Use Chat Analytics to detect participation shifts, capacity risks, emerging spam, and moderation workload so you can sustain healthy real‑time conversations.
Message Volume
Total & trend
Participation
Unique senders & activation
Channel Health
Distribution & concentration
Engagement Quality
Reactions & replies depth
Moderation Load
Flagged rate & review pressure
Temporal Patterns
Daily & hourly peaks
Fast Questions You Can Answer
- Is growth broad?
- Quality dip?
- Channel sprawl?
- Safety load?
- Capacity risk?
- Activation health?
Are more unique users sending messages vs just power users ramping?
Daily Operational Workflow
1
Apply Filters
Pick date range (e.g., last 7 / 30 days), segment (environment, region), and exclude internal test channels.
2
Scan KPI Cards
Messages, Flagged Messages, Reactions, Unique Senders—compare % delta vs prior period.
3
Review Volume Trend
Open New Messages by Day to detect bursts or weekend/weekday abnormalities.
4
Check Participation Breadth
Contrast Users by Day with Messages—if messages up but users flat, investigate power user over-reliance.
5
Channel Distribution
Check Channels Count by Type and Messages by Channel for concentration or underutilized types.
6
Moderation Pressure
Flagged Messages & Flagged Rate vs moderation SLA; queue backlog may require staffing shift.
7
Temporal Hotspots
Use Heatmap to validate moderator coverage during peak hours.
8
Drill Power / Risk Users
Use Users table to inspect top senders & high flagged contributors.
9
Log Actions
Document interventions (rate limits, highlights, education) and set follow-up date.
Modules & Interpretation
KPI Cards
High-level snapshot (Messages, Flagged Messages, Reactions, Unique Senders). Deltas contextualize growth vs prior period.New Messages by Day
Volume rhythm; use for feature launch impact and anomaly detection. Gentle cyclic pattern is normal; abrupt plateau indicates engagement stall.Channels Count by Type
Composition (public, private, broadcast). Skew heavily toward one type may limit discovery or create moderation blind spots.Users by Day
Active unique senders; rising volume without matching unique sender growth implies intensity, not breadth.Total Messages by Type / Channel Distribution
Identifies reliance on a small set of channels. Over-concentration risks single-point community health issues.Top Group Chat Channels Leaderboard
Sort by Members, Messages, Engagement Rate (messages per member), or Flagged Rate to surface exemplars or risk clusters.Messages Heatmap
Hourly/daily density. Align moderator shifts and system scaling policies with dark (peak) cells.Key Metrics & Formulas
Participation & Quality Lenses
- Acquisition
- Activation
- Retention
- Quality
- Safety
- Load & Capacity
Growth in newly activated chat users.
Benchmark & Governance Strategy
Baseline Collection
Baseline Collection
Capture 30 rolling days after stable launch; derive median & IQR for each core metric.
Adaptive Thresholds
Adaptive Thresholds
Alert if metric leaves median ±1.5 * IQR (robust vs outliers).
Cohort Drift
Cohort Drift
Track new vs established communities; avoid mixing maturity profiles.
Quality Gate
Quality Gate
Require Reaction Rate & Retained New Senders above thresholds before promoting channels.
Moderation SLA
Moderation SLA
Define max backlog aging (e.g., 2h). Breach triggers escalation & auto-priority ordering.
Early Warning Signals
- Power User Dependence
- Fragmentation
- Spam / Flood
- Moderation Overload
- Quality Decline
- Adoption Stall
MPAU rises, Unique Senders flat.
Troubleshooting
Related
Activity Analytics
User activity baseline
Social Analytics
Community performance
AI Social Insights
Qualitative topics & sentiment
Raw Data Export
Custom message logs
Need custom spam detection dashboards or extended retention windows? Contact support for advanced analytics enablement.