Tagado's New Smart Monitoring Tool Categorizes and Groups

Tagado’s New Smart Monitoring Tool Categorizes and Groups Customer Feedback in Real-Time

PRNewswire

February 21, 2024

Tagado, the AI analytics provider of high-res CX data intelligence, has announced the launch of its new feedback trends monitor, that automatically allows CX, service and product teams to segment feedback into issues so evolving trends can be instantly prioritized, assigned, resolved and monitored over time.

Traditional planning and task management solutions used by CX, service and product professionals are disconnected from direct customer feedback, but Tagado’s new monitor bridges this gap by bringing customer insights directly to the forefront of CX and product management. It features intelligent recognition of similarities in feedback, allowing issues to evolve dynamically. Supported by metadata, this tool pinpoints issues that are not only highly relevant but also directly tied to ROI, and can be integrated into the user’s existing task management software.

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“Tagado’s new monitor empowers CX, product and service teams to continuously use customer feedback to drive product and customer experience decision-making,” says Gilad Gans, Active Chairman at Tagado. “This underlines how the Tagado platform helps customer-obsessed organizations adopt data-driven practices that put customer feedback in the center.”

Tagado’s new monitor also features an Alerts function, notifying users of changes within CX data to highlight emerging trends as they happen. Tagado’s Alerts can be customized to identify changes in CX data, based on specific topics, metadata (e.g. UK customers only) or thresholds (e.g. hourly changes over 300%).

While the new monitor allows users to actively define the issues they want to follow, Alerts examines the full set of CX data to notify users of trends occurring in real-time. This means for the first time support and customer success teams can monitor the issues they have predefined while also being alerted to anomalies occurring in real-time within the data so they can see trends emerge as they happen.

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