Community Health Metrics Dashboard Interpretation

What it tests

Data literacy in a community context, ability to distinguish signal from noise in engagement metrics, and quality of strategic recommendations derived from quantitative data

Format

1. 1Candidate receives a one-page community health dashboard — mock data covering 90 days of activity: DAU/MAU, thread response rates, member churn, top contributors, content engagement by type, and support deflection rates
2. 215 minutes to review the data silently and prepare a verbal briefing
3. 320-minute verbal briefing: candidate presents their interpretation — what's healthy, what's a red flag, what's a lagging indicator masking a leading problem
4. 4Final 10 minutes: interviewer reveals one hidden context clue ('By the way, the product had a major outage in week 6') and asks how that changes their interpretation

What to look for

• Do they read past surface-level numbers — do they notice that high post volume with low reply rates signals a 'broadcast community' not an engaged one?
• Can they identify lagging vs. leading indicators — e.g., member churn is a lagging signal; drop in first-post-to-second-post rate is the leading one
• When given the hidden context clue, do they recalibrate intelligently or just absorb it without revising their diagnosis?
• Do their recommendations connect community metrics to business outcomes, or stay purely within community KPIs?

Adaptation guide

Use real (anonymized) data from your community platform — candidates who can interpret your specific data structure show immediate practical value. Add a second dataset from a comparable competitor community to test benchmarking instincts.

Full description