Analytics Dashboard
Chat Analytics
Operational insight into messaging volume, participation, channel health, quality, and moderation load to optimize chat retention and scalability.
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
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
| Metric | Definition | Formula (Illustrative) | Why It Matters | Action Trigger |
|---|---|---|---|---|
| Messages | Total messages sent | count(messages) | Overall volume | Drop >15% w/w |
| Unique Senders | Distinct users sending ≥1 message | distinct(user_id) | Participation breadth | Flat while messages rise |
| Messages per Active User (MPAU) | Avg intensity per sender | Messages / Unique Senders | Detect over-reliance | Sharp rise + flat senders |
| Reactions | Total reactions on messages | count(reactions) | Lightweight engagement | Reactions/Message down |
| Reaction Rate | Reactions per message | Reactions / Messages | Content resonance | Below baseline band |
| Flagged Messages | Messages flagged by systems/users | count(flag events) | Moderation workload | Spike >25% w/w |
| Flagged Rate | Flagged / Messages | Flagged / Messages | Safety cleanliness | > threshold (e.g., 1–2%) |
| New Channels | Channels created period | count(channel_create) | Organic growth / fragmentation | Spike + falling engagement rate |
| Channel Concentration Index | Engagement distribution (simplified HHI) | Σ (channel_share^2) | Diversity of conversation | > prior period + rising churn |
| Messages per Channel | Avg messages per active channel | Messages / active_channels | Capacity & sprawl measure | Decline + rising channel count |
| Peak Hour Factor | Peak hour messages vs avg hour | peak_hour / avg_hour | Staffing & infra planning | Factor rising > target |
| Newly Activated Chat Users | First-time senders in period | distinct(new_sender_ids) | Feature adoption | Activation drop consecutive weeks |
| Retained New Senders (7d) | New senders returning within 7d | returning_new / new_sender_ids | Early retention | < benchmark band |
Participation & Quality Lenses
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
MPAU rises, Unique Senders flat.
Troubleshooting
| Symptom | Likely Cause | Investigation | Resolution |
|---|---|---|---|
| Volume up; engagement flat | Few users posting heavily | Distribution of messages per user | Encourage broader participation; prompts, onboarding nudge |
| Flagged rate surge | Spam / abusive burst | Review top flagged channels & user IDs | Apply rate limits; tighten filters; temp-ban offenders |
| Reaction rate drop | Content low relevance / UI friction | Compare reaction latency post message | Improve quick reaction UI; highlight engaging threads |
| New channels spike; avg messages/channel down | Fragmentation / channel creation sprawl | Correlate channel age vs activity | Introduce channel creation guidelines & approval |
| Moderation backlog aging | Understaffed peak or automation gap | Backlog aging metric by hour | Add on-call; refine auto-flag thresholds |
| Low new sender retention | Poor onboarding into existing channels | Funnel: Signup → First message → Return visit | Provide channel suggestions & starter messages |
| Peak hour factor rising | Emerging synchronized usage cluster | Compare heatmap vs previous weeks | Pre-scale infra; adjust sharding or throughput configs |
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.