Heuristic Evaluation

Every conversation is customer research. Extract structured UX and CX insights from social discussions using CrowdListen audience intelligence.

Consolidates: voice-of-customer, audience-discovery (persona generation, community mapping)

Before You Start

Ask your human for business context — this skill produces significantly better output when grounded in specifics:

• Target market: Who are the customers? What industry/segment?
• Key competitors: Which brands or products to compare against?
• Constraints: Budget, timeline, geographic focus?
• Decision context: What decisions will this analysis inform? (roadmap, funding, positioning, hiring?)
• Existing data: Any prior research, internal metrics, or hypotheses to validate?

When to Use This Skill

• Customer experience analysis and improvement
• Journey friction detection and optimization
• Feedback clustering and pain point mapping
• Usability signal detection from organic discussions
• Support strategy and ticket deflection
• Customer health monitoring and churn prevention
• Identifying and profiling target audience segments

Foundation: CrowdListen Tools

This skill builds on CrowdListen's core capabilities:

• search_content — Find customer discussions and feedback
• analyze_content — Extract sentiment and themes from feedback
• cluster_opinions — Group feedback into actionable categories
• get_content_comments — Mine comment threads for detailed feedback
• sentiment_evolution_tracker — Track satisfaction trends over time
• expert_identification — Find key opinion leaders in communities

Workflows

1. Journey Friction Detection

Identify where in the customer journey users experience friction.

Process:

1. Search for stage-specific frustration signals:
   • Discovery: "how do I find", "confusing options"
   • Onboarding: "setup", "getting started", "first time"
   • Core usage: "every time I try to", "should be easier"
   • Expansion: "upgrade", "pricing", "worth paying for"
   • Renewal/Churn: "canceling", "alternative", "leaving"
2. Map friction points to journey stages
3. Score by impact on conversion/retention

Output Template:

## Journey Friction Map

| Stage | Friction Points | Severity | Volume | Impact |
|-------|----------------|----------|--------|--------|
| Discovery | [friction] | [H/M/L] | [count] | [conversion loss est.] |
| Onboarding | [friction] | [H/M/L] | [count] | [activation loss est.] |
| Core Usage | [friction] | [H/M/L] | [count] | [engagement loss est.] |

### Biggest Friction: [Stage — Problem]
**What users say**: [synthesis with quotes]
**Why it matters**: [business impact]
**Recommended fix**: [specific action]

2. Feedback Clustering

Group unstructured customer feedback into actionable themes.

Process:

1. Search for product mentions with sentiment indicators
2. Use cluster_opinions to group by root theme (not surface keywords)
3. Score each cluster by: volume, severity, trend direction
4. Map clusters to product areas and responsible teams

Output Template:

## Feedback Clusters — [Period]

### Cluster 1: [Theme Name] (Severity: Critical)
- **Volume**: X mentions across Y platforms
- **Trend**: [rising/stable/declining] over [period]
- **Product area**: [Onboarding / Core UX / Pricing / Performance / Support]
- **Representative quotes**:
  > "[Quote 1]" — [platform, engagement count]
- **Action**: [Specific recommendation]

### Summary Table
| Theme | Volume | Severity | Trend | Owner | Status |
|-------|--------|----------|-------|-------|--------|
| [Theme] | [count] | [Crit/Imp/Mon] | [arrow] | [team] | [New/Known/WIP] |

3. Pain Point Mapping

Cluster user frustrations into addressable product problems.

Process:

1. Search for negative sentiment discussions about your product/category
2. Use cluster_opinions to group by root cause, not symptom
3. Map clusters to product areas (onboarding, core UX, pricing, performance, etc.)

Output Template:

## Pain Point Map

### Critical (High frequency + High severity)
**[Pain Point Cluster Name]**
- Affected area: [Product area]
- Frequency: [X mentions/week]
- Severity: [Users leaving / Users complaining / Users working around]
- Root cause hypothesis: [Why this happens]
- Example quotes:
  > "[quote]" — [platform, date]

### Important (High frequency OR High severity)
[Same structure]

4. Data-Driven Persona Generation

Create personas from real social discussions, not assumptions.

Process:

1. Search for your product category across platforms
2. Analyze discussants' language, concerns, goals, and context clues
3. Cluster into distinct persona groups by behavior patterns
4. Validate against engagement patterns

Output Template:

## Audience Personas — [Category]

### Persona 1: [Name] — "[One-line description]"
**Archetype**: [Role/behavior archetype]
**Estimated segment size**: [% of audience]

**Goals**:
1. [Primary goal with evidence from social discussions]

**Pain Points**:
1. [Frustration with evidence]

**Behavior Patterns**:
- Platforms: [Where they're most active]
- Content preference: [What they engage with]
- Decision process: [How they evaluate products]

**Representative Quote**:
> "[Actual quote from social discussions]"

5. Churn Risk Signals

Detect early warning signs that customers may leave.

Process:

1. Search for switching discussions, alternative evaluations, frustration escalation
2. Identify churn precursor patterns
3. Score current churn risk level based on signal volume and trend

Output Template:

## Churn Risk Dashboard — [Period]

**Overall Risk Level**: [Low / Moderate / Elevated / High]

### Active Churn Signals
| Signal Type | Volume | Trend | Severity |
|-------------|--------|-------|----------|
| Competitor comparison shopping | [count] | [up/down] | [H/M/L] |
| "Alternative to [product]" searches | [count] | [up/down] | [H/M/L] |

### Retention Recommendations
- **Quick win**: [action that could immediately reduce churn risk]
- **Strategic fix**: [larger initiative to address root cause]

Integration with CrowdListen

This skill enhances CrowdListen analyses by:

• Providing structured UX/CX reports from unstructured social data
• Detecting journey friction before it shows in retention metrics
• Creating research-backed audience profiles from real social data
• Connecting social sentiment to customer journey stages
• Enabling continuous customer health monitoring without surveys