Let’s Talk! How to Build out Conversations for a Better User Experience

For conversational products and interfaces, product creators have to imagine an interaction where the screen plays an ancillary role. As a consequence, the majority of the best practices and principles of a good user experience that we’ve become used to need to be redefined, if used at all.

Even within conversational interfaces, we don’t speak in same way that we write. We don’t pronounce all words in the sentence, combine words, don’t say some words at all, and a huge part of our communication is non-verbal.

Hence the MVP for conversational interfaces needs to be different from that for any other product.

In this two-part series, I look at some of the best practices to build conversational products and interfaces.

Build Out Conversations, Not User Stories

In my experience, building user stories for conversational interfaces turns out very clunky, and a better approach is to build out possible conversations between the end user and the product. Focusing on the bookends of the conversations helps you to understand the trigger and the goal.

Improving the user experience provides customers the solution to their problems with as little friction as possible. Improving confidence levels and increasing the scope of utterances is the key way to reduce friction for conversational UI.

From learning all the different ways that someone may ask for their account balance, to increasing the number of tasks that can be performed autonomously, every challenging question that a customer asks is an opportunity for us to learn and improve.

Here are some of the things to keep in mind when building out these conversations:

High-level Flows

Once you have a few sample dialogs, you can abstract the flow and logic of the conversation. This provides the structure of your conversational interface. Good designs balance the need for clearly-defined user paths with the users’ desire for shortcuts directly to what they want.

Start with the most ideal or optimal path that you envisage with users. This is the perfect alignment between customer actions and your designed intent.

Then expand the path as you go along.

Adapt to What Users Would Naturally Say

The beauty of a conversational interface is that it comes naturally to users and they don’t have to learn how to use one. Your product should to adapt to the user’s word choices, instead of forcing the user to memorize a set of commands. It’s easier, and more natural, for users to respond to a narrow-focus question (“Does that work for you?”) than to be taught what to say (“If that works, say yes”).

Provide Cues

A bot should drive the conversation forward and at times even restrict it. There are some easy ways to help the user stay within guardrails. Visually, you can use buttons or quick replies (FB Messenger and Slack support this) to nudge them in the right direction.

Consider suggesting things to do; this will help users discover additional functionality.

“Hey bot, book a table.”

“Table reserved. Would you like me to order an Uber?”.

Bot interactions are a bit like the traditional e-commerce flows. We should constantly keep the user updated and help them to move forward, while avoiding overwhelming them with a wall of information.

Interactions Should be Simple

The number of paths a conversation can take increases the potential for dead ends. It is better to limit the functionality and nudge the user down a particular path. A simple solution is to use structured messages to guide the users. Rather than asking the end user to type “yes” or “no”, show a structured message with two buttons.

Focus on Spoken Conversations

When starting, it is better to focus on just the spoken conversation, without the technical distractions of code notation, complex flow diagrams, screen size etc. Getting the flow right is easier if everything is in one place. As you expand to other devices like mobile phones, pieces will move out of the spoken prompts and into the display prompts, chips, and visuals.

Handling Errors

Conversational interfaces can face the following three types of errors:

  • No input: The product did not record the user’s input.
  • System error: Error in fulfilment of the user prompt.
  • No match: The action couldn’t interpret the user’s response in context.

The “No Match” error is a little trickier and needs to be handled well in the conversations. Here are a few possible causes of “No Match” errors.

Prompt: What time works for you?

User: Sometime late in the evening?

Prompt: Sorry, what time?

(The user says something relevant to the context, but the product doesn’t understand it.)

Prompt: What time works for you?

User: What’s the weather like?

Prompt: Sorry, what time?

(The user wants to switch topics entirely.)

In each context, it is important consider why the user might be having difficulty. Then, in the subsequent prompt, include additional support in the form of options or additional information. For example:

Prompt: What time works for you?

User: Sometime late in the evening?>

Prompt: Sorry, we have two time slots. One in the afternoon between 1 pm and 2 pm and the other in evening between 4 pm and 5 pm. Now, what works for you.

If there is a No Match even after the second attempt, end the conversation to avoid further user frustration and offer a substitute or alternative method for follow up. For example:

Prompt: Sorry, I’m still having trouble, so you may want to visit our website instead.

Good error handling is context-specific. Even though you’re asking for the same information, the conversational context is different on the second or third attempt. In order to play the right error prompt for the context, you’ll need to keep track of how many, and what type of, errors have occurred.

Randomize Prompts When Appropriate

Craft a variety of responses just like a person would. This makes the conversation feel more natural and keeps the experience from getting stale. For example, randomize your first prompt with Hi, Hello, Hey, Welcome etc.

Right mix of Conversational Components

Conversational components are all the things that make up a prompt, like acknowledgements or questions. They also include chips, which are used to continue or pivot the conversation. Prompts and chips are the core of the conversational interaction and should be designed for every turn in the dialog. For example, Google provides a list of the different conversational components that can be used in a prompt for the Google Assistant .

Conversational component Example
Acknowledgements Okay.
Apologies Sorry, I can’t send eCards yet.
Commands Create a bouquet of yellow daisies and white tulips
Confirmations Got it. The men’s running shoes in royal blue and neon green. In what size?
Discourse markers By the way, …
Earcons <welcome chime when Google Home powers on>
Endings Anything else I can help you with right now?
Errors Sorry, for how many?
Greetings Welcome
Informational statements 42 is an abundant number because the sum of its proper divisors, 54, is greater than itself.
Questions What kind of flowers would you like in your bouquet?
Suggestions I can tell you more about I/O. For example, you might like to know about the keynotes, codelabs, or app reviews. I can also help you find sessions, or office hours. So, what do you want to know?
Chips Add to cart

Be Concise and Relevant

The conversational interface is also linear, and unlike GUIs, there’s no way to skim over the information. By forcing users to uninformative and irrelevant verbiage, you are unnecessarily wasting their time and testing their patience. People do not appreciate taking extra time or jumping through hoops to find things out or to get things done. Successful conversational interface design therefore should be brief and concise.

Graceful Exits

Make sure that the user can leave the current conversation flow. Whether it’s typing “quit”, timing out, or even erroring out. The bot should always allow you to exit gracefully without making the user feel guilty.


Whether you love them or hate them, conversational interfaces are making a significant impact in the business/brand communication landscape. In this article I’ve looked at some of the best practices to build out conversations for your bot or voice assistant. In my next post, I’ll look at some of the other best practices of building conversational products.

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