Hotjar → ChatGPT → Slack: User Behavior Insights

Set up Hotjar to capture heatmaps, session recordings, and funnel analytics on key pages. Configure event-based triggers to export aggregated behavior data such as rage clicks, drop-off points, and scroll depth metrics via the Hotjar API. Focus data collection on high-traffic pages and conversion funnels where user friction has the greatest business impact.

Pipe the raw Hotjar data exports into a Google Sheets workbook where metrics are organized by page, date, and event type. Calculate week-over-week trends for key indicators like rage click frequency, funnel completion rates, and average scroll depth. This structured dataset gives the AI model clean, comparable data to analyze rather than raw event logs.

Send the aggregated and structured behavior data to ChatGPT with a prompt that identifies UX issues, conversion bottlenecks, and areas of high engagement. Ask it to prioritize findings by potential revenue impact and suggest specific design improvements. The AI compares current metrics against previous weeks to flag statistically significant changes.

Post the formatted insight reports to dedicated Slack channels for product and design teams. Include key metrics, identified issues, and recommended actions in a structured message format with links back to the relevant Hotjar recordings. Use threaded replies for each insight so team members can discuss and assign follow-ups directly in Slack.