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Category: Chat

  • Use Chatbots for Employee eLearning

    I recently had to use the eLearning system at a Fortune 100 corporation. All consultants, including me, were required to complete a module on Privacy. I was struck by 3 things:

    – How dated the eLearning system felt
    – How difficult the interface was to understand
    – How disruptive the training was to my day

    If you use an eLearning system at your organization, there’s a good chance you feel similarly.

    I believe chatbots have the potential to transform eLearning at any organization. Watch this demo of the Tangowork Chatbot Accelerator configured for micro-learning.

    There are 3 reasons why chatbots are so powerful for eLearning:

    1. The interface is intuitive. Everyone understands how to use the messaging-style conversational interface.

    2. The information is bite-sized. The nature of the chatbot medium forces brevity.

    3. Chatbots can nudge. A short, proactive broadcast can be sent to employees’ messaging apps, nudging them towards the desired behavior.

    The eLearning systems in most organizations are getting long in the tooth. eLearning by chatbot — or “microlearning” — is an exciting development for anyone tasked with delivering a training or change management initiative to a large group of employees.

    Tangowork Chatbot Consulting

    Tangowork’s consultants use research, design and technology to help your organization create better digital experiences. We specialize in intranets, AI assistants and high-performance websites.

    The post Use Chatbots for Employee eLearning appeared first on Tangowork: Consultants for Intranets, AI Assistants, Fast Websites.

  • How to Collect User Data with a Chatbot

    A Smartloop Conversation Platform Step-by-Step Tutorial

    Chatbots are not only great at chatting with humans, but they can also help you collect user data, such as the user’s name and email. This data can be very useful for profiling your users, for re-targeting, and for creating tailored conversation flows for specific types of users.

    In this tutorial you will learn how to:

    • Set up your first Smartloop chatbot (and account)
    • Collect the User’s Name
    • Get and Validate the User’s Email

    Notes:

    • This tutorial is level 101, i.e. it is for everyone. It doesn’t require previous programming knowledge
    • All screenshots and flows explained in this article are done with the Smartloop chatbot platform
    • Everything in this article is valid for all channels that Smartloop supports for publishing chatbots on: website, Facebook Messenger, Viber, etc.

    Set Up Your Smartloop Chatbot (and Account)

    (if you already have a Smartloop account, please move on to the next section)

    1. Head to the Smartloop website
    2. Click SIGN UP at the top of the screen

    Smartloop chatbot website

    3. Follow the instructions to setup your account. This step takes less than a minute.
    4. Log in your new Smartloop account (feel free to go through the onboarding tutorial).
    5. Once you are in the Smartloop dashboard, click on “+” and enter the required info. In my case, I’ve entered “User Data Collection Bot” as the title and description, my channel is Facebook, and the bot language is English:

    Smartloop chatbot platform

    Feel free to go through the new onboarding tutorial.

    Collecting the User’s Name

    1. For this example, let’s open your chatbot and create a new conversation block, which will ask the user what his/her name (in my case, this is the Start block). Add a TEXT card to the block:

    2. We want the user to input his/her name in the chatbot. The way to do this is to add a User Input card to the block:

    3. Since a person’s name is usually plain text, set the Data Type of the User Input card to Text, as in the screenshot above.

    4. Enter a name for this variable. The variable name should be descriptive enough to explain what data is stored under it (think of this as a label). In my case, I will use {{user_name}} — see screenshot above.

    5. It is fairly hard to validate how names are spelled out, so let’s keep Validation to none.

    6. Let’s add a new TEXT card which will thank the user for his/her input. Let’s also make the conversation a bit more personal, by using the variable we’ve just created:

    Congrats! We have just programmed the Smartloop chatbot to collect the name of the user! This also means that the platform will store the name of this user for future use.

    NOTE: When the User Input card is used, the chatbot will expect input from the user. If you type a command in the chatbot when it is expecting an input, the chatbot will interpret it as input, and not as a command.

