SleekAI emits a small set of named events through gtag when it runs alongside Google Analytics 4. sleekai_open fires when the widget opens. sleekai_message fires per user message. sleekai_deflected fires when the conversation closes without handoff. sleekai_handoff fires when the bot escalates to a human channel. Each event includes the chatbot name, the originating page URL, the model, and the outcome label, so funnels in GA4 can split self-serve success from escalation cleanly.

Because the events come through gtag, every GA4 mechanism keeps working: real-time view, explorations, audiences, and conversion marking. Mark sleekai_handoff as a key event and you have a measurable conversion tied to assistance quality. Use the page URL parameter to segment by content type and see which docs deflect well and which produce escalations.

Display conditions inside SleekAI let you scope tracking to whatever bots you choose, and the conversation log on the WordPress side stays the source of truth for content and tone. GA4 holds the aggregate counters. WordPress holds the raw transcripts. Together they give you both the chart and the example you need to act on it.

Chatbot data lives in a vendor dashboard by default. You see open counts, message counts, and a deflection rate, and you have no way to blend that data with the rest of your analytics. Did that landing page produce useful conversations or just confused them? Did the campaign that drove this audience send people who self-served or people who needed help? You cannot answer those questions because the chat metrics live in one tool and the acquisition metrics live in another.

SleekAI fixes that by pushing the lifecycle of every conversation into GA4 as named events with the parameters that matter: chatbot name, page URL, model, and outcome. The signal joins the funnels, segments, and audiences you already work with. Marking handoff as a key event turns deflection into a conversion you can optimise toward.

Building a funnel from open to deflected vs handoff shows you which pages quietly resolve and which produce support load. And because the raw transcripts still live in WordPress, you can drill from the GA4 counter into the actual conversation that drove it, so the chart and the example sit one step apart. That is the loop most chat tooling never quite closes.