Ra Engagement Tracker

Engagement Tracker

Tracks touchpoint frequency and recency across all communication channels for every stakeholder. Computes engagement health per contact and per account. Part of the Relationship Architecture pillar.

This is a measurement and alerting skill. It tracks frequency -- how often and how recently have you engaged. It does not assess quality -- a weekly call where nothing meaningful is discussed scores the same as a weekly call that transforms the relationship. Frequency is necessary for healthy relationships; it is not sufficient. The CSM provides the quality assessment.

When to Run

• Continuous: Logs touchpoints as they occur from connected channels
• Scheduled: Weekly engagement health computation and gap detection
• On-demand: When a CSM requests engagement data for a specific account or contact

Core Execution Logic

Step 1: Log Touchpoints from All Channels

Every customer interaction is a touchpoint. Capture from all available sources:

For each touchpoint, log:

• Timestamp
• Channel
• Direction: inbound (customer-initiated) or outbound (CSM-initiated)
• Contact(s) involved
• Type: scheduled (planned touchpoint) or unscheduled (ad hoc)

Step 2: Compute Per-Contact Engagement Metrics

For each contact in ra-stakeholder-mapper:

Step 3: Classify Engagement Health

For each contact, compare engagement metrics against the expected cadence for their stakeholder type (from ra-stakeholder-mapper):

Step 4: Compute Per-Account Engagement Health

Aggregate contact-level health into an account-level engagement score:

Weighted by stakeholder type:

• Champion engagement: 35% weight (the most important relationship)
• Economic Buyer engagement: 25% weight
• Executive Sponsor engagement: 15% weight
• Technical Lead engagement: 15% weight
• All other contacts: 10% weight

Account engagement score = weighted sum of contact engagement scores (each contact scored 0-100 based on health level: Strong=90, Adequate=70, Declining=45, Gap=20, Lost=0)

Account engagement classification:

Step 5: Detect Patterns and Generate Alerts

Step 6: Generate Engagement Report

Per-CSM weekly engagement summary:

{
  "csm": "Jane Doe",
  "report_date": "2026-03-10",
  "portfolio_engagement": {
    "total_accounts": 42,
    "engagement_distribution": {
      "healthy": 28,
      "adequate": 8,
      "declining": 4,
      "critical": 2
    },
    "avg_engagement_score": 72
  },
  "alerts_this_week": [
    {
      "type": "no_reply_streak",
      "account": "Gamma Corp",
      "contact": "Lisa Park (VP Eng)",
      "detail": "3 outbound emails in 14 days with no response",
      "recommended_action": "Try a different channel (call) or approach through another contact"
    },
    {
      "type": "champion_cooling",
      "account": "Beta Inc",
      "contact": "Tom Chen (Champion)",
      "detail": "Engagement moved from Strong to Declining. Touchpoint frequency down from every 10 days to every 28 days over last 60 days",
      "recommended_action": "Investigate. Schedule a direct conversation. Do not add more emails to the no-reply streak"
    }
  ],
  "engagement_movers": {
    "improved": [
      { "account": "Acme Corp", "score_change": "+12", "driver": "Executive sponsor engaged for first time in 90 days" }
    ],
    "declined": [
      { "account": "Beta Inc", "score_change": "-15", "driver": "Champion engagement cooling" }
    ]
  },
  "cadence_compliance": {
    "touchpoints_expected": 38,
    "touchpoints_completed": 34,
    "compliance_rate": 0.89,
    "overdue_touchpoints": [
      { "account": "Delta Ltd", "contact": "Sarah Kim", "expected_cadence": "monthly", "days_overdue": 12 }
    ]
  }
}

Handoff to Human

Confidence and Limitations

• High confidence for touchpoint logging and frequency computation -- structured event data from connected systems
• High confidence for engagement health classification -- deterministic comparison of metrics to defined thresholds
• Medium confidence for pattern detection (no-reply streaks, cancellation patterns) -- these are reliable signals but have multiple possible interpretations
• Medium confidence for inbound ratio as an engagement quality proxy -- a declining inbound ratio is a meaningful signal, but some customers are naturally passive communicators. Benchmark against the contact's own historical pattern, not a universal standard
• Low confidence for engagement quality assessment. This is the fundamental limitation: the skill tracks frequency, not depth. A 15-minute status call and a 2-hour strategic planning session count the same. The CSM provides the quality layer
• Cannot track engagement that happens outside connected systems (in-person meetings, conferences, informal conversations, personal phone calls)
• "No inbound engagement" is a strong disengagement signal in most cases, but some customers only engage reactively and have always done so. The baseline matters more than the absolute number

Dependencies

Required:

• CRM API (activity data, contact records)
• Calendar integration (meeting data)
• Email integration (email activity data)

Strongly recommended:

• ra-stakeholder-mapper (stakeholder types and expected cadence for health computation)
• Call platform integration (call data)
• Support platform (support interaction data)
• ca-community-monitor (community engagement channel)

Downstream consumers:

• bi-health-score (engagement data feeds the engagement component -- 25% default weight)
• ra-stakeholder-mapper (engagement status per contact)
• bi-risk-detector (declining engagement as a risk signal)
• lo-check-in-scheduler (cadence compliance data)
• cc-report-generator (engagement metrics for CSM and manager reports)

References

• references/engagement-methodology.md -- Channel definitions, health classification thresholds, and cadence baseline calibration