Analytics Dashboard
AI Social Insights
Qualitative intelligence for topics, sentiment, search intent, and AI research—convert community conversations into actionable product, support, and content decisions.
This dashboard augments quantitative usage analytics with qualitative insight—surfacing what people talk about, how they feel, what they search (but cannot find), and why churn or satisfaction shifts are emerging.
Topic Mentions
Volume & share of conversation
Sentiment
Emotional direction & skew
AI Summaries
Condensed thematic reasoning
Recommendations
Actionable remediation & leverage
AI Research
Ad‑hoc natural language analysis
Search Intent & Gaps
Unmet demand & content prioritization
What Can You Answer in Minutes?
Which features generate praise vs friction and their emerging pain themes.
Daily Operational Workflow
1
Open & Filter
Select date range, locale, platform/app segment, and (optionally) user cohort (new vs returning) to focus analysis.
2
Scan Topic Shifts
Compare Top Mentions list vs prior period % change to flag emerging themes.
3
Assess Sentiment
Check Overall Sentiment gauge; drill into topics with highest negative skew.
4
Read AI Summaries
Skim negative → neutral → positive summaries to capture root causes & delights.
5
Review Recommendations
Validate AI suggestions; convert high-confidence items into backlog tickets (label with source tag).
6
Run Research Query
Pose a targeted natural language question for deeper synthesis (e.g., “Main complaints about video uploads this week”).
7
Evaluate Search Gaps
Open Search Insights tab; prioritize new content for highest Gap Index terms.
8
Log Actions
Record chosen remediations with owner & ETA; schedule follow-up check next cycle.
Community & Topic Filtering
Refine any insight to the communities and topics that matter—filtering removes noise so trend, sentiment, and gap signals stay actionable.
Topic Filter
Focus on specific themes
Community Filter
Isolate audience segments
Combined Scope
Intersect topic + community for precision
Why Use Community-Level Filters?
- Targeted insights: Each community exhibits distinct engagement & sentiment patterns.
- Faster diagnosis: Narrow scope to confirm whether an issue is broad or localized.
- Segment strategies: Tailor interventions (content, moderation, onboarding) per cohort.
- Reduce noise: Avoid dilution from mega-communities overshadowing smaller niches.
Getting Started
| Requirement | Details |
|---|---|
| Access | Portal login with Social Insights permission |
| Feature Enablement | Social Insights enabled (contact support if disabled) |
| Data Window | Default range (e.g., last 30 days)—adjust as needed |
Need Social Insights enabled? Email [email protected] with your tenant ID.
Interface Tour
| Element | Location | Purpose |
|---|---|---|
| Filter Bar | Top of insights view | Houses Topic & Community selectors + date range |
| Topic Dropdown | Left selector | Choose one or multiple topics to include |
| Community Dropdown | Right selector | Search & pick one or more communities |
| Date Range | Right side | Adjust temporal scope for comparison |
Default View
Default View
All communities & topics aggregated (e.g., total mentions count across full corpus) until any filter is applied.
Progressive Narrowing
Progressive Narrowing
Apply topic first to isolate theme, then add community filter to test audience-specific variance.
Reset Strategy
Reset Strategy
If results feel sparse, clear the most recently added filter first to widen context.
Applying Filters (Step-by-Step)
1
Open Topic Menu
Click Topic: Select topic; search or scroll the list.
2
Select Topic(s)
Choose one (start simple) or multi-select to form a thematic cluster.
3
Observe Metrics
Note changes in Total Mentions, sentiment bars, emerging topics list.
4
Add Community Filter
Open Community: Select community; search e.g. “Fashion & Style”; select.
5
Compare Before/After
Is sentiment skew or gap profile materially different? Capture delta.
6
Iterate
Test additional communities to spot outliers; avoid over-filtering below statistically meaningful volume.
Interpreting Filtered Results
If filtered mentions fall below reliability threshold (internal baseline), treat insights as directional only.
Best Practice Patterns
Sequential Filtering
Sequential Filtering
Apply topic FIRST to anchor analysis, then layer community to avoid premature fragmentation.
Benchmark Preservation
Benchmark Preservation
Keep an unfiltered baseline tab open for quick relative comparisons.
