Four steps to build a great chatbot

All companies that have a large number of clients want to be “customer-centric”, always placing the customer as the center of their strategies. This translates into taking good care of them, promptly and 24/7, without increasing costs, if possible.

With that goal in mind, one of the most important strategies is to automate any “routine” elements of customer support, until now performed with Call Center agents by voice or chat. It goes without saying that chatbots are the tool used to reach this objective, but the goal is hardly ever met: chatbots are usually ill-defined and don’t understand well customers’ needs.

Here is our advice: if you want to build a successful chatbot, you have to carefully look into these 4 steps:

  • PROFILE: Define the profile of your user base and audience, and why you are using your chatbot for (e.g. internal use, sales, customer support, access to FAQs, etc.). This will allow you to create an ontology or knowledge graph that will help you develop or decide the training data you will need.
  • CONVERSATION: Design your conversation flows and the subjects (intents) you want your chatbot to answer. Here, less is more: it’s preferable that the bot answers fewer intents but correctly than many imprecisely. Train your chatbot with data coming from your actual customers (how they ask doubts in reality), or find a partner to help you train the bot via Synthetic Training Data (such as Bitext).
    From the beginning your chatbot should show good accuracy, or your users will stop using it altogether.
  • IMPLEMENTATION: Choose wisely the platform you’ll use. Some platforms are better for a given type of chatbot: some are more conversational, some others are more straightforward; their price vary significantly, and so no. Don’t overreact!
  • MONITORING: Take into account that many of these things won’t be useful for you if you are not able to measure your precision (how you are solving your users’ doubts) and which questions are being asked but not solved. A monitoring, Quality Assurance and failed query analysis service will allow to re-train your model and make your bot better and better.


We would be very glad to help you in this process. Let’s talk



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