Support Macro A/B Test Framework
Design and evaluate A/B tests for support macros so teams can improve resolution quality, CSAT, and handle time without guessing which response style works best.
Prompt Template
You are a support operations specialist. Create an A/B testing framework for customer support macros so we can improve outcomes with evidence instead of opinions. **Support channel:** [email, chat, helpdesk, in-app] **Macro or workflow to test:** [refund response, bug acknowledgement, onboarding help, billing reply, etc.] **Current problems:** [slow handle time, low CSAT, too robotic, poor resolution rate] **Volume:** [tickets per week/month] **Team size:** [number of agents] **Support stack:** [Zendesk, Intercom, Help Scout, Gorgias, etc.] **Metrics available:** [CSAT, first response time, reopen rate, resolution time, escalations] Build: 1. **Hypothesis set** — what exactly we are testing and why 2. **Variant design** — control vs test macro, tone differences, structure differences, CTA differences 3. **Success metrics** — primary and guardrail metrics 4. **Experiment design** — sampling, randomization, duration, and segmentation 5. **QA checklist** — how to keep the test fair and safe for customers 6. **Analysis template** — how to interpret winners, mixed results, and no-result tests 7. **Rollout plan** — how to ship the winning macro and retrain the team 8. **Example macro variants** for the scenario provided
Example Output
# Macro Test: Billing Dispute First Response
Hypothesis
A more empathetic opening plus a clearer next step will improve CSAT without increasing average handle time.
Variants
- **Control:** direct policy explanation first
- **Variant B:** acknowledge frustration first, then policy, then next action
Metrics
- Primary: CSAT
- Secondary: one-touch resolution rate
- Guardrails: handle time, reopen rate, escalation rate
QA Checklist
- Use the same agent pool for both variants
- Restrict to English-language billing tickets
- Freeze policy wording so only tone and structure change
- Review 20 live tickets manually before expanding test
Readout Example
Variant B increased CSAT from 78% to 85% with no meaningful rise in handle time. Reopen rate dropped 2.1 points. Ship Variant B as new default.
Tips for Best Results
- 💡Test one meaningful variable at a time, otherwise you will not know what caused the result.
- 💡Use guardrail metrics so a macro does not improve CSAT while quietly increasing escalations or handle time.
- 💡Keep policy content consistent across variants unless policy clarity is the thing being tested.
- 💡Small support experiments compound. One better macro can affect thousands of tickets.
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