Tutorials for Admins Overview

Tutorials for Admins is a collection of articles that explain how to accomplish specific goals using Bright Pattern's Contact Center Administrator application. Step-by-step tutorials cover various topics related to configuration, integrations, setting up services, building scenarios, AI and bots, and much more.

Audience

Readers of this guide are expected to be familiar with and have access to Contact Center Administrator, the application that is used for configuring contact center operations and generating reports.

Questions?

Tutorials for Admins is a growing collection of "how-to" articles. If you need help with a topic that is not already covered in documentation, please contact Customer Success Management, connect with us via the Contact Us page, or post comments to documentation articles.

Guide Sections

The following is a list of sections in this guide.

• AI and Bots
• APIs
• Chat
• Configuration
• Email
• Forms
• Integrations
• Lists
• Number Provisioning
• Phone Devices
• Remote Assist
• Scenarios
• Services
• Voice

How to Create an Amazon Lex Bot

Bright Pattern Contact Center integrates with Amazon Lex, a platform for building, testing, and deploying chatbots from the AWS Management Console. Lex provides both automatic speech recognition (ASR) and natural language understanding (NLU) technologies, enabling chatbots to recognize customers’ speech and text input, understand intent, and transcribe speech input. Integration with Lex lets your contact center access Lex through chat scenarios and provide bot assistance from within chat interactions.

In this article, you will learn how to create a basic Amazon Lex bot that can be used as a conversational bot with your configured chat service.

Please note that Bright Pattern only supports version 1 Amazon Lex bots at this time.

Procedure

This procedure will walk you through the process of setting up your first Amazon Lex bot. For a deeper understanding of Amazon Lex and other AWS resources, refer to AWS’s Amazon Lex Developer Guide.

Step 1: Create an AWS account

1. If you haven’t already done so, set up an AWS account.

Step 2: Create an IAM user, grant administrative permissions, and save credentials

Our integration accounts require access keys to connect to and use your Amazon Lex bot. Access keys are created and managed in AWS Identity and Access Management (IAM) services.

To get an access key, you need to:

1. Go to your AWS Management Console > IAM Dashboard. If you don't know where it is, search AWS Services for "IAM."
   
   
2. From the IAM root menu, click Users and then click Add user to create an IAM user and grant administrative permissions. Adding a user creates credentials that are used to access AWS.
   
   
   

   Add IAM User
   
   
   
3. Copy the access key ID (e.g., AKIAIOSFODNN7EXAMPLE) and the secret access key (e.g., wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY). Save this for when you set up an AWS Lex bot/chat suggestions integration account.
   
   

For more information, see Managing Access Keys for IAM Users.

Step 3: Add Lex as a service and create a sample bot

1. Sign in to the AWS Management Console and open the Amazon Lex console.
   
   
2. On the Create your bot page, you can create a custom bot or create a ready-to-use one with a sample template. For the sake of this example procedure, select the Book Trip sample.
   
   
3. The Create Your Bot page will open. That is where you develop your Amazon Lex bot.

Step 4: Review the bot workspace

The bot workspace will open. Notice that there are four tabs at the top: Editor, Settings, Channels, and Monitoring. In this exercise, we will be focusing on the Editor tab only. You can come back to the other tabs later.

The Editor tab includes the properties for every intent.

Step 5: Edit intent properties

Intents are actions triggered by keywords entered by your customer. You can think of intents as what customers want to do. For example, the first intent included in our sample Lex bot is "BookCar," which is what the customer wants to do (book a car) and what the bot recognizes it needs to do based on the customer's text input.

Properties

• Sample utterances - Phrases (i.e., keywords) that trigger the intent (e.g., "Reserve a car" or "make a car reservation" or "book a car")
  
  
• Lambda initialization and validation - AWS Lambda Function that validates the customer's input
  
  
• Slots - Data the customer must provide in order to fulfill the intent action (e.g., slot "PickUpCity" collects customer data about the city in which the car will be picked up)
  
  
• Confirmation prompt - Questions that confirm the intent actions and prompt the customer to input data (e.g., "Okay, I have you down for a car to be picked up in {PickUpCity}. Should I continue booking the car?")
  
