Guidelines for Conversational Design & chatbot projects

That’s a funny question to start with, but yes you should ask yourself if you even need to build a chatbot. Chatbots are not a perfect solution to everything. If the task is very complexe, requires your users to fill out a form or your target audience doesn’t use a chat, then perhaps you should consider something else.

Your is essentially your grand vision. It will give a direction to your project and allow you to make decisions that are consistent when it comes to building your chatbot. It can be as simple as creating a new digital experience for a new generation of customers or improving operational efficiency for a department.

Your audience is the people you build your chatbot for. If you are building for a majority of men or women, for people of a certain age or a specific social background, there are some moving parts you’ll want to adapt.

Make a list of everything your chatbot could do that would create value for the end user. It’s important to have a use case, something you want the chatbot to be able to do. It will be easier for you to get started if this is precise and small. Don’t try to solve all your problems at once. You’ll have plenty of time to add new skills and most importantly improve existing skills.

This is the part where you use the information gathered from identifying your audience. You don’t always have to craft a personality, but you would be leaving aside a great opportunity to differentiate your organization. Your chatbot’s personality could crack jokes randomly, could be very formal or very casual.

This one forces you to think mid-term for your project. Perhaps you are starting only with English, but eventually will you need other languages? Spanish and French are widely supported by various platforms, but languages like Arabic can be more difficult to find. Don’t get locked in a platform that can’t grow with you.

If you are building a simple Q&A, it might be not be necessary, but if you want to expand the capabilities of your chatbot to self-service and automation of processes. You might want to connect a database to store and retrieve information about customers or perhaps you want to connect it with a Shopify store to fetch order status and allow order cancellation. It’s important to evaluate what you will need from your chatbot platform in order to make these integrations. Are you proficient with the programming languages proposed by the platform? These are all questions to think of.

Now the fun part. Platform? Framework? Damn it, I’ll build it from scratch. I think in our modern world building it from scratch would be a mistake. There are just too many tools out there that exist to help you. You basically need to choose between a framework or a platform. A framework will make your life easier with a lot of functionalities already built-in, but you will need to code. A platform on the other end will require less coding or even no coding at all. Depending on your needs and your skills, you should narrow down already. There are hundreds of frameworks and platforms out there, you just have to pick the one for you.

The last, but not the least. Key Performance Indicators will help you understand how your chatbot behaves with your end users. The tracking of these KPIs will help you improve and most importantly show the value created by the chatbot and your team.

First thing first, you need to know that you should use questions only when you can provide a straighforward answer. Otherwise, you might want to use an intent. Intents also have the advantage of allowing information extraction.

This is very important to avoid creating confusion in your NLP model. You can avoid duplication easily with a simple spreadsheet where you check for duplicate. The overlap is a bit more difficult because it's subject to interpretation by the person doing the dataset.

200 seems like a big number, but for some organization FAQs add on pretty fast. Limiting yourself to 200 will allow you to better manager the previous guidelines and will provide you with speed of delivery.

Aim for consistency and low ambiguity. You can create a naming convention custom to your need, but here's a proposition

• QnA: qna_[topic]_[attribute] ex: qna_library_location
• Intent: intent_[verb]_[noun] ex: intent_book_flight
• Provide information / slot filling: provide_[context]_[slots] ex: provide_ein_credentials
• Affirm something, like a choice or a fact: affirm_[group] ex: affirm_yes_no

This will make your life easier when searching as your chatbot and your dataset gets bigger and more complicated.

The really cool thing with AI chatbot is that you can extract information from the users input. This will allow you to save that information for later or use it to complete an action.

• Entities are information that is extracted either because it's a built-in entities or because you've created entities to extract from a list or regular expression. Those are useful for short list or pattern you can express in the form of a regEx.
• Slots are tagged in your intent at the creation. It's best to use slots when you have a dynamic list or a very long list of items. By tagging your slots in your intents, you will allow your NLP model to train itself to extract them.

How much is enough right? Best case scenario would start with at least 10 utterances (examples / variations) per QnA or intent. This should give the NLP engine enough data to build a good model. Your first try won't be perfect so focus on improvement after your first draft.

I know we said that already, but this one is different. Earlier we said, no duplicate for questions and intents. Now, it's about the utterances. Make sure you don't have the same utterances in two different questions or more. This will confuse the model.

