How to scope your next chatbot project

When sitting down to decide what information your chatbot should know (i.e. scoping your chatbot), you’re actually trying to predict what questions your future users will ask. And by giving your users a keyboard, you’re opening up the list of possible questions to include literally everything and anything.

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When a user asks a question, your bot will use natural language processing (NLP) to try and determine the intent (or purpose) of your user’s question. These intents are the building blocks of your bot’s knowledge.

For your bot to recognize an intent, you need to identify what questions your users will ask, build an NLP model based on that information and train your bot with +40 expressions per intent.

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There’s a big difference between what your bot could know and what it should know. But to get to the should, you need to start with the could. Below we share our favorite places to look for possible intents and our tips on selecting the most impactful ones. Because at the end of the day, your chatbot needs to save you time and money.
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Gather all the possible intents​

The scoping phase of a chatbot project is when you gather all the possible intents. This is the time to dig out a magnifying glass and play Sherlock Holmes. Just like a detective, you need to poke around in the dusty corners, interview witnesses and listen for the truth.

This is the phase where you’ll look into all the possible questions your bot could answer. If you’re feeling impatient and want to know what your bot should answer, skip ahead to the next section.
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Start with the FAQs​

There’s a reason companies have FAQs– they’re the select set of questions that users ask the most. Not only do you already know that these are high-volume questions, but the content is also structured and the answers are there! It's almost a perfect outline for your chatbot's scope. All you need to do is rework the questions to match a single intent and rewrite the copy to fit a conversation.
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Chat with your customer support team​

No one knows your users better than your customer support agents. They interact with and solve your users’ questions and issues daily. Most customer support teams have a knowledge library of questions and answers beyond the basic FAQs. Similar to an FAQ, you can assume that users ask these questions repeatedly, and there’s already a clear outline of how the bot should answer.

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Customer support teams are also your gateway to the “information universe.” They manage the access to the digital archive of all past user conversations, and in most cases, they classify these conversations with topics, tags, and have template responses. This is a friendly warning that reading through real user conversations can lead you down the black hole of information overload, but we swear that this is the best way to understand who your bot will talk to and how they’ll respond to it.

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In case you’re still not convinced, the final reason to involve your customer support agents in scoping your chatbot is that they’ll be your bot’s direct colleagues. When your bot can’t close a user request on its own, your customer support agents will take over the conversation via an offload flow. You need to know how they work to design a bot that will help them, and they need to understand how the bot will work to help users better.
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Review company documentation​

Is there an onboarding manual? Or a handbook of company processes? Since this is information that your company wants to share with employees, partners, customers and more, it’s a great thing to read for inspiration. You’ll be pleasantly surprised how many intents you can find in a basic About-CompanyX.pdf.
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Identify the manual processes​

Chatbots exist to automate repetitive tasks. Basic manual processes inspire some of the best intents and future conversational flows.

A common manual process is data capture. You can easily put the bot in charge of collecting and updating user data, so that your human colleagues can focus on more complex tasks. Managing appointments, routing to the right queue and sending reminders are other processes that a chatbot can easily take over.
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Turn user questions into chatbot intents​

Questions and intents are similar, but not the same. A question is literally what your user asks, while an intent is the purpose of what your user asks. For example, a user asks Hey Google! What’s the number for Amy’s Pizza? The Hey Google… is the question, but 01-restaurant-contact-info is the intent.

Transforming a question into an intent isn’t always easy. The key is to capture the essence in one to three words. A good rule of thumb is that if your intent doesn’t fit on a post-it, it’s too long.
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Choose the best intents​

Now that you have a collection of possible intents, it’s time to decide which intents will have the largest potential impact and therefore, the highest priority. For a pilot project, we recommend starting with ~20 intents. It’s always better to get a small set of intents working well before making a dumpster of half-understood intents.
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Look for the trifecta​

The best intents are the ones that are repetitive, easy to recognize and easy to answer. When you find this trifecta, put this intent at the top of your implementation backlog.

But how do you know if you have a trifecta? We use a systematic framework to visualize which intents will have the largest potential impact.

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Recognizability: Can a bot easily recognize this intent? Is it clear and unique, or very vague, or similar to other intents?

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Repetitiveness: How frequent is this intent? Will it be triggered 200 times a day or twice a day?  

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Easiness: How simple or complex is it to implement this intent? Do we need to use APIs, create a microservice, etc.?

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Review each intent and give it a score from 1-10 for recognizable and repetitive (1 being the least and 10 being the most). Then give the intent a score from 1-5 based on how easy it’ll be to implement (1 being the easiest and 5 being the most difficult).

Now it’s time to map each intent on a graph; recognizability is on the y-axis and repetitiveness is on the x-axis. Once you have the coordinates for an intent, mark it with its easiness score.

A cluster in the top right quadrant will appear. These are the intents that you should prioritize for your chatbot. Start with the easiest intents to implement, and then work your way up through the more difficult ones.

Any intents that don’t make the list for the pilot should go on your backlog so that you can pick them up to include in future releases.
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A chatbot’s knowledge isn’t static​

Launching your bot is only the beginning. This is one of our favorite diagrams of all time because it’s so simple, yet so accurate 👇

The best source of new intents for your chatbot comes from its users. Once your bot is live, take time to review the incoming conversations. This allows you to make sure that the bot’s recognizing existing intents correctly and to see what other questions users ask that are currently not in scope.

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Look for patterns and figure out how the bot could answer that question instead of putting it in a not understood flow. Your priority should be to make every experience a neutral or positive experience for the user. Looking at the missed questions gives you all the necessary information to increase your bot’s knowledge. The best bots grow continuously and are updated regularly to answer actual user questions.