  
• Fulfillment - AWS's business logic required to fulfill the customer's intent
  
  
  • AWS Lamda Function - The function used as a code hook for your Amazon Lex bot; this can perform initialization and validation, fulfillment, or both
    
    
  • Return parameters to client - Sends the intent information to the client application (i.e., Bright Pattern scenario) for intent fulfillment
    
    
• Response - Message(s) or response cards that close the intent or invoke a different one

Step 6: Save and build

1. For this example procedure, leave everything on the Editor tab as-is.
   
   
2. If you did change something, be sure to click Save at the bottom.
   
   
3. Click Build at the top of the page. This builds the bot with the configured intents.

Step 7: Test it

Once the build is complete, you can test the bot in the chat window.

1. On the right side of the screen, click Test chatbot to pop out the chat window.
   
   
2. Try typing a request such as, “I want to book a trip.” See what happens.

Step 8: Publish your bot

1. At the top of the page, click Publish.
   
   
2. In the Publish dialog that opens, choose or create a new alias (e.g., “TripBooker”) for this bot. The alias is used to point to the specific version of the bot. Having multiple aliases for the same bot allows you to keep and access different intents and properties for the same bot.
   
   
3. Click Publish again.

Next Steps

You have now set up a very basic Amazon Lex bot that can be integrated with Bright Pattern Contact Center. You may now:

• Read AWS documentation, explore Amazon Lex intents and slot types, and edit as desired
• Create an AWS Lex bot/chat suggestions integration account
• Edit a chat scenario that uses your bot
• Configure web chat to work with bots

How to Create a Watson Assistant

Bright Pattern Contact Center integrates with providers like IBM Watson to enable chatbots to be used in your contact center services.

In this article, you will learn how to create a basic IBM Watson Assistant that can be used as a conversational bot with your configured chat service. Note that the instructions provided in this article apply to either IBM Watson Assistant or IBM Watson Assistant (Conversation).

Procedure

This procedure will walk you through the process of setting up your first Watson Assistant. For a deeper understanding of Watson Assistant and other IBM resources, refer to IBM’s Getting Started tutorial and IBM’s API Reference.

Step 1: Create an IBM account

1. If you haven’t already done so, create an IBM account. This process creates and activates an IBMid.
   
   
2. Sign up for IBM Cloud. IBM Cloud is where you will be developing and managing resources like Watson Assistant bots.

Step 2: Add Watson Assistant as a resource

1. Search IBM's catalog of resources for Watson Assistant.
   
   
2. Edit service name, region, and select a resource group.
   
   
3. Click Create Assistant to add Watson Assistant.
   
   
4. The Assistants page will open, showing the skills available for your account plan type.
   
   

Step 3: Add a dialog skill

Skills are the workspaces where you will be developing your bot (note that IBM skills used to be called workspaces). Skills are what provide Natural Language Understanding (i.e., sentiment analysis) for your Watson Assistant. Because you are making a conversational bot, you will be building a dialog skill for talking to customers during live chat.

1. Click Add dialog skill.
   
   
2. For the sake of this example procedure, select Use sample skill, and click Customer Care Sample Skill, which is already set up for you to use and edit. Click it again and the Assistant page will open. That is where you develop your skill.
   
   
   

   Use sample skill
   
   
   

Step 4: Create and/or add intents for your dialog skill

Because you selected the sample skill, the Assistant page will show some preconfigured intents. Intents are actions triggered by keywords entered by your customer.

In Watson syntax, intents always begin with the hashtag ("#") symbol, followed by word(s) in title case (i.e., where the first letter of a word is capitalized, unless it's a preposition like "to" or an article like "the"). Multiple words are separated by underscores ("_").

