customer-research

Customer Research Skill

Deep customer understanding through structured research frameworks, powered by real customer data.

Purpose

Build comprehensive customer intelligence that informs product, marketing, and sales strategies.

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Required Integrations

Setup: Connect these data sources to enable full functionality. Claude will prompt you to connect any missing integrations when you use this skill.

Data Sources

# CUSTOMER RESEARCH DATA SOURCES
# Configure the sources relevant to your research needs

# Enterprise Search (searches across all internal sources)
- source: enterprise_search
  connector: "{{GLEAN | MOVEWORKS | ELASTIC}}"
  data:
    - internal_docs
    - wiki_content
    - shared_drives
    - slack_history

# CRM & Customer Data
- source: crm
  connector: "{{SALESFORCE | HUBSPOT}}"
  data:
    - customer_records
    - deal_history
    - win_loss_data
    - industry_segments
    - company_size_data
    - customer_lifecycle

# Product Usage & Analytics
- source: product_analytics
  connector: "{{MIXPANEL | AMPLITUDE | PENDO | HEAP}}"
  data:
    - user_behavior
    - feature_adoption
    - usage_patterns
    - cohort_analysis
    - retention_metrics

# Customer Feedback
- source: nps_surveys
  connector: "{{DELIGHTED | MEDALLIA | QUALTRICS | TYPEFORM}}"
  data:
    - nps_scores
    - survey_responses
    - feedback_themes
- source: review_sites
  sources:
    - g2
    - capterra
    - trustradius
  data:
    - customer_reviews
    - sentiment_analysis
    - competitive_mentions

# Conversation Intelligence
- source: call_recordings
  connector: "{{GONG | CHORUS | CLARI}}"
  data:
    - discovery_calls
    - win_loss_calls
    - objection_patterns
    - customer_language
    - competitive_mentions

# Support & Feedback
- source: support
  connector: "{{ZENDESK | INTERCOM | FRESHDESK}}"
  data:
    - ticket_themes
    - feature_requests
    - complaints
    - common_questions
- source: product_feedback
  connector: "{{PRODUCTBOARD | CANNY | USERVOICE}}"
  data:
    - feature_requests
    - voting_data
    - feedback_themes

# Customer Success
- source: customer_success
  connector: "{{GAINSIGHT | CHURNZERO | TOTANGO}}"
  data:
    - health_scores
    - churn_reasons
    - expansion_data
    - customer_segments

# Research Documents
- source: research_docs
  connector: "{{GOOGLE_DRIVE | SHAREPOINT | NOTION | CONFLUENCE}}"
  paths:
    - "/Research/Customer Interviews/"
    - "/Research/Survey Results/"
    - "/Research/Personas/"
    - "/Research/ICP Documentation/"

# Marketing Intelligence
- source: marketing_analytics
  connector: "{{GOOGLE_ANALYTICS | HUBSPOT | MARKETO}}"
  data:
    - conversion_paths
    - content_engagement
    - lead_sources
    - attribution_data

Output Destinations

# Where to deliver customer research outputs
outputs:
  # Always available - display in Claude UI
  - type: display
    enabled: true

  # Save research to knowledge base
  - type: documents
    connector: "{{GOOGLE_DRIVE | SHAREPOINT | NOTION | CONFLUENCE}}"
    destination: "/Research/Customer Insights/"

  # Update CRM with ICP/persona data
  - type: crm
    connector: "{{SALESFORCE | HUBSPOT}}"
    actions:
      - update_segment_definitions
      - add_persona_tags

  # Share insights with team
  - type: slack
    connector: "{{SLACK}}"
    channel: "#customer-insights"

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Instructions for Claude

IMPORTANT: Before executing this skill, you MUST validate the configuration above.

Pre-Execution Checklist

1. Check for placeholder values: Scan the YAML configuration for any {{...}} placeholders. These indicate required configuration that the user must provide.

2. Validate data sources: For each data source listed:

   • If a connector field shows {{OPTIONS}} format, ask the user which option they use
   • If URLs, paths, or names contain {{PLACEHOLDER}}, ask the user to provide actual values
   • Verify any required MCP servers are connected and available

3. Validate output destinations: For any output type beyond display:

   • Confirm the connector is available as an MCP server
   • Ensure destination paths/channels are configured (not placeholders)

If Configuration is Incomplete

Do not proceed with the skill. Instead:

1. List the specific missing or placeholder values found
2. Explain what each value is needed for
3. Ask the user to provide the missing configuration
4. Offer to help them set up the required MCP integrations

Example response when config is incomplete:

Before I can run this skill, I need some configuration:

**Missing values:**
- [List specific {{PLACEHOLDER}} values found]

**MCP connections needed:**
- [List required connectors not yet available]

Please provide these values, or let me know which data sources you'd like to skip.

