Genesys Chatbots (CE31) for PureCloud

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This topic is part of the manual PureCloud Use Cases for version Public of Genesys Use Cases.
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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.

Story and Business Context

The proliferation of digital channels leads to more demanding customer expectations and an increased number of interactions that companies deal with when servicing customers. Coupled with increased usage of 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 if 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. Benefits typically include:

Use Case Benefits

Use Case Benefits Explanation
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
Improved Net Promoter Score Reduce the time required to address the customer request, handle off-hour contacts, offer immediate options, and improve outcomes.
Reduced Volume of Interactions Increase self-service interactions to reduce agent-assisted interactions for repetitive or common requests

Summary

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, however, focuses on deploying a bot on web chat, mobile chat, Facebook Messenger, Twitter Direct Message, Line Messaging, WhatsApp, or SMS. 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
  • Simple bot orchestration enables customers to use the best bot for the job e.g. Google Dialogflow has highest alphanumeric recognition rates
  • PureCloud Architect makes it very easy to integrate to new bot providers, switch between bot providers or to use multiple bot providers within a single interaction
  • A-B testing with PureCloud Architect helps determine which bot is most effective for a particular business use case
  • Graceful escalation to a live Agent at the right time


Use Case Definition

Business Flow

When a customer interacts through a supported Genesys digital channel, a chatbot starts. The chatbot first attempts to use context to anticipate why the customer may be engaging and in turn provides personalized messages 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 finishes, 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 intent is not established or understood, the chatbot passes the customer to an advisor.

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 present a suitable message.

The chatbot continues in this fashion, creating a conversational loop and building 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 External Contacts
    • API call to third-party data source
  4. The customer receives a personalized message or is handed over to an agent. Examples include:
    • Custom message or update: "Your next order is due to arrive on Thursday before 12."
    • 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 interaction is sent to a chatbot (for example Amazon Lex) which asks an open-ended question like: “How may I help you?” to determine intent and capture the customer's response.[BL1]
    • If intent and slots are returned, the conversation moves to the correct point in the interaction flow, for example;
      • Automated notification task (such as display balance)
      • Handoff to live agent
    • If intent and slots are not returned, the conversation returns to the interaction flow and the customer is handed off to an agent.
  6. Upon completion of a task,the interaction is sent to a chatbot (for example Amazon Lex) which asks a follow-up question like: "Is there anything else I can help you with?"
    • If the customer responds “yes,” they return to Step 5: "How may I help you?”
    • If the customer responds “no,” then the conversation returns to the interaction flow
    • If the customer responds with a more advanced answer, then determine intent and entities for further processing.
  7. Customer information and/or context is retrieved to determine whether to offer a survey.[BL2]
    • If a survey is offered, the interactions is sent to a chatbot.
    • If no survey is offered, the interaction flow shows a goodbye message and ends
  8. The survey is executed. The survey questions are configurable by the customer on a business-as-usual basis in the chatbot and therefore no dialog flow is defined here.
  9. The interaction flow presents a goodbye message and ends the chat

Related Documentation



For more details

For additional details, contact your Genesys Sales Representative by filing out the form or for immediate assistance call us: 1-888-Genesys.


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