Like this: #Talk_to_Someone

Get familiar with intents by clicking on the first one in the list. In this example, we clicked on #Cancel to review its properties. You can leave all the preconfigured intents as-is or add new user examples.

Intent Properties

For every intent, you need to specify:

• Intent name - Descriptive name consisting of "#" and text (e.g., "#Cancel")
  
  
• Description - What this intent does (e.g., "Cancel current request")
  
  
• User examples - Keywords and phrases that trigger the intent (e.g., anything a customer might type during the chat); add as many examples and variations of spelling/phrases as you can, as the user examples are what make the bot compelling and helpful

Step 5: Create entities

Click on the Entities tab at the top of the Assistant page. As with intents, you will see some preconfigured entities. Entities are like word sets that more narrowly define the customer text that the bot recognizes.

In Watson syntax, entities always begin with the "@" symbol, followed by word(s) in lowercase. Multiple words are separated by underscores ("_").

Like this: @zip_code

Get familiar with entities by clicking on the first one in the list. In this example, we clicked on @holiday to review its properties. For this example, leave all the preconfigured entities as-is.

Entity Properties

For every entity, you need to specify:

• Entity name - Descriptive name consisting of the "@" symbol and text (e.g., "@holiday")
  
  
• Value name - A particular word that's included under the umbrella of the entity (e.g., "Hanukkah" and "Christmas" could be values for entity "@holiday")
  
  
• Synonyms - Related words that could be used in place of the value name (e.g., "turkey day" could be a synonym for "Thanksgiving"); if you don't know any synonyms, IBM can recommend some for you

Step 6: Design your dialog flow

Click on the Dialog tab at the top of the Skills page. The dialog for the sample skill you selected in Step 3 will be shown.

A dialog is like a scenario in that it defines what the bot does in response to a customer's text or actions. When you design your dialog flow, you are telling the Watson Assistant what to do when it recognizes defined intents and entities during an active chat. Branches of a dialog are called nodes, and nodes can be organized into folders.

Dialog Properties

In this example, we are going to leave everything in the dialog as-is. Click on the "Hours of Operation" node to open up properties for that node:

• Name - The name of the node (you can edit it here)
  
  
• If assistant recognizes - This is where you specify intents related to the node name (e.g., for this "Hours of Operation" node, you are telling the bot what to do when it recognizes the user examples defined in the "#Customer_Care_Store_Hours" intent)
  
  
• Then respond with - This is where you specify entities and how the bot should respond when it recognizes them
  
  
  • If assistant recognizes - Enter entities here (with "@" symbol)
    
    
  • Respond with - This is the bot's response to the customer; in Bright Pattern's Agent Desktop, anything entered in this field becomes the bot suggestion
    
    
• Wait for user input - Select this to provide a bot response only when the customer types something

Step 7: Try the Dialog

1. Click the Try it button at the top of the page. This button launches the Watson Assistant you just built within a chat window.
   
   
2. Pretend to be a customer and type some text into the text input field to see how the bot responds. The Try it out tool will show the intents and entities that the bot recognizes from the text you entered. You can click on the intents shown to select other ones or mark them as irrelevant.
   
   
   

   Sample dialog

Please read IBM's documentation to get a deeper understanding of Watson Assistant skills, intents, entities, and dialogs. Refer to IBM’s Getting Started tutorial and IBM’s API Reference.

Step 7: Add the Watson Assistant

Now that you're done creating a dialog skill, it's time to assign the skill to your Watson Assistant. This last step is important because later, you may have multiple Watson Assistants and skills, and IBM needs to know which ones are assigned to each other.

1. Click Add Assistant.
   
   
2. Select the Use sample skill tab, which is the skill you just set up in this procedure. If you do not see it listed, select the Add existing skill tab and find it there..
   
