Genesys Chatbots (CE31) for Genesys Engage cloud

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This topic is part of the manual Genesys Engage Cloud 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*

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry, lines of business or Genesys product line:

Use Case Benefits Explanation
Improved Customer Experience Reduce the time required to address the customer request, handle off-hour requests, 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, 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 necessary
  • 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 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.

If intent is not established or understood, it presents a retry or max retries message.

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 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 started (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.
  6. The customer's response is then sent to a supported third-party NLU engine. [BL1-BL2]
    • 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 subflows (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?"
    • 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 to offer them a survey or not (see step 8).
    • If the customer responds with a more advanced answer, the response is sent to a supported third-party NLU engine to determine intent and entities for further processing.
  8. Customer information and/or context is retrieved to determine whether to offer a survey. [BL3]
    • If a survey is to be offered, the chatbot continues to the step 9.
    • 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 are shown a goodbye message and the chatbot ends.
  10. The survey is executed using the Designer survey microapp.
    • Optional: If the survey results meet a certain criteria based on the configured evaluation parameters, a specific action can be taken. For example, if the customer provides a negative response, they can be routed to a live agent.

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. Upon maximum retries, the dialog can be configured to present a message or be handed over to an agent.

BL3: Survey: The customer can determine whether to address a survey or not. This survey can be based on:

  • Customer profile information in UCS
  • API call to third-party data source
  • Logic defined in Designer

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

Agent desktop capabilities are part of the channel-specific use case and links found 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

Real-time reporting includes:

  • Agent Group capacity for chat interactions to define whether to offer escalation to customer service
  • Current Chat interactions waiting in the system
  • Total Chat interactions (self-service vs assisted service)
  • Agent Group Status
  • Queue/tenant-related statistics definitions have queue names that are hard-coded

Historical Reporting

Reporting Bot Analytical Dashboard for Designer are historical bot reports for the Designer-based solution. The Designer reports are specifically bot reports and not interaction queue reports.

The dashboard provides detailed reporting on bot activity during interaction flows that involve Genesys Designer applications, and contrasts self-service sessions with and without bot participation, which can help you understand how bots impact the customer experience.

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

          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.
          • Support for quick replies on SMS is not yet available.
          • Rich messaging support only quick replies.
          • Designer user interface is used to configure chatbots and is available in United States English only.
          • Customer responsibility to build the natural language bot model such as utterances, intents, or slots. Professional Service may be engaged to develop the model.
          • Customer selects one or more of the supported NLU platforms.
          • NLU configuration takes place in the native NLU interface (which may be embedded within Designer).
          • Dialog Engine is not available on Genesys Engage. It is only available for Genesys Could.

          Customer Responsibilities



          Document Version

          • Version V 1.0.3 last updated March 9, 2021