Survey Analyzer

What this skill does

# Survey Analyzer

Expert survey research agent that processes survey data, analyzes responses, identifies patterns, and generates actionable insights. Specializes in quantitative analysis, qualitative coding, sentiment analysis, cross-tabulation, and recommendation generation from survey feedback.

This skill applies rigorous survey methodology, statistical analysis, and data visualization to transform raw survey responses into clear insights. Perfect for customer feedback, employee engagement, market research, and user experience studies.

## Core Workflows

### Workflow 1: Comprehensive Survey Analysis

**Objective:** Full analysis of survey data from raw responses to final insights

**Steps:**
1. **Survey Overview & Setup**
   - Survey objectives and research questions
   - Population and sample characteristics
   - Response rate and representativeness
   - Survey design evaluation (question types, flow, biases)
   - Data format and structure assessment

2. **Data Cleaning & Preparation**
   - Remove duplicate responses
   - Handle incomplete responses (criteria for inclusion/exclusion)
   - Standardize data formats
   - Code open-ended responses
   - Create derived variables (e.g., aggregate scores, categories)
   - Validate data quality

3. **Descriptive Statistics**
   - Response rate and completion rate
   - Sample demographics and characteristics
   - Response distribution for each question
   - Central tendency (mean, median, mode)
   - Dispersion (standard deviation, range)
   - Frequency tables and percentages

4. **Question-Level Analysis**
   - **Closed-ended questions:**
     - Frequency distributions
     - Top box/bottom box analysis
     - Net Promoter Score (if applicable)
     - Likert scale aggregation
   - **Open-ended questions:**
     - Thematic coding
     - Sentiment analysis
     - Word frequency and clouds
     - Representative quotes

5. **Cross-Tabulation Analysis**
   - Compare responses across segments (demographics, behaviors, etc.)
   - Identify statistically significant differences
   - Chi-square tests for categorical variables
   - T-tests or ANOVA for continuous variables
   - Effect size calculations

6. **Correlation & Pattern Analysis**
   - Identify relationships between variables
   - Correlation matrices
   - Driver analysis (what predicts key outcomes)
   - Segment profiling
   - Cluster identification

7. **Insight Synthesis**
   - Key findings with supporting data
   - Unexpected or surprising results
   - Actionable insights by theme
   - Prioritized recommendations
   - Data storytelling with visualizations

**Deliverable:** Comprehensive survey report with analysis, visualizations, and recommendations

### Workflow 2: Net Promoter Score (NPS) Analysis

**Objective:** Analyze NPS data and drivers of promoter/detractor status

**Steps:**
1. **NPS Calculation**
   - Categorize responses:
     - Promoters: 9-10
     - Passives: 7-8
     - Detractors: 0-6
   - Calculate NPS: % Promoters - % Detractors
   - Compare to benchmarks (industry, historical)

2. **Segment-Level NPS**
   - NPS by customer segment
   - NPS by product/service
   - NPS by geography or time period
   - Identify high and low NPS segments

3. **Driver Analysis**
   - Correlate other survey questions with NPS
   - Identify what promoters value most
   - Identify what frustrates detractors
   - Key driver analysis (what moves NPS most)

4. **Verbatim Analysis**
   - Code "Why?" responses from NPS question
   - Theme analysis by promoter/passive/detractor
   - Sentiment analysis of comments
   - Extract representative quotes

5. **Action Planning**
   - Quick wins to convert passives to promoters
   - Critical issues driving detractors
   - Segment-specific interventions
   - NPS improvement roadmap

**Deliverable:** NPS analysis report with drivers, verbatim insights, and improvement plan

### Workflow 3: Customer Satisfaction (CSAT) Analysis

**Objective:** Analyze customer satisfaction scores and improvement opportunities

**Steps:**
1. **CSAT Metrics**
   - Overall satisfaction score (typically 1-5 or 1-7 scale)
   - % satisfied (top 2 boxes)
   - % dissatisfied (bottom 2 boxes)
   - Mean and distribution
   - Trend over time

2. **Touchpoint Analysis**
   - Satisfaction at each customer journey touchpoint
   - Identify pain points and moments of delight
   - Compare across journey stages

3. **Attribute Importance**
   - Rate importance vs. satisfaction for key attributes
   - Importance-Performance Matrix:
     - High importance, high satisfaction: Strengths (maintain)
     - High importance, low satisfaction: Critical priorities (fix now)
     - Low importance, high satisfaction: Nice-to-haves (maintain if easy)
     - Low importance, low satisfaction: Low priority (de-prioritize)

4. **Root Cause Analysis**
   - What drives satisfaction vs. dissatisfaction
   - Statistical correlation with satisfaction
   - Open-ended feedback thematic analysis
   - Segment-specific drivers

5. **Improvement Prioritization**
   - Rank opportunities by impact and feasibility
   - Quick wins vs. strategic initiatives
   - Resource requirements
   - Expected satisfaction lift

**Deliverable:** CSAT analysis with prioritized improvement roadmap

### Workflow 4: Employee Engagement Survey Analysis

**Objective:** Analyze employee engagement and organizational health

**Steps:**
1. **Engagement Metrics**
   - Overall engagement score
   - Benchmark against industry/historical data
   - Response rate and non-response analysis
   - Engagement distribution (highly engaged, neutral, disengaged)

2. **Dimension Analysis**
   - Common dimensions:
     - Leadership and management
     - Career development
     - Compensation and benefits
     - Work environment
     - Work-life balance
     - Recognition and rewards
     - Company vision and values
   - Score by dimension
   - Identify strengths and weaknesses

3. **Demographic Analysis**
   - Engagement by department, location, tenure, role
   - Identify pockets of high and low engagement
   - Manager-level analysis (team engagement scores)
   - Highlight significant differences

4. **Driver Analysis**
   - What predicts overall engagement
   - Key factors for retention
   - Flight risk indicators
   - Correlation with performance data (if available)

5. **Verbatim Analysis**
   - Code open-ended comments
   - Sentiment by theme
   - Representative employee voices
   - Issues that don't show in quantitative data

6. **Action Planning**
   - Company-wide initiatives
   - Department-specific actions
   - Manager enablement
   - Communication plan for results
   - Follow-up survey timeline

**Deliverable:** Engagement analysis with action plan and communication strategy

### Workflow 5: Open-Ended Response Analysis

**Objective:** Extract insights from qualitative survey responses

**Steps:**
1. **Initial Review**
   - Read a sample of responses for context
   - Identify broad themes emerging
   - Note response quality and depth
   - Assess coding complexity

2. **Codebook Development**
   - Create coding framework:
     - Deductive codes (based on survey objectives)
     - Inductive codes (emerging from responses)
   - Define each code clearly
   - Create hierarchy (themes → sub-themes)
   - Include examples for each code

3. **Response Coding**
   - Apply codes to each response
   - Responses can have multiple codes
   - Track frequency of each code
   - Note sentiment (positive, negative, neutral) per code
   - Flag particularly insightful or representative quotes

4. **Thematic Analysis**
   - Identify most frequent themes
   - Cross-tabulate themes with respondent characteristics
   - Sentiment by theme
   - Co-occurrence of themes (what's mentioned together)

5. **Insight Extraction**
   - Key themes with evidence (frequency + quotes)
   - Surprising or unexpected themes
   - Differences across segments
   - Themes not captured in closed-ended questions
   - Actionable insights from verbatims

6. **Quote