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Making automated flows more flexible is an opportunity in your interactions

The design of an automated conversational flow is a crucial aspect in the development of chatbots and virtual assistant systems. One of the main concerns in this process is to prevent users from getting stuck in complicated, unintuitive sequences or where they cannot move forward. These problematic moments are commonly known as bot “traps” or “loops” and can result in user exhaustion, abandonment of the interaction and negative impact on important metrics such as Net Promoter Score (NPS).

Fortunately, there are deductive tools that can address this challenge and improve the user experience by quickly identifying user needs and steering the conversation flow in the right direction. One of the most popular tools for this purpose is Dialog Flow. This platform offers the ability to create lists of keywords that trigger proactive actions in the conversation, contributing to a smoother and more satisfying interaction with the bot.

At OneMarketer, we have incorporated Dialog Flow into our chatbot system to redirect the conversation flow when relevant keywords are detected. This implementation has resulted in streamlined processes, shorter interactions, and an increase in user satisfaction rates at the end of a support case. Let me illustrate two key situations where this tool has proven effective:

Bot deflection: 

Imagine a user is going through a support process and, at a certain point, expresses interest in purchasing or pricing. In this scenario, our bot automatically redirects the user to a conversation flow specialized in sales transactions. This ensures that the user receives the appropriate attention based on their changing needs.

Case referral:

On the other hand, if during the user’s interaction with the chatbot a red flag is detected, such as words like “dissatisfied” or “complaint”, the bot can not only redirect the conversation to a human agent, but can also connect the user to an agent specialized in complaint or hold cases. This makes it possible to address critical situations efficiently and provide personalized service.

In summary, the key to improving the user experience in an automated conversational flow lies in anticipating the user’s needs and expectations through the use of deductive tools. By identifying keywords and triggering specific actions, a more fluid, adaptive conversation focused on the particular needs and opportunities of each user is achieved. This not only improves real-time interaction, but also contributes to overall user satisfaction and better performance on key metrics such as NPS.

Would you like to know more? Write us at and we will be happy to tell you more.