Genesys Chatbots (CE31) for PureConnect

This topic is part of the manual PureConnect 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.

Other offerings:

Use Case Overview

Story and Business Context

The proliferation of digital channels has led to more demanding customer expectations and a drastic increase in the number of interactions that companies have to deal with when servicing their customers. Coupled with increased usage of AI for business applications, this has resulted 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 automated chats. 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*

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 Containment Rate	Increase self-service interactions to reduce agent-assisted interactions for repetitive or common requests
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

^*You can sort all use cases according to their stated benefits here: Sort by benefits

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 Messengerand/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
Identification & Verification (ID&V) – to identify and verify the customer if required
Directed Dialog – to automate relevant business processes or provide information
Involve another NLU/AI platform including (e.g. Amazon Lex, Microsoft bot framework, IBM Watson or Google Dialogflow) – if it specializes in a particular topic
Hand-off 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 on what to do next. For example, if the customer replied 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, the chatbot presents a retry or max retries message.

Once the task is completed, the chatbot asks if the customer still needs 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 if it is outside of business hours, the chatbot can offer a callback option or present a suitable offer.

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

A chat interaction is initiated (reactive or proactive) across a supported channel.
The customer receives a standard welcome message from the chatbot.
Customer information and/or context is retrieved from:
- Genesys User Data (e.g. Altocloud Segment or from the website passed by Genesys Widgets)
- Journey context available from Altocloud or customer journey data
- API call to third-party data source
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."
- Self-service task, such as loan application, is executed based on Segment provided by Altocloud or other attached user data.
- Customer is handed over directly to an agent because they have an outstanding balance (or is able to request a callback).
- If the customer is not handed over to an agent, the customer could end their chat, confirm the contact reason, or continue.
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)
The customer's response is then sent to 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 and Verification.
  - Automated business process (such as payment collection microapp).
  - Hand-off to live agent or request a callback (see the relevant use case for the channel).
- 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."
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 the next step).
- If the customer responds with a more advanced answer, the response is sent to a third-party NLU engine via API to determine intent and entities for further processing.
Customer information and/or context is retrieved to determine whether to offer a survey. (BL5)
- Logic defined in Intelligent Automation
- If a survey is to be offered, the chatbot continues to the next step.
- If no survey is to be offered, the chatbot continues to step 11 and shows a goodbye message.
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.
The chatbot offers the customer a survey. 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 Intelligent Automation Questionnaire Builder microapp.
- The chatbot presents a concluding message and ends the chat.

Business and Distribution Logic

Business Logic

BL1: Agent Hand-off: 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, hand off to an agent, or offer a callback if busy or outside of business hours.

BL3: Response Type: The interaction flows can be configured to accept natural language responses as well as closed responses, such as account number, date of birth, and yes/no questions. This means that customers can backtrack to a different point in the dialogue 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 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. This can be based on:

Genesys User Data
Journey context from Altocloud or customer journey data
API call to third-party data source
Internal logic

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:

User Interface & Reporting 

Agent UI

Are handled as part of channel-specific use cases:

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

Reporting

Real-time Reporting

Current Chat interactions waiting in the system
Total Chat interactions
Agent Group Status

Historical Reporting

Historical reports cover:

How many conversations took place over a period of time
Length of time for each conversation: maximum/minimum/average
How many unique customers/contacts and how many repeat customers/contacts

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

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