Actionable Insights Start with Conversational AI Tag Suggestions

Although Conversational AI has the potential to deliver actionable insights, results are only as good as the data you provide. Tapping into the value of customer conversations to extract actionable insights requires a systematic approach across people, technology, and business culture. That's why effective enforcement of conversation tag suggestions is a critical prerequisite  for extracting actionable insights. By assigning relevant tags to customer conversations, such as product categories, new service requests, and other topics, organizations can unlock a treasure trove of valuable information.

Conversation Tag Automation in Glassix Conversational AI Suite

Conversational AI insights are only as good as your underlying tagging data

Imagine a scenario where a company wants to analyze customer feedback to identify areas for improvement specific to the customer journey. They have collected a vast amount of customer conversations across multiple channels spanning human agents and chatbots. Without relevant conversation tag data, it is challenging to analyze customer conversations effectively. For instance, a customer may have expressed frustration with the goods - even though the actual issue was damage caused during the delivery. Without a category tag specific to "delivery damage", the company may fail to prioritize aspects of the business that would resolve the shipping issue.

Without accurate tagging practices, companies are more likely to struggle to identify the most frequently discussed issues or trends among their customers. This makes it difficult to spot recurring problems, patterns, or emerging market needs. As a result, organizations may miss out on opportunities for innovation or simply fail to address critical issues in a timely manner, putting their competitive edge at risk.

The impact of bad tagging practices can be devastating. Inaccurate or inconsistent tagging can lead to biased or misleading insights, undermining the entire process of data analysis. Considering organizations manage an average of 35 tags, human errors relating to tagging are common. For example, if a human agent is unaware of our recently added tag "delivery damage", it can skew the analysis, leading to incorrect conclusions and misguided decision-making.

To avoid such pitfalls and ensure accurate analysis, organizations must prioritize the implementation of robust conversation tagging frameworks. Conversational AI-powered tools and technologies can streamline the tagging process, reducing human error and increasing efficiency. By enforcing effective conversation tagging policies, businesses can unlock valuable information, improve customer experiences, and make better data-driven decisions.

The Conversational AI tag suggestion capability is implemented in the Glassix Conversational AI Suite.