   
3. Click on the desired skill name. That's it.

Next Steps

You have now set up a very basic Watson Assistant that can be integrated with Bright Pattern Contact Center. You may now:

• Read IBM's documentation, explore the Watson Assistant, and edit it as desired
• Create a Watson bot/chat suggestions integration account
• Edit a chat scenario that uses your Watson Assistant
• Configure web chat to work with bots

How to Configure a Microsoft Azure Web App Bot from a Template

A Web App Bot is a web service that uses a conversational interface and communicates with the Microsoft Azure Bot Framework Service to send and receive messages and events. You can utilize a Web App Bot in Bright Pattern Contact Center to provide self-service options to customers, bot suggestions to agents, and more in chat interactions.

This application note will guide you through the process of creating a template-based Web App Bot in Azure, setting up a Bot Framework Direct Line channel, and configuring a Bright Pattern Contact Center scenario to send and receive chat messages.

Prerequisites

Before you begin, you must have the following:

• Microsoft account
• Active subscription to Azure (free trial OK)
• Admin access to the Bright Pattern Contact Center Administrator application

Procedure

Step 1: Create a Web App Bot in Azure from a Sample Template

1. In the Microsoft Azure Portal, in the resources and services blade menu, click + Create a resource.
   
   
   

   Click "+ Create a resource" to get started
   
   
   
2. In the Azure Marketplace > AI + Machine Learning section, select Web App Bot.
   
   
   

   Create a "Web App Bot" bot service
   
   
   
3. In Web App Bot properties, fill in the required fields:
   
   
   1. Bot handle - The name of your bot, which can be changed later if needed. The name should be unique across Azure; you won’t be able to continue if the name is already taken.
      
      
   2. Subscription - The subscription you want to use to pay for the service; you may also use some of your available credits.
      
      
   3. Resource group - The resource group for the bot to easily manage all resources created in Azure.
      
      
   4. Location - Choose the nearest location for your customers; this option cannot be changed later.
      
      
   5. App name - The name of your app, which also should be unique across Azure because a subdomain will be created to access the Web App Bot.
      
      
   6. Bot template - Choose Echo Bot.
      
      
      

      Web App Bot properties

Step 2: Configure Direct Line channel

The Bot Framework Direct Line channel lets you integrate your bot into your mobile app, webpage, or other application. In this step, we will set up a Direct Line channel and grab the secret key, which is needed for integration with Bright Pattern.

1. Go to Home > Resource groups, and select the group that you created or select for the bot.
   
   
   

   Select your group
   
   
   
2. Now, within the bot group:
   
   
   1. Select the app service you created.
      
      
   2. Go to Bot management > Channels, and click Configure Direct Line channel.
      
      
      

      Click "Configure Direct Line channel"
      
      
      
   3. Copy a Secret key. The Bot Framework generates secret keys that your client application can use to authenticate the Direct Line API requests that it issues to communicate with your bot.
      
      
      

      Copy a secret key

This completes Web App Bot configuration.

---

Next step: Edit Bright Pattern Contact Center scenario

Now that you have completed your bot configuration, you can use the bot in a Bright Pattern Contact Center chat scenario. See How to Configure a Chat Scenario That Uses a Microsoft Azure Web App Bot for detailed instructions.

How to Add a Bot/Chat Suggestions Engine Integration Account

Bot/chat suggestions engine integration accounts allow you to use third-party bots to automate chat conversations, provide self-service options, intelligently route customers to agents, and provide meaningful suggestions to an agent during active chat sessions.

Bright Pattern Contact Center supports the following types of bot integration: AWS Lex and Watson Assistant.

Adding a New Account

1. In the Contact Center Administrator application, go to Configuration > Call Center Configuration > Integrations Accounts.
   
   
2. Click the Add account (+) button to add a new integration account.
   
   
   

   Add new integration account
   
   
   
3. Select account type Bot / Chat suggestions engine and click OK.
   
   
   

   Select "Bot / Chat suggestions engine"
   
   
   
4. In the Bot / Chat suggestions engine type dialog, select your desired type of bot instance.
   
   
   
   
   
   
5. Click OK. The properties for that engine will open next.

Next Steps

Now that you have a bot/chat suggestions engine integration account, you can edit its properties.