Volume Guardrail
Volume Guardrail
Set a minimum mentions threshold (e.g., 300) for acting on sentiment shifts.
Change Logging
Change Logging
Record filter combinations used when generating recommendations to ensure reproducibility.
Periodic Reset
Periodic Reset
Once per week, review global view to avoid tunnel vision from niche segments.
Example Workflow
- Start unfiltered → identify emerging topic.
- Apply topic filter → confirm growth & sentiment skew.
- Add top 3 related communities → detect which exhibits worst negative skew.
- Run AI Research prompt: “Root causes of negative sentiment about <topic> in <community>.”
- Validate references → create targeted remediation ticket.
Combining topic + community filters early in investigation accelerates root cause isolation but always validate sample size before action.
Modules & Interpretation
Mentions & Topic Breakdown
Highlights conversation concentration. A healthy distribution usually shows a balanced long-tail—extreme concentration may indicate a blocking issue OR a successful campaign.Trend Graph
Multi-line frequency lines reveal acceleration or decay. Sustained upward slope + positive sentiment → amplify; upward + negative → triage.Sentiment (Overall + Per Topic)
Overall gauge (0–100). Correlate dips with deployment timestamps or incident reports. Topic sentiment bars isolate outlier issues masked by aggregate positivity.AI Post Summaries
Condense thousands of posts into 5–10 key statements per sentiment polarity. Treat as directional—spot check references before broad decisions.AI Recommendations
Strategic remediation / opportunity list derived from the weighted intersection of volume, sentiment skew, and recency. Confidence score (if shown) reflects model certainty; low-confidence items may require manual verification.AI Research (Conversational Analyst)
Natural language Q&A across indexed conversations. Returns a structured mini-report: Introduction, Methodology, Findings, References (canonical post links) to enable traceability.Search Insights & Content Gaps
Maps expressed intent (search queries) vs existing content coverage. Gap Index ranks unmet demand to drive documentation & self-service improvements.Metrics & Signals
| Metric | Definition | Why It Matters | Action Trigger |
|---|---|---|---|
| Total Mentions | Total topic references in filtered period | Measures conversation volume / engagement energy | Spike > X% without planned campaign |
| Topic Share % | Mentions of topic / total mentions | Identifies dominance or neglect | Single topic >40% share 2+ days |
| Emerging Topic | Topic with high period-over-period growth and baseline low prior volume | Surfacing new opportunities or issues | Growth rate > threshold (e.g., >150%) |
| Overall Sentiment Score | Normalized weighted sentiment across posts (0–100) | Health & morale indicator | Drop of >5 pts d/d or >8 pts w/w |
| Topic Sentiment Skew | Deviation between topic sentiment and overall baseline | Prioritizes hotspots | Negative skew > (baseline -10) |
| Negative Summary Themes Count | Distinct negative themes extracted | Complexity of issues / support load | Rapid increase in count |
| Total Searches | Volume of internal search queries | Demand / intent scale | Sustained decline (possible UX findability issue) |
| Trending Searches Count | # of search terms with rapid growth | Emerging informational needs | Large increase → accelerate content creation |
| Content Gap Count | Unique queries with low/no results | Self-service deficiency | > predetermined target |
| Gap Index | Weighted (query volume vs results available) | Prioritization of content production | Top index unchanged across cycles (stale backlog) |
| Zero-Result Rate % | (Queries with no result) / total searches | Support deflection efficiency | Rate > target (e.g., >8%) |
Leveraging AI Research Effectively
”Why did sentiment drop after release 5.2?"
Prompt Patterns
Comparison
Comparison
”Compare sentiment for video uploads vs live streaming this month."
Root Cause
Root Cause
"Summarize primary reasons behind negative sentiment about notifications."
Cohort Split
Cohort Split
"Differences in topics between new and returning users last 14 days."
Temporal Shift
Temporal Shift
"What changed in top 5 topics compared to previous week?"
Opportunity
Opportunity
"Identify feature improvement opportunities mentioned positively but low volume.”
Related
Activity Analytics
Quantitative usage metrics
Raw Data Export
Deeper custom analysis
User History
Drill into individual behavior
Webhooks & Events
Data integration sources
Need advanced taxonomy tuning, extended retention windows, or custom sentiment domains? Contact support for enhanced AI configuration options.