Community Health Scoring

This drill maintains a living scorecard for every community in your engagement portfolio. It detects communities losing ROI, surfaces emerging high-potential communities, and recommends how to reallocate engagement effort. Runs on a weekly automated cadence.

Input

• Community target list in Attio (from community-reconnaissance drill)
• PostHog tracking events flowing (from posthog-gtm-events drill)
• Activity log of community interactions (from community-engagement-tracking fundamental)
• At least 4 weeks of engagement data per community

Steps

1. Define the health score formula

Each community gets a composite score (0-100) computed weekly from five weighted signals:

| Signal | Weight | Measurement | Source | |--------|--------|-------------|--------| | Referral volume | 30% | Unique sessions from this community in the last 7 days | PostHog community_referral_visit events filtered by source property | | Conversion rate | 25% | Signups / referral sessions for this community | PostHog funnel: community_referral_visit -> signup_completed filtered by community | | Engagement efficiency | 20% | (Referral sessions + signups) / hours invested in this community | Activity log hours vs PostHog referral data | | Engagement trend | 15% | Week-over-week change in referral sessions (positive = growing, negative = declining) | PostHog trend query comparing last 7 days to prior 7 days | | Pipeline attribution | 10% | Deals in Attio where lead_source_detail matches this community | Attio API query filtered by community name |

2. Build the scoring n8n workflow

Using the n8n-scheduling fundamental, create a workflow that runs every Monday at 6am:

Schedule Trigger (weekly, Monday 6am)
  -> PostHog HTTP Request Node:
     Query: community_referral_visit events, last 7 days, grouped by source property
     Return: {community: string, sessions: number}[]
  -> PostHog HTTP Request Node:
     Query: community_signup events, last 7 days, grouped by source property
     Return: {community: string, signups: number}[]
  -> PostHog HTTP Request Node:
     Query: community_referral_visit trend, last 7 days vs prior 7 days, grouped by source
     Return: {community: string, current_week: number, prior_week: number}[]
  -> Attio HTTP Request Node:
     Query: Deals where lead_source_detail matches community names, created in last 30 days
     Return: {community: string, deal_count: number, pipeline_value: number}[]
  -> Function Node (compute scores):
     For each community:
       referral_score = min(sessions / target_sessions * 100, 100) * 0.30
       conversion_score = min(conversion_rate / target_rate * 100, 100) * 0.25
       efficiency_score = min(efficiency / target_efficiency * 100, 100) * 0.20
       trend_score = ((current_week - prior_week) / max(prior_week, 1) + 1) * 50 * 0.15
       pipeline_score = min(deal_count / target_deals * 100, 100) * 0.10
       total = referral_score + conversion_score + efficiency_score + trend_score + pipeline_score
     Classify:
       80-100: "thriving" — increase investment
       60-79: "healthy" — maintain current effort
       40-59: "watch" — investigate, may need strategy change
       20-39: "declining" — reduce effort, reallocate to higher-scoring communities
       0-19: "dormant" — pause engagement, archive from active list
  -> Attio Node: Update each community record with new score, classification, and component scores
  -> Slack Node: Post weekly community health report

3. Configure anomaly detection

Using the posthog-anomaly-detection fundamental, set alerts for:

• Referral spike: Any community's weekly referral sessions exceed 3x its 4-week average. Action: investigate what caused the spike and replicate it.
• Referral collapse: Any community's weekly referral sessions drop below 30% of its 4-week average. Action: check if the community is still active, if your account was restricted, or if a competitor entered.
• Conversion rate shift: Conversion rate for any community changes by more than 50% (up or down) vs 4-week average. Action: review the landing page experience and content quality for that community's traffic.

4. Generate the weekly health report

The n8n workflow produces a structured report posted to Slack and stored in Attio:

## Community Health Report — Week of {date}

### Portfolio Summary
- Active communities: {count}
- Total referral sessions this week: {total}
- Total signups this week: {total}
- Portfolio health score (weighted avg): {score}/100

### Top Performers
| Community | Score | Sessions | Signups | Trend |
|-----------|-------|----------|---------|-------|
| {name}    | {score} | {sessions} | {signups} | {trend%} |

### Declining Communities (action required)
| Community | Score | Issue | Recommended Action |
|-----------|-------|-------|--------------------|
| {name}    | {score} | {issue} | {action} |

### Emerging Opportunities
{Communities newly added or with rapidly improving scores}

### Reallocation Recommendation
- Increase effort: {list communities}
- Maintain effort: {list communities}
- Decrease effort: {list communities}
- Archive: {list communities}

5. Feed into autonomous optimization

The health scores and anomaly alerts from this drill feed directly into the autonomous-optimization drill at Durable level. When the optimization loop detects a metric anomaly, it uses community health data to generate targeted hypotheses — for example, "Community X's referral volume dropped 40%; hypothesis: our posting frequency decreased. Test: increase to 3 posts/week for 2 weeks."

Output

• Weekly health score for every active community (0-100 with classification)
• Automated Slack report with portfolio summary and reallocation recommendations
• Anomaly alerts for sudden changes in community performance
• Historical score data in Attio for trend analysis
• Input data for the autonomous-optimization drill's hypothesis generation

Triggers

• Automated: weekly (every Monday)
• Ad-hoc: when evaluating whether to add or drop a community
• Quarterly: full portfolio review with strategic reassessment