    Getting and Validating the User’s Email

    1. Let’s create a new conversation block, which will ask the user for his email. I will call this block email:

    2. Since this block is not the ‘start’ block which the bot will start the conversation with, we need to add a keyword which will later allow us to call this block from the chatbot. Go to the Expressions tab and enter email as a keyword:

    3. Go back to the Response tab of the block, and add a TEXT card which to prompt the user to enter his/her email.

    4. Since we want the user to type his/her email, let’s add the User Input card to the block:

    5. Since the email is text-only, set the Data Type of the User Input card to Text.

    6. Let’s enter a name for this variable. I will use {{email}} — see image above.

    7. Smartloop can automatically validate the email addresses for you, so let’s set Validation to email. Once we do this, the platform will give us the option to enter a Message in case the email that is entered is invalid:

    8. Let’s test the flow by typing the email keyword in the chatbot and hitting Enter (you may need to refresh the test console). If you type a wrong email address, the bot will give you the invalid message (see image above).

    9. Let’s add a new TEXT card to the flow which will thank the user for his/her input (refresh the chatbot if you want to test the new flow):

    Well done! Your Smartloop chatbot will now store the user’s email.

    Putting It All Together

    It is easy to connect the two flows described in this article in one seamless chat flow. Go to your initial block (in my case it was the Start block) and add a Go To Block card, which to point to our email collection block:

    The Go To Block card instructs the bot to go to the email block once it completes the name collection process.

    Hit refresh in the test console and go through the flow — you’ll see that don’t need to use any keywords anymore:

    HINT: The Go To Block card allows you to design more complicated flows, based on conditions that are triggered by a user inputs or events. We discuss some of these scenarios in this blog article: Customizing a Conversation Flow Based on User Input

    Questions? Comments? Let me know in the comments below.

    Enjoy using the Smartloop chatbot platform! Please feel to reach out to us if you have any questions.

    How to Collect User Data with a Chatbot was originally published in Smartloop on Medium, where people are continuing the conversation by highlighting and responding to this story.

  • 5 best practices for deploying an employee or HR chatbot

    Starting a chatbot project can seem overwhelming, but following best practices makes the road ahead smoother. Benefit from our team’s experience deploying chatbots with five need-to-know techniques to improve the success of your chatbot implementation.

    1. Start with a narrow domain

    Don’t try to build Siri. Unless you have a massive budget, and a team of hundreds of engineers, you’re not going to be able to keep up with the likes of Microsoft Cortana, Google Assistant or Siri. Even those teams don’t always get it right.

    Even teams with hundreds of engineers don’t get everything right. (Don’t worry, they’ve fixed it.)

    Keeping the domain of your chatbot narrow to start and setting your users’ expectations about what the bot can deliver is a realistic way to achieve success. Ideas for a first phase could be a bot that delivers company news, gives information about a conference, provides contact details, or answers questions on a specific subject matter. Later, the chatbot can be expanded to have a wider domain.

    Keep the domain of your chatbot narrow at the start

    A good example of this is a Tangowork chatbot implemented at international healthcare company Bupa (read the Bupa case study). When Bupa planned to move their headquarters, they created a chatbot to answer questions about the move, like “when are we moving?” or “do I need to pack my own things?”. After the office relocation, they gradually expanded the bot to answer common day-to-day questions, like “where can I print?”, “where do I get a new Skype headset” or “what’s the number for HR?”.

    “Before our move to Angel Court, the chatbot focused mainly on the office move: things like ‘when are we moving?’ and ‘do I need to pack my own things?’ After the move, we expanded to questions about day-to-day work in the new environment.”
    Del Green

    Senior Digital Communications Manager, Bupa

    2. Have one killer feature

    What answer does your chatbot have that your employees can’t live without? When your chatbot has a feature that keeps employees coming back again and again, they’re going to turn to it as a resource for other information as well.

    At Bupa, now that Cyan (the Bupa chatbot) answers questions about day-to-day work, the number one question it gets asked is “What’s the guest wifi password?” Since the password changes frequently, many employees find that the easiest way to get the password for a guest is to ask Cyan.

