Does your FAQ stand for Fail to Answer Questions? · Nu Echo

Yves Normandin
Nov 26, 2019 · 4 min read

From FAQs to chatbots: Improve customer experience with conversational question answering.

A significant portion of customer service inquiries is about users wanting an answer to a question. Organizations are rightfully motivated to provide efficient means for users to find answers to their questions autonomously (i.e, without interacting with a human agent) since it can improve user experience while greatly reducing costs by freeing up valuable time for their customer service agents.

To achieve this, organizations traditionally propose a Frequently Asked Questions (FAQ) section on their website and they also often provide a search capability that can return relevant articles from a knowledge base, the website, or both. In many cases, these can provide fairly effective means for users to get the information they’re looking for, therefore reducing pressure on the contact center.

In that context, how can a question-answering customer service chatbot add value? Certainly, that cannot be by providing a chat-like interface to a static FAQ or to an existing website search capability. That just wouldn’t be very compelling (for a discussion on this topic, see Tobias Goebel’s great blog post explaining why we can’t just convert FAQs into a chatbot 1:1).

Chatbot question answering: beyond static FAQs and search

In order to really provide question answering value, a customer service chatbot has to go beyond the FAQ capabilities already provided on the website. This can be achieved in a number of ways, including by:

  1. Directly answering user’s questions rather than providing links to relevant documents. If I ask “Are strollers allowed on airplanes?” I’d like to have a clear response (“Yes, strollers are allowed.”) rather than list of articles that may or may not answer my question.
  2. Truly leveraging a conversational interface, for instance by enabling the chatbot to clarify vague questions (User: I’m looking for a telephone number; Chatbot: Who would you like to call? User: Lost items) or by enabling users to ask follow-on questions (User: Can I bring breast milk on a plane? Chatbot: Yes, breast milk is allowed on airplanes. User: What about strollers? Chatbot: Strollers are also allowed).
  3. Providing dynamic and/or personalized answers, which require access to back-end systems. For instance “What is the arrival time for flight United 285?”, or “When should I expect to receive my luggage?
  4. Enabling question answering at any time during the course of a chatbot conversation.
  5. Giving users the ability to continue the conversation with a human agent, if the chatbot isn’t able to solve the user’s issue.

In a chatbot, the very frequent queries (the short tail) can — and should — be handled using standard approaches (e.g., with intents and entities). While that requires work to maintain the chatbot to handle those new frequent queries that will inevitably occur, it’s the approach that will provide the best results.

Meanwhile, however, there will always be all those long tail queries that would just require too much effort to try to support that way. So when the chatbot doesn’t have the answer to a question, it is best to fall back to a search-like mode that can automatically leverage all those documents and knowledge bases that you already have. They most likely contain answers to many of these questions. This not only reduces development effort, but it makes it much easier to keep the system up to date with the latest answers.

Search-like capabilities in conversational platforms

Some conversational platforms provide search-like capabilities that make it possible to automatically leverage existing knowledge bases or documents to search for answers to those user queries that the chatbot cannot answer. For instance:

  • Chatbots developed with Watson Assistant can leverage Watson Discovery for that purpose. Performance can be improved by using Watson Knowledge Studio to teach Watson about the language and relationships that are useful in order to understand your specific domain or industry.
  • Chatbots developed with Google Dialogflow can leverage Dialogflow’s Knowledge Connectors to search knowledge bases for a response to a user query. Knowledge connectors are offered in two varieties: FAQs and knowledge base articles. FAQs are used to integrate existing Frequently Asked Questions (e.g., from a website). In that case, finding a response means finding the FAQ question-answer pairs (QA pairs) that best match the user query. With knowledge based articles, Dialogflow actually looks for the answer to user queries within the articles and returns the most relevant portion of the article as answer.

In future blog posts, we will report on experiments with some of these platforms. Stay tuned.

Originally published at on November 26, 2019.


A blog about delivering the best customer experience in omnichannel contact center projects and solutions, speech and conversational technologies, A.I., chatbots, etc.

Yves Normandin

Written by


A blog about delivering the best customer experience in omnichannel contact center projects and solutions, speech and conversational technologies, A.I., chatbots, etc.

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