL3: Response Type: The interaction flows can be configured to accept natural language responses and closed responses such as account number, date of birth, and yes/no questions; enabling customers to backtrack to a different point in the dialog when required. For example, if a customer is midway through making a payment and says “actually just tell me where your nearest branch is”, then the chatbot shows the nearest branch.

BL4: Callback: If outside of business hours, or estimated wait time (EWT) is high, the chatbot can offer an immediate or scheduled callback. If this option is not included, then a message states that a transfer is not possible.

BL5: Survey: The customer can determine whether to address a survey or not, based on:

  • Customer profile information in UCS
  • Journey context from Altocloud or customer journey data
  • API call to third-party data source


Distribution Logic

When the conversation is handed over to a live agent, the interaction moves to one of these use cases, depending on the channel the customer is using the use cases listed under the interdependency section.


User Interface & Reporting


Agent UI

The agent desktop requirements for the required digital use cases can be referenced by clicking on the respective use case in the interdependencies section.

Chat transcript between customer and chatbot is populated in the chat interaction window in the agent desktop.

Reporting

Real-time Reporting

The following is a summary of real-time metrics, for more details reference the eServices Statistics for additional information.

  • Agent Group capacity for chat interactions to define whether or not to offer escalation to customer service.
  • Concurrent Chats statistic in the Chat Agent Activity template is helpful in assessing Agent Group capacity for chat interactions to define whether or not to offer escalation to customer service.
  • Chat Agent Activity is applicable for Agent and Agent Group object types.
  • Current Chat interactions waiting in the system
  • Current Wait statistic in Chat Queue Activity template addresses "Current Chat interactions waiting in the system".
  • Total Chat interactions (self-service vs assisted service). Below are a list of available templates:
    • Chat Agent Activity> Offered: The total number of chats that were offered for processing to this agent or agent group during the specified period. This stat type counts interactions both offered by business routing strategies and other agents.
    • Chat Agent Activity> Accepted: The total number of chats that were offered for processing and that were accepted by Agent during the specified period.
    • Chat Queue Activity>Requested: Total number of Chats Requested.
    • Chat Queue Activity> Accepted: Total number of Chats Accepted by Agent.
    • Chat Offered/Accepted: This metric is suited to Agents only (assisted service), unless some adapter for bots is used, the one that expose itself as an agent.
    • Chats Requested: Represents all requests for new chats. These chats later may be served by agents or by bots.
  • Agent Group Status: There are out-of-the box templates with the same name with lots of useful statistics available supporting multimedia channels email, voice, chat etc., found in the Genesys Pulse Agent Statistics reference.

Historical Reporting

Intelligent Automation offers a suite of internal reports details below:

Dashboard

  • Application Overview
  • System Pulse
  • Real-time Graphs

Prebuilt Reports

  • Summary
  • Calls per Day
  • Calls by Time of Day
  • Block Results
  • Recognition Summary
  • Business Task Summary

Customer Journeys

  • See what’s important to callers
  • Monitor the impact of changes
  • Compare customer experience
  • Data Extracts (CSV format)
  • Call Details
  • Business Tasks
  • GUI Actions
  • Inbound SMS

For more information regarding Historical Reporting for bots, reference the Bot Dashboard page.

Customer-facing Considerations

Interdependencies

All required, alternate, and optional use cases are listed here, as well as any exceptions.

All of the following required: At least one of the following required: Optional Exceptions
None

    Digital

        Digital

          Self-Service and Automation

            None

            General Assumptions

            • This Use Case is supported by industry templates that contain examples of chatbot applications combining personalization, natural language understanding, AI, and microapps. Chatbot application requirements including required microapps are confirmed during design. These application templates are created for Financial Services, Telco, and Travel.
            • Handoff to agent is on the same channel (unless callback).
            • Dialog Engine has been "trained" with intents.
            • Dialog Engine provides English only language support for NLU capabilities.
            • NLU capabilities for languages not supported by Dialog Engine can be supported through integrations to third-party NLU engines such as Google Dialogflow.
            • The Genesys Control Center to configure Chatbots is localized to support the following languages:
              • English (United Kingdom)
              • French
              • Spanish (Mexican)
              • German
            • Callback Dialogflow is provided by the Smart Transfer microapp.
            • Chat transcript is not passed to callback agent.
            • Survey Dialogflow is provided by Questionnaire Builder microapp. Results available for download from Genesys Intelligent Automation Control Center or via web service. Review Menu Block documentation for further details.
            • To deploying bots when Dialog Engine is the NLU provider, see Integrating Intelligent Automation with Dialog Engine.

            Customer Responsibilities



            Related Documentation

            Document Version

            • V 1.0.8