codexkit-survey-analyzer

# Survey Analyzer ## When to Use - After collecting NPS, CSAT, or employee engagement survey responses - When analyzing market research or customer feedback data - When leadership needs actionable insights from survey results - When comparing satisfaction across segments (regions, teams, products) ## Procedure ### Step 1 — Data Overview Summarize the survey: - Total responses vs invitations sent → **response rate** - Collection period - Question types: Likert scale, multiple choice, ranking, open-ended - Known biases: self-selection, non-response, recency ### Step 2 — Quantitative Analysis For each closed-ended question: | Question | N | Mean | Median | Std Dev | Distribution Shape | |----------|---|------|--------|---------|-------------------| | [Q1 text] | [n] | [mean] | [median] | [sd] | Normal / Skewed L / Skewed R / Bimodal | Calculate key indices: - **NPS:** % Promoters (9–10) − % Detractors (0–6) - **CSAT:** % Satisfied (4–5 on 5-point scale) - **Engagement:** Overall index from engagement battery ### Step 3 — Segment Comparison Cross-tabulate by key segments: | Segment | N | Score | vs Overall | Significant? | |---------|---|-------|-----------|-------------| | Region A | 120 | 72 | +4 | Yes (p<0.05) | | Region B | 95 | 65 | −3 | No (p=0.12) | Test significance: - Chi-square for categorical × categorical - t-test or ANOVA for continuous × categorical - Flag small samples (<30) as unreliable ### Step 4 — Open-Ended Theme Extraction For free-text responses: 1. Code responses into themes (max 8–10 themes) 2. Count frequency of each theme 3. Identify sentiment per theme (positive / neutral / negative) | Theme | Frequency | % of Responses | Sentiment | Example Quote | |-------|-----------|---------------|-----------|---------------| | Onboarding speed | 45 | 18% | Negative | "Took 3 weeks to get access" | ### Step 5 — Insight Synthesis Structure insights as: - **What:** the finding (data-driven) - **So What:** why it matters (impact) - **Now What:** recommended action ### Step 6 — Action Recommendations | Priority | Insight | Action | Owner | Timeline | |----------|---------|--------|-------|----------| | 1 | NPS dropped 8 pts in segment X | Investigate root cause | CX Lead | 2 weeks | ## Inputs | Input | Required | Format | |-------|----------|--------| | Survey responses | Yes | CSV, spreadsheet, or summary tables | | Question list | Yes | Questions with response types | | Segments | Recommended | How to slice the data | | Previous results | Recommended | For trend comparison | ## Output ```markdown ## Survey Analysis — [Survey Name] ### Overview - **Period:** Q1 2024 | **Responses:** 532 / 1,200 (44% response rate) - **NPS:** +32 (↓4 from Q4) | **CSAT:** 78% (stable) ### Key Findings 1. **Onboarding satisfaction dropped 12 points** — driven by Region B - Root cause: New system rollout created delays - Action: Expedite training for Region B ops team 2. **Support quality is the #1 driver of promoter scores** - Correlation: 0.72 between support rating and NPS - Action: Protect support team headcount in budget cycle ### Segment Comparison [Cross-tabulation table] ### Open-Ended Themes [Theme frequency table with example quotes] ### Recommended Actions [Prioritized action table with owners] ``` ## Definition of Done - [ ] Response rate calculated and bias noted - [ ] All quantitative questions summarized with distributions - [ ] Segment comparisons with significance testing - [ ] Open-ended themes extracted and quantified - [ ] What / So What / Now What insights provided - [ ] Action recommendations prioritized with owners ## Quality Criteria - [ ] Data sources and assumptions are explicitly stated - [ ] Calculations are reproducible from provided inputs - [ ] Visualizations or tables have clear labels, units, and time ranges - [ ] Caveats and confidence levels are documented for estimates ## Verification (4C) | Check | Question | |-------|----------| | **Correctness** | Are formulas, aggregations, and statistical methods applied correctly? | | **Completeness** | Does the analysis cover all requested metrics and time ranges? | | **Context-fit** | Are the chosen metrics relevant to the business question being answered? | | **Consequence** | If this data were used for a decision today, what blind spots remain? | ## Edge Cases - **Missing or incomplete data** — Document gaps and their potential impact on conclusions. Provide ranges instead of point estimates. - **Outliers skewing results** — Report with and without outliers. Document the decision to include or exclude. - **Changing data definitions mid-period** — Split analysis at the change boundary and note the schema difference. ## Changelog - v1.0.0 — Initial release