    3. Design failure carefully

    You need to intentionally design what failure looks like, because a chatbot is not always going to be able to give a user the answer they are looking for. That happens for several reasons:

    • The chatbot might need more examples to delineate related questions
    • The chatbot might need more training on unexpected terms the employee is using
    • The chatbot doesn’t contain any answers related to the employee’s question

    Every response the chatbot gives will fall into one of the four categories on a tool known as the confusion matrix:

    • True positive: the chatbot knows the right answer and delivers it
    • False positive: the chatbot knows the right answer, but delivers an incorrect answer
    • True negative: the chatbot doesn’t know the answer, and says it doesn’t know
    • False negative: the chatbot knows the right answer, but says it doesn’t know

    Each possible type of response that the bot might give needs to be considered

    The Tangowork Chatbot Accelerator reduces false positive answers by using a confidence threshold: it only returns an answer if the chatbot is at least 40% sure that it has the correct answer. For answers where the Tangowork Chatbot Accelerator is 40 to 60% confident, it delivers the answer but then asks the user to confirm whether their question was correctly understood.

    It’s good to consider each of these scenarios for your bot, and analyze what the chatbot will do in each case. Because failures (incorrect or unknown answers) are going to occur, designing for failure will result in the best possible outcome when it happens. Make sure that the chatbot is giving the best answer possible in each scenario, and steer the user back to supported tasks when needed.

    If a user is asking the chatbot for information that it is not designed to provide, redirecting a user back to supported tasks helps the user know what the chatbot can do for them.

    Instead of:

    Sorry, I don’t understand. Ask me something else.

    Try:

    Sorry, I don’t understand. I know things about 
Acme Human Resources policies and benefits. 
Try "benefits", "payroll" or "time off".

    4. Grow your pilot gradually

    At the beginning, a small team of stakeholders and subject-matter experts brainstorms questions and answers that the chatbot will be fielding. Starting the pilot with 10 or 20 people allows the team to review the conversation transcripts and see questions that weren’t anticipated or that the bot is misunderstanding. They can then teach the bot to handle those questions, expand the pilot by another 10 or 20 people, and repeat the process.

    As the pilot grows, the percentage of successful responses climbs higher and higher. If you launch at the very beginning, the number of unsuccessful responses will result in user frustration and failed adoption. Once the success rate is in the 90-95% range, the chatbot is ready to launch to the entire organization.

    As the chatbot pilot gradually grows, incorrect responses decrease

    Growing your pilot gradually allows for transcript review, chatbot training and greater insight into user needs, for a high conversation success rate on launch.

    5. Review transcripts constantly

    Reviewing chatbot conversation transcripts is especially important during the pilot period for your chatbot, but it continues to be an important part of general maintenance. Transcript review allows you to see when the chatbot doesn’t understand a message, or doesn’t have the answer a user is looking for. Fine-tuning the bot by adding more information on a topic, or training it to understand a user’s intention in a particular message allows for continuous improvement.

    Transcript review pinpoints answers the chatbot is missing, which are added for an increased success rate

    Conclusion

    Apply these best practices to find success as you enhance your employees’ digital workplace with an informed, responsive chatbot.

    Summer Chatbot Webinar Series

    In July, August and September, sign up for 3 free webinars that explore 3 types of chatbots: Intranet Chatbots, Event Chatbots, and HR Chatbots.

    Learn more

    Tangowork Chatbot Consulting

    Tangowork’s consultants use research, design and technology to help your organization create better digital experiences. We specialize in intranets, AI assistants and high-performance websites.

    The post 5 best practices for deploying an employee or HR chatbot appeared first on Tangowork: Consultants for Intranets, AI Assistants, Fast Websites.

  • Tangowork Chatbot Accelerator Release Notes

    Tangowork Chatbot Accelerator v22: Excel import/export

    July 16, 2018

    What’s new: chatbot

    • Buttons with links. Now you can add hyperlink buttons to messages. When the user clicks, they’re redirected to a URL.
    • Messages with non-text. Tangowork Chatbot Accelerator now recognizes unsupported messages and responds appropriately. For example, if a user sends a photo, Tangowork Chatbot Accelerator responds with “Thanks for the photo. Unfortunately, I only understand text.” Works for audio, video, images, location, and the Messenger “thumbs up” button. Responses are customizable.