Minimum Requirements

At minimum, this skill requires:

• Target customer segment or ICP definition
• display output enabled (always available)
• Web search provides market and persona research without additional setup

Enhanced functionality requires:

• CRM for existing customer patterns
• Call recordings for voice of customer insights
• Survey/research platform data
• Win/loss analysis documentation

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Scheduling & Automation with Taizen

Automate this skill: Schedule recurring customer research tasks with Taizen. Create a free account to set up automated agents that run on your schedule.

How It Works

The Taizen MCP server accepts natural language requests to schedule agents. Simply describe what you want to automate:

On the 1st of each month, aggregate voice of customer insights from NPS surveys,
G2 reviews, support tickets, and Gong calls to generate a monthly customer
insights report. Post to #customer-insights.

Taizen will:

1. Read this skill's definition to understand the capabilities
2. Create a recurring agent with your specified schedule
3. Execute the task and deliver results to your configured destinations

Example Natural Language Requests

Monthly Voice of Customer Report:

On the 1st of each month, aggregate VoC insights from NPS surveys, G2 reviews,
support tickets, and call recordings to generate a monthly customer insights
report. Share with #customer-insights.

Weekly Feedback Digest:

Every Monday at 8am, summarize customer feedback from the past week including
support themes, review trends, and call sentiment. Post a quick digest to
#customer-insights.

Quarterly ICP Analysis:

At the start of each quarter, analyze our customer data to validate and update
ICP definitions based on best customer characteristics and win patterns.
Update our ICP documentation and notify #product-marketing.

Win/Loss Trend Analysis:

On the 1st of each month, analyze last month's closed deals for win/loss
patterns by competitor, segment, and persona. Alert me if there are emerging
competitive threats or new loss patterns.

Setting Up Taizen Automation

1. Create Account: Sign up at usetaizen.com
2. Connect MCP: Add the Taizen MCP integration in your Claude settings and authenticate
3. Connect Data Sources: Link your CRM, Gong, and feedback tools
4. Schedule Agent: Describe your automation in natural language
5. Taizen Reads Skill: The agent receives this skill's full definition to understand what it can do

Technical Details

When scheduling via Taizen MCP, Claude will:

1. Read this SKILL.md file to get the full skill definition
2. Call Taizen MCP with the skill content included

Parameter	Description	Example
task	Natural language description of what to do	"Aggregate VoC insights and generate monthly report"
schedule	When to run (cron or trigger)	"on the 1st of each month" or "when new review posted"
skill_content	Object containing primary skill and referenced skills	See structure below
outputs	Where to send results	"Slack #customer-insights, Notion"
focus	Research focus area	"VoC", "ICP", "win/loss"

skill_content structure:

skill_content:
  primary:
    name: "customer-research"
    content: "<full content of this SKILL.md>"
  referenced:
    - name: "product-context"
      content: "<full content of product-context SKILL.md>"

Note to Claude: When a user asks to schedule this skill with Taizen, read this entire SKILL.md file and pass it as skill_content.primary. Also read any referenced background skills (like product-context) and include them in skill_content.referenced.

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Research Frameworks

1. Ideal Customer Profile (ICP)

Define the companies most likely to succeed with your product:

Firmographic Criteria

• Industry/vertical
• Company size (employees, revenue)
• Geography
• Growth stage
• Technology stack

Behavioral Signals

• Buying triggers and timing
• Current solutions in use
• Budget indicators
• Decision-making patterns

Success Indicators

• What makes customers successful
• Common characteristics of best customers
• Red flags and disqualifiers

2. Buyer Personas

Persona Components:

• Demographics: Title, role, reporting structure
• Responsibilities: Day-to-day, quarterly goals, KPIs
• Challenges: What keeps them up at night
• Goals: Professional and personal aspirations
• Buying Behavior: How they research, evaluate, decide
• Objections: Common concerns and hesitations
• Channels: Where they get information
• Influences: Who they trust and listen to

3. Jobs to Be Done (JTBD)

Job Statement Format: When [situation], I want to [motivation], so I can [expected outcome].