Using synonyms will make your model stronger and more resilient. Because not everyone uses the same words to describe actions or things, using synonyms will help cover a broader ranger of conversations.

Similar to the aforementioned guideline, move the subject and the verbs around in the questions or sentences. Try using different interogative words.

Example: “Reset password” and “I want to reset my password” and “Can you please help me to reset my password for my emails”

Some people think they will save a massive amount of time by leveraging NLG to make their dataset, but in fact they are shooting themselves in the foot. Your dataset will make or break the performance of your chatbot so it shouldn't be taken with a grain of salt. NLG will not create the quality of data you need.

You just don't need to bother yourself with that. Most engine will take that into account by themselves when the training is done.

At this point, it's almost like we are repeating ourselves, but make sure to use the different abbreviation and lingo of your domain. Example: “I have a problem with a po” and “Purchase order issue”

You want to prepare your chatbot to handle the different situation because it would be silly to believe that the end-user will always provide all the information you need from the get-go. Make sure your have intent with fully filled slots, partially filled slots and empty.

If you don't do this, you will end up with an AI model who will recognize the position of the words in a sentence and will extract based on this variable. By swithcing around the slots, you make sure the chatbot will get an accurate understanding of what to extract.

Simply put, 3 slots is 15 utterances minimum plus the 10 utterances per intent for a grand total of 25 utterances minimum. I suggest going even higher than that, the upper end would be something around 50.

The more slots you add to your intent, the more difficult it gets to train and maintain accuracy.

Avoid making covnersational detour. Always provide the users with the shortest route to their destination. How can you help them get there in fewer steps?

Chatbots can be accessible on a wide variety of channel, but they might support different components and they might have a context that needs to be taken into account. For example, a chatbot embedded on your pricing page versus a chatbot on Facebook will get very different messages.

Building a chatbot with a personality that is consistent with your brand can make a big difference on the experience users will have. If you usually have a friendly approach, building a chatbot with a very professional language can feel weird.

Some of the channels have components you can leverage for better experience. Rich Elements such as carousel and cards can be useful to display certain type of information. Use them when appropriate to make it more interesting for the end users.

Your greeting message is super important. This is the first impression and it sets expectations. It must contains the following elements: Say hi; Mention that it is a chatbot and not a person; State the purpose. By doing the latter, you make sure the end users undersand the capabilities of the chatbot. If they can speak with a live agent as well, do mention it!

If you want to collect the email or the phone number of your user before they start, it's fine! However, it shouldn't block from using it. These hurdles are not only bad for the experience, but they also reduce adoption for a solution you spent time building.

It's a chat, so don't write email format. In fact, if it's longer than a tweet (240 characters) it shouldn't be a message. Perhaps they should be sent to a page or in most cases it can be simpler.

There should never be a blank cursor moment (where the user has to think what to do next), always try to guide the user to the next step. The chatbot can ask if it can help with something else for example.

There are three possible outcomes to the chatbot saying something: User answers correctly, user asks for more info or user cancels. Knowing that, you should preparer for each case.

You can't possibly think of everything, but at least prepare a few. As you learn more, you will be able to add things that you didn't think of when you started.

It would be very annoying if Microsoft Word didn't ask to save your changes before you quit the app. Well kind of the same thing here. If the action the user is about to take have ireversible consequences, you should ask for confirmation. Are you sure you want to quit without saving?

Sometimes it fails. You need to make an API call, the request times out. Let the user know something happened and that you will need to try again now or later.

Create a feedback loop in your chatbot to measure if the conversation is helpful for the user. You can do this by asking the question simply or some platform have a built-in system for that.

You probably know your chatbot won't be doing the job 100% of the time. How will you handle those situation to avoid frustration among the users? You can use a live chat system to take over the chat session when the chatbot isn't capable of helping anymore. You could also use the chatbot to send an email and notify the users that someone will get back to them.

For example, the chatbot can use information from the user’s interaction with a website. If the user has visited the pricing page, you might want to ask if they have questions about pricing.

Some platform will have a built-in system for this. If that's not the case, you should find a way to leverage this to imrpove your chatbot. You can use the conversation data to understand what topics your chatbot is not covering or if there are existing topics that it doesn't understand very well.