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7 Key Metrics Every AI Chatbot Should Track

· 2 min read
Daniel Garcia
CEO @ Optimly

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AI-powered agents are now part of critical business workflows—support, onboarding, sales, internal tooling. Yet most teams have no clear view of how those agents are actually performing. Are users satisfied? Are responses helpful? What’s causing drop-offs?

To manage what matters, you need to measure what matters.

In this post, we break down the seven most important metrics your team should be tracking to improve any chatbot or LLM agent.

1. Total Sessions and Active Users​

Start with basic engagement. How often is your agent being used?
This gives you context for every other metric.

  • Why it matters: Tells you whether the agent is being adopted at all.
  • What to watch: Growth trends, usage by time of day, spikes after releases.

2. Abandonment Rate​

When do users give up? Are they getting frustrated or confused?

  • Why it matters: A high abandonment rate often signals failed responses or unclear flows.
  • How to detect it: Look for short sessions or exit right after a model reply.

3. Repeat Questions​

Are users asking the same thing in different ways?

  • Why it matters: Indicates poor understanding or low-quality answers.
  • Optimization tip: Track phrases or intents that reappear in a short time window.

4. Token Usage per Session​

Tokens are money. They also reflect verbosity or efficiency.

  • Why it matters: Shows how efficient your prompt + model combo is.
  • Watch for:
    • Long replies with no value
    • Repetitive answers
    • Sessions with high token count and poor outcomes

5. Response Quality and Satisfaction (Direct or Estimated)​

Do responses help users? Or create confusion?

  • Why it matters: This is the ultimate health metric for any LLM agent.
  • Sources:
    • Thumbs up/down buttons
    • Rephrasing signals
    • Follow-up sentiment analysis
    • Abandonment after response

6. Document (RAG) Usage​

Are your knowledge bases doing anything?

  • Why it matters: If you’re using RAG, you want to know which documents help—and which are ignored.
  • Bonus: You can optimize your document set and remove unused context sources.

7. Tool Activation and Success Rates​

If your agent triggers actions (e.g., lead capture, ticket creation, API call), measure them.

  • Why it matters: These are signs of real-world value.
  • What to track:
    • Tools triggered per session
    • Errors vs. successful activations
    • Time from query to action

Turning Metrics into Improvements​

Once you track these metrics, you can start:

  • Rewriting underperforming prompts
  • Adjusting agent behavior by context
  • Improving RAG quality
  • Reducing token waste
  • Justifying LLM costs with real impact

But none of this happens without visibility.

How to Track These Metrics (Without Rebuilding Your Stack)​

You can either:

  • Build a full analytics layer from scratch
  • Or plug into a platform like Optimly

Optimly automatically tracks:

  • Every message, session, and agent
  • Flags for frustration, confusion, and abandonment
  • RAG usage
  • Token costs
  • Comparative performance across agents

Whether you're using OpenAI, Claude, Cohere or your own model—Optimly works as your analytics layer.

Want to start tracking what actually matters?

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