    What’s new: management console

    • Excel import/export. Add and maintain content via Excel. You can export existing content to a spreadsheet, make changes, then import it again.
    • People import/export. For private bots, add and maintain a list of users via Excel or JSON. It’s ideal for scenarios where automated synchronization via API isn’t possible.
    • Phone number internationalization. Phone numbers are automatically converted to the E.164 standard when they’re added to the system. For example, (604) 555-1212 is converted to +16045551212. This ensures that duplicates are recognized and ensures that SMS messages are delivered. A “Country” preference in “Bot settings” allows the Tangowork Chatbot Accelerator to infer country code (e.g. “+1”) when none is provided.
    • Default introduction for broadcasts. Set a default introduction for broadcasts, such as “**ACME EMPLOYEE ALERT**”.
    • We moved the “Chat” button to the top-right corner so it’s easier to find.
    • We changed the behavior when you click on intent names. Clicking now shows and hides the intent’s messages. To edit the intent, click the pencil icon.
    • We changed the behavior when you click on intent metadata like sample questions, sort order and list type. Clicking now opens the entire “Edit intent” dialog and highlights the metadata you clicked on.
    • We’ve changed dialogs so that they can only be closed by clicking a button or by clicking a close icon. Previously, clicking outside the dialog closed it, but that led to inadvertent closures.

    Bug fixes

    • The prompt to “Train language model” was occurring too often. Now it’s only appearing when it really needs to.
    • Buttons in messages couldn’t be edited in the management console. Now they can.
    • Long intent names were getting truncated on narrow screens. Now they’re not.

    Tangowork Chatbot Accelerator v21: Easy entity editing

    June 21, 2018

    What’s new: management console

    • We completely overhauled working with entities (e.g. in “What is John Doe’s phone number?”, the entity is “John Doe”). Now you can add new entities and tag them within sample questions, all within the management console.
    • Sometimes you need to run custom integrations on a schedule, like for running a nightly import. Now you can.
    • If users ask about a date or time, the “DateTime” prebuilt entity needs to be enabled. Now you can do that yourself in the management console, without asking Tangowork Support.
    • We added a tiny little X to the right-hand side of the search box so you can quickly delete your search term. No more backspace backspace backspace.
    • Now you can instantly change confidence thresholds from within the “Bot settings” page. The default minimum confidence is 40%, but some bots can benefit from dropping it lower. The default confidence for sending user verifications, i.e. “I’m only 45% confident in my answer… did I understand you correctly?” is 60%.
    • We changed the default response setting for new content to “Random” and “No list”. It used to be “Newest” and “List: Cards”, but we found this wasn’t appropriate in the majority of cases.

    Bug fixes

    • When you delete content, we used to move sample questions to the “None” category, but that was degrading the accuracy of the AI. Now we just make deleted content vanish, and the AI is much happier.
    • It wasn’t possible to use your iPad to add new content in the management console. We made a few tweaks, and now the iPad compatibility, while not perfect, is much better.
    • We lost the ability to log on to the management console in Safari, but we found it again.

    Tangowork Chatbot Accelerator v20: Custom buttons in messages

    May 9, 2018

    What’s new: chatbot

    • Messages with buttons. Add any button to any message. Buttons can trigger a certain message, a certain intent, or can send quick-reply text.

    What’s new: management console

    • Add custom buttons or triggers to any message. A new “Buttons” option on Add/Edit Message lets you define custom buttons or triggers for any message. In Skype for Business or SMS, buttons don’t display, but the button can down-render to a prompt, such as “Type ‘more’ for more info.”
    • Bot settings screen. The new bot settings screen lets you quickly adjust the appearance of your bot across channels. For example, you can change the color, font and icon for web chat.
    • Manual message ordering. For lists of messages, you can now select manual ordering. Drag and drop messages to sort them how you want.
    • To make room for expanded functionality, the Add/Edit Message screen now uses collapsible cards.

    Bug fixes

    • Whenever we displayed Cards in Slack, we were showing action buttons twice — before and after the card. It looked confusing, so we eliminated the first set of buttons.

