Customer Support Bot Testing & QA

Customer support bots face uniquely emotional interactions. Testing must go beyond accuracy to measure empathy, escalation judgment, and policy compliance. 1. **Scenario libraries** — Build test scenarios from real support tickets. Categorize by emotion (frustrated, confused, angry) and topic (billing, technical, account). Each scenario should have defined success criteria beyond just the correct answer. 2. **Escalation accuracy** — Test that your bot escalates at the right moments: legal threats, safety concerns, repeated failures, and explicit agent requests. Equally important: test that it doesn't escalate unnecessarily, which wastes human agent time. 3. **Policy compliance** — Define rules your bot must follow (refund policies, data handling, disclaimers) and test adherence. A bot that promises a refund outside policy creates liability. 4. **Tone calibration** — Test responses to frustrated customers specifically. The bot should acknowledge emotion before providing solutions. Score responses on empathy, not just correctness.

// Escalation decision test
{
  scenario: "angry_billing_dispute",
  messages: [
    "I was charged twice for my subscription!",
    "This is the third time this happened!",
    "I want to speak to a manager NOW"
  ],
  expected: {
    should_escalate: true,
    gather_before_escalate: [
      "account_id", "charge_details"
    ],
    tone: "empathetic_and_apologetic"
  }
}