Learn more at:

• Set up a Watson Assistant Integration Account

Set up an AWS Lex Integration Account

Integrations with AWS Lex are enabled through integration accounts, which store the credentials of third-party services so that Bright Pattern Contact Center can access and work with them.

In this article, you will learn how to set up your AWS Lex bot/chat suggestions engine integration account and edit its properties.

Please note that Bright Pattern only supports version 1 Amazon Lex bots at this time.

Procedure

Step 1: Add integration account

Add a bot/chat suggestions engine integration account, and select type AWS Lex.

Step 2: Edit properties

In the Properties dialog, enter the credentials of your AWS Lex bot instance as follows. This allows Bright Pattern to access your bot and use it in chat interactions.

Name

The unique name of this integration account (any). Because you can have multiple integration accounts of the same type, it is helpful to have a descriptive, memorable name.

Type

By default, the type is “AWS Lex” because you selected this type when adding the account.

User ID

Your AWS Account ID.

Find it in AWS by going to My Account > Account Settings.

Bot name

The bot name (e.g., “TripBooker”); note this may be different than the bot alias (see below).

Find it in AWS by going to Amazon Lex > Bots (select the name of your bot) > Settings.

Bot alias

The alias name (if any).

It's possible to save multiple versions of your bot, each with different intents and configurations. A bot alias is the name of the version of the bot.

Access key

The access key ID (e.g., AKIAIOSFODNN7EXAMPLE).

You need to set up an access key for AWS Identity and Access Management (IAM) service to get this. See Managing Access Keys for IAM Users and AWS Management Console.

Secret key

The secret access key (e.g., wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY)

Region

The region for your bot instance (e.g., “us-east-1”)

Max API calls per day

The maximum number of calls that can be done each day. This limit is here to keep your account from being charged for additional calls beyond what is included in a free account.

Step 3: Save properties

Click Apply at the bottom of the screen to save your account properties. Your AWS Lex integration configuration is now complete.

Recommended Reading

For more information on bots, see:

• How to Create an Amazon Lex Bot
• Bot/Chat Suggestions Engine
• How to Add a Bot/Chat Suggestions Engine
• How to Integrate Bots with Chat

Set up a Watson Assistant (Conversation) Integration Account

Integrations with IBM Watson Assistant (formerly Conversation) are enabled through integration accounts, which store the credentials of third-party services so that Bright Pattern Contact Center can access and work with them.

Bright Pattern integrates with both IBM Watson Assistant (Conversation) (legacy version) and IBM Watson Assistant (latest version).

In this article, you will learn how to set up your Watson Assistant (Conversation) integration account and edit its properties.

Procedure

Step 1: Add integration account

Add a bot/chat suggestions engine integration account, and select type Watson Assistant (Conversation).

Step 2: Edit properties

In the Properties dialog, enter the credentials of your IBM Watson Assistant (Conversation) bot instance as follows. This allows Bright Pattern to access your bot and use it in chat interactions.

Name

The unique name of this integration account (any).

Type

By default, the type is “Watson Assistant (Conversation)” because you selected this type when adding the account.

Url

The endpoint of your Watson Assistant Skill in the following format: <Legacy v1 Workspace URL>?version=2017-05-26

For example:

https://gateway.watsonplatform.net/assistant/api/v1/workspaces/12a3ab45-b12b-1234-12a3-12d34aebe56c/message?version=2017-05-26

1. To find the Legacy v1 Workspace URL, go to IBM Watson Assistant (Conversation) > Skills, click on the desired skill’s Actions button, and select View API details.
   
   
   

   IBM Watson Assistant > Skills
   
   
   
2. On the page that opens, copy your Legacy v1 Workspace URL.
   
   
   

   Copy your Legacy v1 Workspace URL

Workspace ID

The identifying number (string) of your Watson Assistant (Conversation) Skill (i.e., instance). Note that in IBM Watson, skills used to be called workspaces.