     Tangowork Chatbot Accelerator v19: Custom integrations

    April 2, 2018

    What’s new: management console

    • Help. Inline help is now available on every page.
    • Content categories. Create your own categories to organize content.
    • Custom integrations. It is now possible to execute any custom code in response to an intent. For example, a user could type “search for expense form” and the Tangowork Chatbot Accelerator can execute that search on SharePoint.
    • Previous button. Users can now page through lists of messages using “Next” and “Previous” buttons. This only affects intents where lists are enabled. (Buttons are not supported on Skype for Business or SMS.)
    • Content export. Export content. This is designed primarily for exchanging content with other Tangowork Chatbot Accelerator installations.
    • Content import. Import content. This is designed primarily for exchanging content with other Tangowork Chatbot Accelerator installations.
    • Parent & child intents. There is now full support for parent & child (nested) intents. This allows for lists of lists.

    Tangowork Chatbot Accelerator v18: Confidence thresholds

    February 13, 2018

    What’s new: chatbot

    • Fewer false positives. the Tangowork Chatbot Accelerator now employs a confidence threshold that a potential answer must meet before being sent to the user. If the chatbot is less than 40% confident (configurable) that the potential answer is the right one, it sends a ‘not understood’ reply instead.
    • User confirmation for medium-confidence answers. If the Tangowork Chatbot Accelerator is 40 to 60% confident (configurable) that an answer is correct, it sends the answer, but then verifies with the user whether their question was correctly understood. The user is presented with a “Yes” or “No” button on platforms that support it, or a prompt to enter “yes” or “no” on text-only platforms.

    What’s new: management console

    • Verified answers screen. A new “Verified answers” screen lists questions and corresponding answers that end users have confirmed are correct or incorrect. The administrator can agree, disagree or discard the confirmation.
    • Unrecognized questions screen. A new “Unrecognized questions” screen lists user questions that weren’t understood (i.e. that were below the confidence threshold). The administrator can align the question with the correct answer or discard it.
    • Transcripts now list the confidence under each user question. If a user confirms a medium-confidence answer, it’s noted on the transcript. If an administrator updates the “Verified answers” or “Unrecognized questions” screens, the change is marked on the corresponding transcript.
    • The list of transcripts now scrolls indefinitely. Once you scroll near the bottom of the list, the next 100 conversations load.

    Tangowork Chatbot Accelerator v17: Minor changes

    February 1, 2018

    What’s new: chatbot

    • Long questions rejected. Very long questions aren’t usually understood by the language engine, and they’re usually entered in error anyway. If a user asks a very long question (longer than 140 characters), the bot now responds with, “I have trouble understanding long sentences. Please try again using fewer words.” This response is configurable.

    What’s new: management console

    • White space is now automatically trimmed from the start and end of all input on the management console.

    Bug fixes

    • Transcripts were sorting by date created instead of date updated, causing some current conversations to be hidden low on the list. Now transcripts are sorted by date last updated.
    • Transcripts weren’t being recorded for Microsoft Teams or Cortana. Now they are.
    • Some errors returned by Microsoft Cognitive Services weren’t being surfaced in the management console, and it appeared the console was hanging. Now those errors are shown.

    Release history

    • Version 22: Excel import/export
    • Version 21: Easy entity editing
    • Version 20: Custom buttons in messages
    • Version 19: Custom integrations
    • Version 18: Confidence thresholds
    • Version 17: Minor changes
    • Version 16: Annotate transcripts with notes
    • Version 15: Direct language model training
    • Version 14: Chatbot analytics
    • Version 13: Features for when the chatbot doesn’t know
    • Version 12: Chatbots for Sharepoint, easier content management
    • Version 11: Messages with multiple intents
    • Version 10: Skype for Business improvements
    • Version 9: Transcripts, Subscribe, Test Broadcasts
    • Version 8: Better date support, broadcasts for public bots
    • Version 7: New search technology
    • Version 6: Intent management & embedded web chat
    • Version 5: Enhanced management console
    • Version 4: Skype for Business, image management
    • Version 3: Carousels & buttons
    • Version 2: More preferences
    • Version 1: Content management, broadcasts and language processing

    Tangowork Chatbot Consulting

    Tangowork’s consultants use research, design and technology to help your organization create better digital experiences. We specialize in intranets, AI assistants and high-performance websites.

    The post Tangowork Chatbot Accelerator Release Notes appeared first on Tangowork: Consultants for Intranets, AI Assistants, Fast Websites.