AI and chatbots: How to design a great conversation?

Reducing complicated, confusing processes down to a natural conversation is potentially a huge business opportunity for anyone willing to jump headfirst and create a great user experience. Chatbots are only as smart as the words you feed them. If a bot is too rudimentary, people will lose trust in the company and will feel ignored and unappreciated. UX problems appear when user deviates from the designed linear flow.

How do we make chatbots usable?

Imagine how much simpler doing your taxes, booking a vacation or learning how to use a complicated product could be with a friendly virtual assistant on hand to help you through the process.

Here are some usability practices to help you create the best CX experience based on a recap of some of the lessons we learned when designing chatbots for our customers:

  • Transparency: Some businesses do not always disclose upfront to their customers that they are interacting with a bot. We believe that this is a mistake. Be clear about the chatbot goal upfront. Don't let people guess the functionalities of you bot. Clarify the goal as soon as they onboard, and in the bot description.
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  • Avoid an excess of predetermined links and buttons. These tools save users from typing but free-text input allow users flexibility in choosing the types of questions they need to ask and deviate from the (often too strict) chatbot flow. This flexibility is highly needed to design a great conversational experience.
    Paul Demo MP4
  • Some bots have trouble establishing the context of a query or are not able to take advantage of previously entered information when a new query is asked by the same user. For example, when a user has logged in a web page and has already completed his personal details it is useless to request them again.
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  • Use an NLP middleware approach: Chat, as a medium, tends to be slightly more direct than face-to-face or telephone conversation, possibly because of the higher interaction cost of typing. Unlike statistical methods, it allows users to talk naturally and reaches extremely high levels of accuracy in key tasks like intent detection.
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  • Use linguistic models where politeness and sociocultural dimensions of the conversation (small talk) are included: acknowledgement tokens as please and thanks/thank you are expected to be used both on the human and the machine sides.
  • Tolerate typos and slightly ambiguous formulations.
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  • Always include domain specific terminology as language and expressions related to the product may vary from one business to another.
  • The type of content your bot provides will dictate the tone of its speech. If your bot is news-focused or meant to guide users through a process involving payments or other sensitive information, a formal and authoritative tone is advisable.
  • Try to keep the number of possibilities limited, to facilitate choices. Carousels, the UI element that bots use for showing sets of results, are simply not the best choice for displaying long lists.
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  • Consider undo and cancel as a functionality for a smooth conversational experience. The user should be able to suspend a search or go back a step in a conversation easily. Allow the user to jump forward and backward in the linear flow to enhance its customer experience.
  • Keep each text short and easy to read, as most people tend to read quickly. We recommend no more than 90 characters per message (around three lines on mobile).
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  • Offer an escape hatch in the form of a real human, a phone number, or a link to a different interaction channel or provide a way back to the “main menu” somehow.
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  • If you anticipate that it will take longer than a few seconds for a chatbot to reply to something, let the user know upfront, and monitor the process to let the user know that things are taking longer than normal. Use visual cues such as a “typing symbol”.

 

At Bitext, we can enhance the design of your chatbot. Our NLP middleware approach puts linguistic knowledge to serve bot builders. Therefore, one can expect a truly conversational experience from chatbots that benefit from this middleware.


We also introduced a new way to speed up the deployment of new domains and languages for any bot platform. If you would like to have more info about Bitext's Artificial training data, also called multilingual synthetic data using our Natural Language Generation services, please contact us for a demo!

Here are some additional resources:

SYNTHETIC TRAINING DATA

 

 

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