---
title: "AI Chatbot Analytics Reporting in HitKeep | HitKeep"
description: "Measure on-site AI chatbot usage with a structured event schema, audience context, and aggregate city, provider, and ASN reporting."
canonical: "https://hitkeep.com/guides/analytics/ai-chatbot-analytics/"
---

# AI Chatbot Analytics Reporting in HitKeep

AI Chatbot Analytics shipped in **HitKeep 2.2.0**. The dashboard is built on top of structured custom events, so teams can instrument support bots, docs bots, shopping assistants, and AI search experiences without deploying a separate analytics pipeline.

The report is built on top of normal custom events, which keeps the instrumentation model simple and portable.

This page is not about inbound AI traffic. It measures **your own** on-site conversational assistant — the widget you shipped — from `assistant.*` events your code sends. AI agents crawling your site and humans arriving from ChatGPT or Perplexity are the opposite direction of traffic and are reported on the AI Agents page instead. See [AI Visibility Analytics](https://hitkeep.com/guides/analytics/ai-visibility/) for that side.

![HitKeep AI chatbot analytics with KPI cards, conversation activity chart, and chatbot breakdown panels](https://hitkeep.com/_astro/analytics-ai-chatbots.Bgkz5xbl_ZA39bH.webp)

Conversation KPIs, timeseries activity, and chatbot-specific breakdowns on the Chatbots page.

## What this report measures

The Chatbots page (labeled **Chatbots** in the sidebar, route `/ai-chatbots`) focuses on on-site chatbot usage and outcomes:

- conversation starts
- prompts sent
- responses rendered
- citation clicks
- handoff requests
- assisted conversions

Because this is event-based analytics, the report supports the same time ranges, audience context, and share links as the rest of the dashboard.

## Recommended event schema

Use these event names consistently:

| Event name | When to send it | Recommended properties |
| --- | --- | --- |
| assistant.chat_started | User opens or starts a chatbot session | bot_id, provider, model, surface |
| assistant.message_sent | User submits a prompt | conversation_id, message_index, intent |
| assistant.response_rendered | A model response is shown to the user | conversation_id, message_index, response_ms, tool_count, citation_count |
| assistant.citation_clicked | User clicks a cited source or linked answer | conversation_id, citation_url, citation_index |
| assistant.handoff_requested | User asks for a human or the bot escalates | conversation_id, message_index, reason |
| assistant.goal_assisted | A conversion happens during or after the chatbot flow | conversation_id, goal_name, goal_value |

## Property guidance

Keep the payload metadata-focused.

- Use stable identifiers such as `bot_id` and `conversation_id`.
- Prefer short categorical values like `provider=openai` or `surface=support-widget`.
- Store performance and usage metrics like `response_ms`, `tool_count`, and `citation_count`.
- Do not send raw prompt or response bodies by default. Structured metadata is usually enough and is much safer from a privacy perspective.

## Browser example

```
<script>
  const conversationId = crypto.randomUUID();

  window.hk?.event?.('assistant.chat_started', {
    bot_id: 'support-bot',
    provider: 'openai',
    model: 'gpt-4.1-mini',
    surface: 'help-center'
  });

  window.hk?.event?.('assistant.message_sent', {
    conversation_id: conversationId,
    message_index: 1,
    intent: 'billing'
  });

  window.hk?.event?.('assistant.response_rendered', {
    conversation_id: conversationId,
    message_index: 1,
    response_ms: 842,
    tool_count: 1,
    citation_count: 2
  });
<\/script>
```

## What the dashboard shows

The Chatbots page groups these events into a dedicated workflow:

- KPI cards for conversation volume, prompts, responses, assisted conversions, handoff rate, and citation CTR
- timeseries chart for conversation starts, responses, handoffs, and assisted conversions
- breakdown panels for intents, providers, and surfaces
- audience context such as top pages, sources, devices, countries, city, provider, and ASN

You can also scope the report by `provider`, `bot_id`, `surface`, or `model`.

## Recommended implementation pattern

1. Start by sending `assistant.chat_started` and `assistant.message_sent`.
2. Add `assistant.response_rendered` once you can measure latency and citations.
3. Add `assistant.handoff_requested` and `assistant.goal_assisted` to connect usage with support and conversion outcomes.
4. Keep property names stable so dashboard filters remain useful over time.

## Related

- [Custom Events](https://hitkeep.com/guides/tracking/custom-events/)
- [AI Visibility Analytics](https://hitkeep.com/guides/analytics/ai-visibility/)
- [Goals](https://hitkeep.com/guides/analytics/goals/)
- [Funnels](https://hitkeep.com/guides/analytics/funnels/)

[Previous AI Agents dashboard](https://hitkeep.com/guides/analytics/ai-visibility/)[Next AI visibility reporting template](https://hitkeep.com/guides/analytics/ai-seo-reporting-template/)