Ca community monitor

Community Monitor

Tracks customer engagement in community channels -- forums, Slack communities, user groups, and peer-to-peer platforms. Surfaces actionable signals for CS and identifies advocates from activity patterns. Part of the Customer Advocacy & Community pillar.

This is a monitoring and signal detection skill. It observes community activity and surfaces patterns. Community management strategy, moderation, and direct engagement are human-led.

Applicability note: This skill is only relevant if the product maintains a customer community (forum, Slack workspace, user group, etc.). If no community exists, this skill is not applicable and should not be activated.

When to Run

• Continuous: Monitors community channels for signals in real-time or near-real-time
• Scheduled: Weekly community health report
• On-demand: When a CSM requests community engagement data for a specific account

Core Execution Logic

Step 1: Track Activity per Account and Contact

For each customer contact active in the community, monitor:

Metric	Definition	Why It Matters
Post frequency	Posts and comments per 30-day period	Engagement level
Response rate	Replies to other customers' questions	Indicator of advocacy behaviour
Topic engagement	Categories or channels they participate in	Shows what they care about
Sentiment	Positive, neutral, or negative tone per post	Early warning for frustration or strong advocacy
Help ratio	Answers given / questions asked	High ratio = potential advocate
Recency	Days since last community activity	Engagement trend

Link community members to CRM accounts using email domain or explicit account mapping.

Step 2: Detect Actionable Signals

Signal	Detection Criteria	Urgency	Routing
Unanswered question	Post from a customer with no reply after 48 hours	High	Alert CSM or community manager -- customer waiting for help in public
Negative sentiment post	Frustrated, critical, or angry language detected	High	Alert CSM -- public frustration needs attention before it influences other customers
Competitor mention	Evaluation or comparison language referencing a competitor	High	Route to bi-competitive-intel as a community-sourced signal
Feature discussion thread	Multiple customers discussing a missing capability	Medium	Route to pa-feedback-aggregator as community-validated demand
High-value contributor emerging	Customer contact answers 5+ questions in 30 days	Medium	Flag for ca-advocacy-tracker as potential advocate
New community member	Customer contact joins community for the first time	Low	Log engagement signal in ra-engagement-tracker
Sudden silence	Previously active member has no activity in 30+ days	Low	Log for CSM awareness -- may correlate with disengagement from the product
Positive testimonial	Enthusiastic praise or recommendation in a public post	Low	Flag for ca-advocacy-tracker -- potential testimonial or case study material

Step 3: Identify Community Advocates

Customers who actively help other customers are the highest-quality advocates:

Advocate scoring:

Factor	Weight	Measurement
Help ratio (answers/questions)	30%	Ratio >2:1 indicates a helper, not just a participant
Response quality	25%	Posts that receive "helpful" votes or positive replies
Consistency	20%	Active for 3+ consecutive months (not a one-time burst)
Reach	15%	Posts that receive 5+ views or 3+ replies (indicating influence)
Sentiment	10%	Consistently positive tone (advocates must be genuine, not compensated)

Advocate tiers:

• Power advocate (top 5% of community contributors): Candidate for all advocacy types -- references, case studies, events, advisory board
• Active contributor (top 15%): Candidate for references and reviews
• Participant (active but not contributing): Not yet an advocate. Nurture through engagement

Step 4: Generate Community Health Report

Weekly report for CS leadership and community management:

{
  "report_date": "2026-03-10",
  "community_health": {
    "active_members_30d": 142,
    "new_members_30d": 18,
    "churned_members_30d": 4,
    "total_posts_30d": 234,
    "total_replies_30d": 412,
    "questions_asked_30d": 67,
    "questions_answered_30d": 61,
    "unanswered_questions": 6,
    "avg_response_time_hours": 6.2,
    "sentiment_distribution": {
      "positive": 0.62,
      "neutral": 0.31,
      "negative": 0.07
    },
    "trending_topics": ["API rate limits", "Custom reporting", "Q2 release preview"]
  },
  "signals": [
    {
      "type": "unanswered_question",
      "account": "Beta Inc",
      "contact": "Lisa Park",
      "post_summary": "Question about bulk import performance with large datasets",
      "hours_waiting": 52,
      "urgency": "high",
      "recommended_action": "Community manager or CSM respond. Customer waiting publicly"
    },
    {
      "type": "negative_sentiment",
      "account": "Gamma Corp",
      "contact": "Mike Chen",
      "post_summary": "Frustrated with reporting limitations. Third post on this topic in 2 weeks",
      "urgency": "high",
      "recommended_action": "CSM outreach privately. Do not let frustration build in public channel"
    },
    {
      "type": "competitor_mention",
      "account": "Delta Ltd",
      "contact": "Sarah Kim",
      "post_summary": "Asked if anyone has compared our reporting to Competitor X",
      "urgency": "medium",
      "recommended_action": "Route to bi-competitive-intel. CSM assess whether this is casual curiosity or active evaluation"
    }
  ],
  "advocates": {
    "power_advocates": [
      {
        "contact": "Tom Chen",
        "account": "Acme Corp",
        "posts_30d": 18,
        "helpful_replies": 12,
        "help_ratio": 3.0,
        "months_active": 8,
        "advocate_score": 92,
        "already_in_advocacy_programme": true
      }
    ],
    "emerging_advocates": [
      {
        "contact": "Anna Lee",
        "account": "Echo Systems",
        "posts_30d": 8,
        "helpful_replies": 5,
        "help_ratio": 2.5,
        "months_active": 3,
        "advocate_score": 71,
        "recommendation": "Approaching power advocate status. CSM should acknowledge their community contributions"
      }
    ]
  },
  "period_over_period": {
    "active_members_change": "+6%",
    "response_time_change": "-0.8 hours (improved)",
    "sentiment_trend": "stable",
    "unanswered_questions_change": "-2 (improved)"
  }
}

Handoff to Human

Decision Point	Context Provided	What the Human Decides
Unanswered question response	Question content, customer account, waiting time	Whether the community manager, CSM, or product team should respond, and what the response should be
Negative sentiment response	Post content, customer context, prior posts	Whether to respond publicly, reach out privately, or monitor. Public responses must be handled carefully
Advocate development	Advocate scores, contribution history, account health	Whether to formally recognise the advocate, invite them into the advocacy programme, or let organic contribution continue
Competitive mention follow-up	Post content, customer account, evaluation indicators	Whether this is casual or signals active evaluation. Determines whether bi-competitive-intel escalation is warranted
Community strategy	Health metrics, trends, topic analysis	What content to create, which discussions to seed, how to grow engagement. Strategic decisions are human territory

Confidence and Limitations

• High confidence for activity tracking and unanswered question detection -- quantitative metrics from the community platform are objective
• High confidence for advocate identification from activity patterns -- help ratio and consistency are measurable
• Medium confidence for sentiment analysis -- community posts can be ambiguous, sarcastic, or context-dependent. NLP sentiment detection is a starting point; human review is needed for high-stakes posts
• Medium confidence for competitor mention detection -- depends on keyword registry quality (shared with bi-competitive-intel)
• Low confidence for interpreting advocate motivation. High posting frequency could indicate enthusiasm (genuine advocate) or frustration (frequent poster because they keep encountering issues). The help ratio helps distinguish, but the CSM must validate
• Only applicable if a customer community exists. Many B2B SaaS products do not maintain one
• Cannot moderate content or enforce community guidelines -- that is human and platform responsibility
• Cannot attribute community engagement to retention outcomes with statistical confidence -- correlation exists but causation is difficult to prove
• Community signals are public. A frustrated post is visible to all customers and prospects. Response urgency is higher than for private feedback channels

Dependencies

Required:

• Community platform API (forum, Slack workspace, or equivalent)
• CRM API (to link community members to customer accounts)

Strongly recommended:

• bi-competitive-intel (for routing competitive mentions)
• pa-feedback-aggregator (for routing feature discussion threads)
• ca-advocacy-tracker (for advocate pipeline management)
• ra-engagement-tracker (for logging community engagement as a touchpoint channel)

Downstream consumers:

• ca-advocacy-tracker (advocate candidates)
• bi-competitive-intel (competitive signals from community)
• pa-feedback-aggregator (feature feedback from community discussions)
• CS leadership (weekly community health report)

References

• references/community-signals.md -- Signal definitions, detection criteria, and routing rules