Load your conversations table and join it to a users table with a signup_date . Create a pivot table comparing first response time for week-1 users vs. year-1 users. Hypothesis: New users tolerate slower responses, but power users expect instant help.
By moving your conversational data into an associative analytics engine, you stop managing tickets and start improving your product. Start small: extract just conversations and users , build one dashboard on response times, and expand from there. intercom to qlik
Measure expression:
Avg(Churn_Rate) by Tag If #export-slow has a 40% churn rate and #forgot-password has 5%, you know where to send the product team. Load your conversations table and join it to
In Intercom, agents should tag conversations with topics (e.g., #billing-error , #export-slow ). In Qlik, count conversations by tag per customer. Then overlay that with your churn dataset. Hypothesis: New users tolerate slower responses, but power