sales-skills/sales-maestroqa

MaestroQA Platform Help

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated platform knowledge.

1. What do you need?

   • A) Set up or refine QA scorecards
   • B) Build coaching workflows from QA data
   • C) Configure conversation analytics / AskAI
   • D) Integrate with helpdesk, phone system, or CRM
   • E) Export data to warehouse or via API
   • F) Monitor AI chatbot quality
   • G) Ingest CSAT scores and correlate with QA
   • H) Other

2. Current setup?

   • A) New to MaestroQA (not yet connected)
   • B) Connected to helpdesk, building scorecards
   • C) Running QA, want to improve coaching
   • D) Mature — need analytics, API, or warehouse export

3. Helpdesk / phone system?

   • A) Zendesk / Freshdesk / Intercom / Front / Gorgias
   • B) Salesforce Service Cloud / ServiceNow / Dynamics 365
   • C) Five9 / Talkdesk / Amazon Connect / NICE / Genesys
   • D) Aircall / RingCentral / Dialpad / Twilio / Vonage
   • E) Other / multiple

Skip-ahead rule: if the user's prompt already contains enough context, skip to Step 2.

Step 2 — Route or answer directly

| Problem domain | Route to | |---|---| | Comparing QA tools (MaestroQA vs Observe.AI vs Enthu.AI) | /sales-coaching for cross-platform comparison | | Choosing a CCaaS platform | /sales-ccaas-selection | | Real-time agent coaching during calls | /sales-balto or /sales-cresta | | Help desk platform selection | /sales-helpdesk-selection | | Salesforce-specific questions | /sales-salesforce | | Sales call review and coaching | /sales-call-review |

When routing, provide the exact command: "This is a {problem domain} question — run: /sales-{skill} {user's original question}"

Step 3 — MaestroQA platform reference

Read references/platform-guide.md for the full platform reference — modules, pricing, integrations, data model, workflows.

Answer the user's question using only the relevant section. Don't dump the full reference.

Step 4 — Actionable guidance

Focus on the user's specific situation.

• Scorecard design: Start with 5-8 binary criteria per scorecard. Binary (yes/no) scores faster and more consistently than scaled (1-5). Add weighted sections for compliance vs quality vs sales effectiveness.
• Coaching connection: Link low QA scores to coaching sessions automatically — don't let scores sit in reports. Use reverse-ETL to push coaching tasks to Slack or your CRM.
• Analytics adoption: Start with AskAI for ad-hoc queries before building custom dashboards. AskAI answers natural language questions about conversation data without needing SQL.
• API automation: Use the Rippit API for bulk data export, CSAT ingestion, and agent provisioning. Rate limit is 10 req/s — batch operations where possible.
• Chatbot QA: Connect Ada, Sierra, Agentforce, or Forethought to grade bot conversations with the same scorecards used for human agents.

If you discover a gotcha, workaround, or tip not covered in references/learnings.md, append it there.

Gotchas

Best-effort from research — review these, especially items about plan-gated features and integration gotchas that may be outdated.

• AI features cost extra. MaestroQA's AI-powered analytics and conversation intelligence are add-ons — budget separately from the base QA subscription.
• Ticket sync delay. Zendesk tickets typically take up to 3 hours to sync. Only tickets updated within the last 45 days sync. Plan QA workflows around this lag.
• API tokens expire every 90 days by default. Set up a rotation process or disable expiry for long-lived integrations.
• Export format limitations. Some users report inability to export grading progress as CSV/PDF directly from the UI. Use the Rippit API POST /request-raw-export endpoint as a workaround.
• Dashboard customization is limited. For advanced analytics, export to Snowflake/BigQuery and use external BI tools.
• Learning curve. MaestroQA requires upfront investment to configure scorecards, calibration sessions, and coaching workflows. Budget 2-4 weeks for initial setup with a dedicated QA admin.
• Rate limits. API is 10 req/s and 100 req/min. Batch operations (comments, metrics) accept up to 500 items per request — use these instead of individual calls.

Related skills

• /sales-coaching — Sales coaching and training strategy across all QA and enablement platforms
• /sales-observe-ai — Observe.AI platform help (enterprise contact center QA with Auto QA, Agent Copilot)
• /sales-enthu — Enthu.AI platform help (affordable contact center QA, fast setup)
• /sales-balto — Balto platform help (real-time AI guidance during calls)
• /sales-cresta — Cresta platform help (enterprise contact center AI)
• /sales-convin — Convin platform help (contact center QA + coaching + LMS)
• /sales-ccaas-selection — Choosing a CCaaS platform
• /sales-helpdesk-selection — Choosing a help desk platform
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Examples

Example 1: Set up QA scorecards for a support team

User says: "We just connected Zendesk — how do I build QA scorecards for our support team?" Skill does: Walks through scorecard design (binary criteria, weighted sections, compliance vs quality), calibration setup for evaluator consistency, and assignment rules for random sampling vs targeted review.

Example 2: Export QA data to Snowflake

User says: "I need to get our grading data into Snowflake for custom reporting" Skill does: Covers the native Snowflake integration setup and the Rippit API export endpoints (POST /request-raw-export, GET /export-data/:id) as alternatives, with rate limit considerations.

Example 3: Correlate CSAT with QA scores

User says: "We want to see if our QA scores actually predict customer satisfaction" Skill does: Explains the CSAT Bulk Ingestion API, how to connect Qualtrics/Delighted/Simplesat for native CSAT ingestion, and how to use AskAI to query correlations between QA scores and CSAT data.

Troubleshooting

QA scores don't correlate with customer satisfaction

Cause: Scorecards measure process compliance (did the agent follow the script) but not outcome quality (was the customer actually helped) Solution: Add outcome-oriented criteria — resolution quality, customer effort reduction, empathy signals. Use CSAT ingestion to validate that QA criteria predict customer satisfaction. If they don't, revise criteria.

Evaluators score the same ticket differently

Cause: Criteria are ambiguous or subjective Solution: Use binary (yes/no) criteria instead of scaled (1-5). Run calibration sessions monthly — have 3+ evaluators score the same 5 tickets independently, then discuss disagreements. MaestroQA has built-in calibration tools for this.

Coaching sessions feel disconnected from QA data

Cause: QA scores sit in MaestroQA dashboards but coaching happens in a separate tool or ad-hoc Solution: Use MaestroQA's coaching workflows — low scores automatically trigger coaching sessions with specific call segments linked. Use reverse-ETL to push coaching tasks to Slack or your CRM so supervisors see them in their workflow.