Job Layers:

• Functional job (practical task)
• Emotional job (how they want to feel)
• Social job (how they want to be perceived)

Forces of Progress:

• Push: Pain with current solution
• Pull: Attraction to new solution
• Anxiety: Fear of change
• Habit: Comfort with status quo

4. Voice of Customer (VoC)

Sources:

• Customer interviews
• Support tickets and feedback
• Reviews (G2, Capterra, etc.)
• Sales call recordings
• NPS and survey responses
• Social media and community

Analysis Framework:

• Themes and patterns
• Language and terminology used
• Emotional indicators
• Feature requests and gaps
• Competitive mentions

5. Win/Loss Insights

Win Analysis:

• Why did they choose us?
• What was the decisive factor?
• Who championed us internally?
• What would have lost the deal?

Loss Analysis:

• Why did we lose?
• Who won and why?
• What could we have done differently?
• Was it ever winnable?

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How to Use This Skill

Invoke with natural language describing the research you need:

ICP Development

• "Define our ideal customer profile based on our best customers"
• "Analyze our customer data to identify ICP patterns"
• "What characteristics do our most successful customers share?"
• "Build an ICP for our enterprise segment"

Persona Research

• "Create a buyer persona for VP of Sales"
• "What do we know about how CHROs buy software?"
• "Develop personas for our top 3 buyer types"
• "Update our CMO persona with recent interview data"

Jobs to Be Done

• "What jobs are our customers hiring us to do?"
• "Analyze the jobs to be done for our scheduling feature"
• "Map the forces of progress for switching from our competitor"

Voice of Customer

• "What are customers saying about our product?"
• "Analyze the themes from our recent NPS responses"
• "What language do customers use to describe their problems?"
• "Pull voice of customer insights for our messaging update"

Win/Loss Analysis

• "Why are we winning deals against Competitor X?"
• "Analyze our lost deals this quarter - what patterns do you see?"
• "What factors predict deal success?"
• "Review our win/loss data for enterprise deals"

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Output Format

ICP Definition

# Ideal Customer Profile: [Product/Segment]

**Created**: [Date]
**Data Sources Used**: [CRM analysis, customer success data, win/loss, etc.]

---

## Summary

*Based on analysis of [X] customers and [Y] deals:*

[2-3 sentence summary of the ideal customer]

---

## Firmographics

| Attribute | Ideal | Acceptable | Disqualifier |
|-----------|-------|------------|--------------|
| Industry | [Ideal industries] | [OK to pursue] | [Avoid] |
| Employee Count | [Range] | [Range] | [Outside this] |
| Revenue | [Range] | [Range] | [Outside this] |
| Geography | [Regions] | [Regions] | [Regions to avoid] |
| Growth Stage | [Stage] | [Stages] | [Stages to avoid] |

## Behavioral Signals

### Buying Triggers
*From win analysis and call recordings:*
- **[Trigger 1]**: [How to identify - data points]
- **[Trigger 2]**: [How to identify]
- **[Trigger 3]**: [How to identify]

### Technology Indicators
*From technographics and deal data:*
- **Must Have**: [Technologies that correlate with success]
- **Nice to Have**: [Good signals]
- **Red Flag**: [Technologies that predict churn]

### Intent Signals
- [Signal 1]: [What it means]
- [Signal 2]: [What it means]

## Success Indicators

### Best Customer Characteristics
*From customer success data and product usage:*
1. **[Characteristic 1]**: [Evidence - X% of top customers have this]
2. **[Characteristic 2]**: [Evidence]
3. **[Characteristic 3]**: [Evidence]

### Warning Signs
*From churn analysis:*
- **[Red flag 1]**: [Correlation with churn]
- **[Red flag 2]**: [Correlation with churn]

## Scoring Model

| Criteria | Weight | 3 Points | 2 Points | 1 Point | 0 Points |
|----------|--------|----------|----------|---------|----------|
| [Criteria 1] | [%] | [Ideal] | [Good] | [OK] | [Poor] |
| [Criteria 2] | [%] | [Ideal] | [Good] | [OK] | [Poor] |
| [Criteria 3] | [%] | [Ideal] | [Good] | [OK] | [Poor] |

**Tier A (Prioritize)**: Score 80+
**Tier B (Pursue)**: Score 60-79
**Tier C (Opportunistic)**: Score 40-59
**Disqualify**: Score <40

