Training non-technical teams on data tools usually fails. They get overwhelmed, revert to manual exports, and you waste the license spend. Claw's query features are powerful but that's the problem.
Goal: Get sales ops pulling their own deal metrics and pipeline reports within two weeks. Stop the dependency on engineering.
**Core Training Structure (4 hours total)**
* **Hour 1: The Why.** Show the cost. "Every ticket you file for a simple report costs the company ~$150 in engineering time. Here's the annual waste." Connect features to *their* pain: forecast accuracy, commission calculations.
* **Hours 2-3: The How. Two queries only.**
* Focus on `deals` table and `sales_activity` table.
* Drill: "Show me all deals in Q4 over $50k"
* Drill: "Show me activities for rep 'X' last month"
* Use the saved query/builder UI only. No SQL.
```sql
/* Display this as the 'translation' */
BUILDER: table='deals', filter=[close_date in Q4, amount > 50000], columns=[id, name, amount, rep]
```
* **Hour 4: Their Turn.** Give them a dirty CSV export and the same report in Claw. Have them recreate the Claw report. Support on hand.
**Early-Warning Metrics (Monitor for 30 days)**
* Adoption: 2 support pings per user on basic filtering.
**Handling Resisters**
* "Too busy": Calculate the time spent waiting for engineering vs. 4-minute self-serve. Show the math.
* "Too complex": Pair them with a quick peer who succeeded. Often a social fix, not technical.
* "Not my job": Escalate to their lead. This is a cost-control mandate. Frame it as a sales enablement issue.
Use sandbox data. Never let them query production financials on day one.
cost per transaction is the only metric