From skills to bottom line: share playbooks, KPIs, dashboards and experiment designs that prove skills ROI

Problem brief: organizations often can’t see who knows what or how quickly skills translate into revenue, margin or cost savings. I’m starting a focused thread to collect concrete artifacts—metrics, data models, dashboards, templates and causal methods—that prove the financial value of skills programs and smarter talent allocation.

What to share (please be concrete):
- KPIs you track (exact formulas): e.g., revenue per skill-FTE, billable utilization by skill, speed-to-proficiency, internal mobility rate, cost-per-skill-upskilling, margin uplift from skill-led projects, avoided hiring cost, churn delta after training.
- Data model/schema snippets: required tables/fields (person, role, skill, skill_level, assignment, time_entry, project_revenue, training_event, cost), sample joins, key dimensions and grain.
- Dashboards & tools: screenshots, metric definitions, queries or LookML/PowerBI/DAX snippets, integrations (Workday, LMS, ATS, Jira, Harvest, QuickBooks).
- Experiment / evaluation designs: RCTs, randomized pilots, difference-in-differences, propensity matching, synthetic controls, pre/post with controls — include sample size, timeframe, and how you defined treatment vs control.
- Templates & calculators: ROI calculators, cohort analysis templates (CSV/Google Sheets/SQL), dbt models, KPI tracker templates.
- Case studies & lessons: short before/after numbers, what worked, what didn’t, unexpected failure modes, measurement pitfalls (double-counting, attribution windows, selection bias).

Starter checklist for a pilot (share if you’ve run something similar):
1) Define business hypothesis (e.g., 12-week upskill on X increases billable utilization by Y pp) and primary KPI.
2) Map data sources and minimal schema to link people → skills → assignments → revenue/cost.
3) Choose evaluation method and pre-register the analysis plan.
4) Run a randomized or matched pilot, collect 6–12 weeks of post-deployment data, compute incremental revenue/cost changes and payback period.
5) Document the experiment artifacts so they’re repeatable.

Please post exact formulas, SQL/LookML/dbt snippets, sample CSVs, screenshots of dashboards, or links to anonymized case studies. If you prefer, describe a failure you uncovered (e.g., “training completions up but no revenue uplift — here’s how we tracked attribution and what we changed”).

Community asks: who can contribute a working ROI calculator, a minimal skill-to-revenue schema, or a reproducible experiment design? If you’ve vendor integrations or open-source models, list them and the data fields they require. Let’s build a shared playbook that HR/L&D, ops and analytics teams can reuse to turn skills visibility into measurable financial outcomes.