## Validation Data

- **Analysis based on**: [X] customers over [time period]
- **Win rate for Tier A**: [%]
- **Average deal size for Tier A**: [$]
- **Retention rate for Tier A**: [%]

Buyer Persona

# Buyer Persona: [Name/Title]

**Created**: [Date]
**Data Sources Used**: [Interviews, Gong calls, surveys, etc.]

---

## Quick Profile

| Attribute | Details |
|-----------|---------|
| Common Titles | [Titles] |
| Reports To | [Typical reporting] |
| Team Size | [Range] |
| Experience | [Years in role] |
| Role in Purchase | [Decision maker/Influencer/User] |

## Representative Quote

*From customer interviews:*
> "[Quote that captures their mindset]"

---

## Day in the Life

### Primary Responsibilities
*From job postings, interviews, and call recordings:*
- [Key responsibility 1]
- [Key responsibility 2]
- [Key responsibility 3]

### Success Metrics
*What they're measured on:*
- [Metric 1]
- [Metric 2]
- [Metric 3]

### Tools They Use Daily
- **[Category]**: [Common tools]
- **[Category]**: [Common tools]

---

## Psychology

### Goals & Aspirations
- **Professional**: [Career goals - from interviews]
- **Personal**: [Personal motivations]

### Challenges & Pain Points
*From VoC analysis:*
1. **[Challenge 1]**: [Impact on their work]
   - Voice of customer: "[Actual quote]"
2. **[Challenge 2]**: [Impact]
   - Voice of customer: "[Actual quote]"
3. **[Challenge 3]**: [Impact]

### Fears & Anxieties
- **[Fear 1]**: [How it manifests in buying behavior]
- **[Fear 2]**: [How it manifests]

---

## Buying Behavior

### Information Sources
*From marketing analytics and interviews:*
- [Where they research - with data on engagement]
- [Who they trust]

### Decision Criteria
*From win/loss analysis:*
1. **[Criterion 1]**: [Importance level]
2. **[Criterion 2]**: [Importance level]
3. **[Criterion 3]**: [Importance level]

### Common Objections
*From Gong analysis:*

| Objection | Frequency | Root Cause | Response |
|-----------|-----------|------------|----------|
| "[Objection]" | [% of deals] | [Why they say this] | [How to address] |

---

## Messaging That Resonates

### Language They Use
*From call recordings and reviews:*
- [Term they use]
- [How they describe the problem]

### Do Say
- [Message that works - with evidence]

### Don't Say
- [What doesn't resonate - with evidence]

---

## Engagement Strategy

### Best Channels
*From marketing analytics:*
- **[Channel 1]**: [Engagement data]
- **[Channel 2]**: [Engagement data]

### Content Preferences
*From content engagement:*
- [Format 1]: [Performance data]
- [Format 2]: [Performance data]

### Conversation Starters
- [Topic that resonates]
- [Topic that resonates]

Voice of Customer Report

# Voice of Customer Report: [Topic/Product Area]

**Created**: [Date]
**Data Sources Used**: [G2, NPS, Support, Gong, etc.]

---

## Summary

Analysis of [X] data points across [sources]:
[Key findings in 2-3 sentences]

---

## Theme Analysis

### Theme 1: [Theme Name]

**Mentions**: [Count] ([%] of total)
**Sentiment**: [Positive/Neutral/Negative]

**Representative Quotes**:
> "[Quote 1]" - [Source]
> "[Quote 2]" - [Source]

**Implications**:
- [What this means for product]
- [What this means for messaging]

### Theme 2: [Theme Name]
[Same format]

### Theme 3: [Theme Name]
[Same format]

---

## Competitive Mentions

| Competitor | Mentions | Context | Our Opportunity |
|------------|----------|---------|-----------------|
| [Competitor] | [Count] | [What they say] | [How to respond] |

---

## Language Patterns

### Words Customers Use
- "[Word/phrase]" - [frequency]
- "[Word/phrase]" - [frequency]

### Recommended Messaging Updates
Based on VoC, consider:
- [Recommendation 1]
- [Recommendation 2]

---

Automation Options

When configured with integrations, this skill can:

1. Automated ICP refresh - Regularly analyze customer data to update ICP definitions
2. VoC monitoring - Continuously aggregate customer feedback across sources
3. Win/loss triggers - Auto-generate analysis when deals close
4. Persona updates - Flag when new data suggests persona updates are needed
5. Competitive tracking - Monitor competitive mentions in customer feedback