1. To find this ID, go to IBM Watson Assistant > Skills, click on the desired skill’s Actions button, and select View API details.
   
   
   

   IBM Watson Assistant > Skills
   
   
   
2. On the page that opens, copy your workspace ID.
   
   
   

   Copy your workspace ID

Username

The username (string) that is used to authenticate the Watson Conversation API. The username is provided in the service credentials for the service instance that you want to use.

Password

The password (string) used to authenticate the Watson Conversation API. The password is provided in the service credentials for the service instance that you want to use.

Max API calls per day

The maximum number of calls that can be done each day. This limit is here to keep your account from being charged for additional calls beyond the first 1,000 that are included in a free account.

Maximum suggestions

The maximum number of suggestions (e.g., 3) that can be delivered to the Agent Desktop during active chat interactions. Suggestions are the bot-generated replies that agents can select and use during chats.

Test Connection

Click to be sure Bright Pattern Contact Center can connect to your Watson Assistant (Conversation).

If the credentials are invalid, go back to the Url property and make sure you’ve entered it exactly as explained. Test until you see the success dialog: "Account credentials appear to be valid."

Step 3: Save properties

Click Apply at the bottom of the screen to save your account properties. Your Watson Assistant (Conversation) integration configuration is now complete.

Recommended Reading

For more information on Watson Assistant, see:

• How to Create a Watson Assistant

• How to Integrate Bots with Chat

How to Integrate Bots with Chat

Bright Pattern Contact Center integrates with providers like IBM Watson to enable chatbots to be used in your contact center services. In this article, you will learn how to integrate an IBM Watson Assistant (i.e., a conversational bot) with your configured chat service.

In How to Configure Web Chat, you learned how to set up a chat scenario, chat service, and chat scenario entry point to work together to display a chat widget on your website. A working chat widget connects a customer on a website to a live agent in your contact center.

When a bot is integrated with your chat services, customers can be connected to the bot, which can answer questions, provide assisted self-service, and connect to a live agent if needed. Even if the chat is routed to an agent, the integrated bot still runs in the background, ready to assist the agent by providing suggestions (i.e., suggested responses for the agent to select and use).

Prerequisites

This article assumes that you have:

• Read and understand How to Configure Web Chat
• Access to your company’s IBM Cloud account
• Some understanding of how Watson Assistant works

Procedure

This procedure consists of the following concepts:

• Create a bot in IBM Watson
• Add an IBM Watson integration account
• Create or choose a chat scenario that incorporates bot interactions
• Create or choose a chat service for your bot
• Link the chat scenario and service to your integration account
• Test the chat

Step 1: Create a Watson Assistant in IBM Cloud

1. If you haven’t already done so, sign up for an IBM account and launch IBM Cloud. IBM Cloud is the application where you will be developing and managing resources like Watson Assistant chatbots.
   
   
2. Follow all the steps in IBM’s Getting Started tutorial in order to create a Watson Assistant instance. The Watson Assistant will be your bot, and you will be adding intents, entities, and a dialog to it in a workspace. For more information on Watson Assistant, see IBM’s API Reference.
   
   
3. You can also read our basic guidelines in How to Create a Watson Assistant.

Step 2: Get Your Watson Credentials

Your credentials are necessary for integrating Bright Pattern Contact Center with your Watson Assistant. You will be using these credentials in later steps to add an integration account.

1. In your IBM Cloud console, get your Watson Assistant credentials from either of these two places:
   
   1. Watson Services > Assistant > Manage (for credentials in plain text)
      
   2. Watson Services > Assistant > Service Credentials (for JSON snippet)
      
      
2. Copy the Url, Username, and Password for your Watson Assistant to a separate doc, such as a text file.

Step 3: Add an Integration Account

Integration accounts are what allow your contact center to operate with third-party services.

1. In the Contact Center Administrator application, go to Call Center Configuration > Integrations Accounts.
   
   
2. Click the Add account (+) button to add a new integration account.
   
   
   

   Add new integration account
   
   
   
3. Select account type Bot / Chat suggestions engine and click OK.
   
   
   

   Select "Bot / Chat suggestions engine"
   
   
   
4. In the Bot / Chat suggestions engine type dialog, select your Watson Assistant type and click OK.
   
   
   
   
   
   

Properties

Fill in all Watson Assistant properties or Watson Assistant (Legacy) properties.

Click Apply at the bottom of the screen to save your account properties.

Step 4: Create or Select a Chat Scenario

Note that this step is the same as Web Chat Configuration Step 1.

1. Go to Configuration > Scenarios > Chat.
   
   
   

   Configuration > Scenarios > Chat
   
   
   
2. Either select an existing Chat Scenario from the list, or click the Add from template button at the bottom of the screen to create a new chat scenario from the “Mobile Chat” template.
   
   
   

   Select the "Mobile Chat" template
   
   
   
3. Creating a new chat scenario from a template will open the Scenario Builder application in a new browser tab or window. For the purpose of this simple setup, leave the scenario as-is and click Save.
   
   
   

   Name the scenario
   
   
   
4. Give the scenario a unique name (e.g., "Bot Scenario") and click Save. Your new scenario will appear in the list of scenarios.

Step 5: Edit the Scenario to Work with Your Bot

To use the Watson Assistant in live web chat interactions, you’ll have to edit the basic Mobile Chat scenario template to allow the bot to interact with the customer. For more information about scenarios, see the Scenario Builder Reference Guide.

There are many ways to construct such a bot-enabled scenario; the instructions given in this step are provided as just one example of how to use your Watson Assistant in chat.

In this scenario-building exercise, you will be configuring the system to use the bot to:

• Automatically accept the customer’s chat request
• Greet the customer
• Provide answers to the customer
• Provide suggested replies to the agent
• Connect to an agent when it’s determined that the bot is not being helpful

1. Download File:App Example Bot Scenario.zip and import it to your Contact Center Administrator application.
   
   
2. Select the imported scenario and click EDIT to open it.

The following scenario blocks (with comments) are included in this example scenario. Be sure to read the comments that explain each scenario block's properties.

1. Chat Bot Select Account: This block tells the system which integrated bot will be used for this scenario.
   
   
   

   Select an integration account
   
   
   
2. Set Variable: This establishes the customer's first name and last name. Setting this context helps to personalize the conversation.
   
   
   

   Use a variable to get the customer's name from the pre-chat form and use it in the chat interaction
   
   
   
3. Send Message+: This block delivers a chat message to the customer, incorporating the customer's name.
   
   
   

   Have the bot send a message to the customer, addressing him/her by name
   
   
   
4. Set Variable: This time, we are defining a step. In this example scenario, we want the bot to respond to the customer two times before connecting to the agent. The first time is specified as "step 0" and the second time is specified as "step 1." The scenario will run through both steps. If the customer's issue is not resolved by step 1, the customer is transferred to the agent for help.
   
   
   

   Use a variable to define this part as the first in a series of steps
   
   
   
5. If: The If block specifies what happens if the customer did not write a message in the pre-chat form's Message field. If that field is empty, the scenario tells the bot to send a message. The message aligns with what you have configured your Watson Assistant to say. We call the bot's messages suggestions.
   
   
   1. Add a branch
      
      
   2. Give it a condition of item.message is empty
      
      
      

      If the customer's message is empty, have the bot send a message to the customer
      
      
      
6. Ask a Bot: The scenario uses this block to get suggestions (i.e., some response from the Watson Assistant) to deliver to the customer. For each of the conditional exits (Failed, Timeout, or No Data), the scenario will send a message and then connect to the agent.
   
   
   

   Get suggestions from the bot
   
   
   
7. Send Message+: This is used to send a message to the customer via either chat or SMS. In this example, the message is sent in the active chat interaction. If a specific message is not defined here, the standard system-wide message is sent to the customer.
   
   
   

   Send another message if you want
   
   
   
8. Set Variable: This time, we are defining "step 1." Remember, we want the bot to respond to the customer two times before connecting to the agent. The first time is specified as "step 0" and the second time is specified as "step 1." The scenario will run through both steps. If the customer's issue is not resolved by step 1, the customer is transferred to the agent for help.
   
   
   

   Define this part as step 1
   
   
   
9. If: In this If block, a condition is set to make sure that if the scenario takes more than two steps to resolve the customer's issue, then it will transfer the chat from the bot to the agent.
   
   
   1. Add a branch
      
      
   2. Give it a condition of “steps” > “2”
      
      
      

      If the scenario takes more than two steps to resolve the customer's issue, then the agent steps in
      
      
      
10. Find Agent: The Find Agent block looks for the next available skilled agent to accept the chat. You can use Find Agent to set wait times and send the customer messages about estimated waiting time (EWT).
    
    
    

    The bot couldn't resolve the customer in two steps, so the scenario looks for an agent
    
    
    
11. Connect Chat: Connect Chat is for setting the destination of the chat. If you leave the destination fields empty, the Find Agent block will find the first available agent.
    
    
    

    You can either specify a destination for the chat or let the system find the next available agent
    
    
    
12. Exit: The Exit block completes the scenario. Without it, the scenario will loop through this configured flow until the customer ends the chat.

Step 6: Create or Select a Chat Service

See Web Chat Configuration Step 2.

Step 7: Create or Choose a Chat Scenario Entry

Note that this step is basically the same as Web Chat Configuration Step 3, with the addition of another important property.

1. Go to Configuration > Scenario Entries > Messaging/Chat and select a scenario entry point to use with your chat scenario. The entry point is what starts the scenario.
   
   
   

   Messaging/Chat scenario entries list
   
   
   
2. If there are no existing chat scenario entries shown, you have to create one.

How to Add a New Chat Scenario Entry

1. Click the Add chat scenario entry button.
   
   
2. In the Properties tab, fill in the properties as appropriate (see below for required and optional properties). For definitions, see the Contact Center Administrator Guide, section Messaging/Chat.

Required Properties

In the Properties tab, make sure to address the following fields right now. These are critical to chat configuration.

• Name - The unique name (any) of the chat scenario entry (e.g., "Redwood Chat")

• Unique identifier - A long string of characters that identifies the chat entry. It is recommended that you change it to something short and unique (e.g., "1234").

• Scenario - Choose the Chat Scenario you selected or created earlier in this procedure (e.g., "Super Chat"). This is where the parts come together to make chat possible.

• Service - Choose the Chat Service you selected or created earlier in this procedure (e.g., "Pacific Chat"). This is where the parts come together to make chat possible.

• Bot / Chat suggestions engine - The engine that will provide suggested replies to the agent during chat. Note that in order to use a bot/chat suggestions engine, you must have an integration account configured for your contact center.

Other Properties

The other remaining properties are optional at this time. You can come back to them later.

Step 8: Edit Chat Widget Style

See Web Chat Configuration Step 4

Step 9: Add a Contact Tab

In order for this chat scenario entry to work, you must add a contact tab for the chat widget. See How to Add a Contact Tab.

Step 10: Get the HTML

See Web Chat Configuration Step 6

Final Step: Test the Chat

You have now successfully set up your contact center service, scenario, and chat widget to work in an integrated manner with your Watson Assistant. You should test it to make sure that the:

• Customer's name entered on the pre-chat form comes through during the chat
• Bot is able to automate the conversation with the customer
• Customer is connected to an agent if the bot couldn't help within two tries
• Agent is able to see the bot's suggestions during the active chat

The following screenshot shows a service chat (on the Agent Desktop side) where the bot provides two responses to the customer and then connects to an agent. You can see the bot's suggestion below the agent's text input field.

And the following is how the chat looks to the customer: