Design and Analyze NPS Survey Frameworks

This skill provides comprehensive expertise in Net Promoter Score (NPS) survey methodology, from question design through statistical analysis to actionable business insights. It covers the complete NPS lifecycle: the standard "How likely is it that you would recommend [company/product/service] to a friend or colleague?" question on a 0-10 scale, score calculation using the formula (% Promoters - % Detractors), timing strategies (relationship, transactional, and touchpoint NPS), and frameworks for translating data into prioritized improvement initiatives.

Use this skill when you need to measure customer loyalty at scale, design survey frameworks for B2B or B2C contexts, optimize response rates, segment NPS data by customer cohorts, or build executive dashboards with statistically significant metrics. Deploy it for post-onboarding surveys (30+ days after signup), quarterly relationship checks for active users, or post-support interaction feedback. Do NOT use this for sample sizes under 30 responses per segment (statistical significance threshold) or when surveying the same customer within 90 days (creates survey fatigue).

You provide your business context (B2B/B2C, industry, customer segments), survey timing requirements, and existing response data. The skill covers survey question structures, distribution strategies with smart triggers, segmentation analysis approaches, trend analysis methodologies, and response action frameworks. The source material includes this standard question structure:

1. NPS Question (0-10 scale)
2. Open-ended follow-up: "What is the primary reason for your score?"
3. Categorical follow-up (optional): "Which area most influenced your rating?"
   - Product quality
   - Customer service
   - Pricing/value
   - Ease of use
   - Other (specify)
4. Demographic/segmentation questions (2-3 max)

The source material also includes B2B-specific variations and feature-specific versions:

### For B2B contexts:
"How likely would you be to recommend [product] to a colleague in a similar role?"

### For specific features:
"Based on your experience with [feature], how likely would you be to recommend it?"

### For competitive differentiation:
"Compared to alternatives you've considered, how likely would you be to recommend us?"

The source material includes examples of SQL queries for database analysis (calculating NPS by segment, promoter/detractor percentages with 90-day windows), Python code using scipy and pandas for trend analysis with seasonality detection and statistical significance testing, and YAML-based response action playbook structures for detractor outreach (24-hour response timeline) and promoter activation (48-hour thank-you and referral program invites).

Customer success managers who need to design quarterly relationship surveys and respond to detractors within 24 hours. Product managers measuring feature-specific NPS and prioritizing improvements using impact-vs-effort matrices. Data analysts building executive dashboards with confidence intervals, YoY trends, and correlation analysis between NPS and business metrics like churn. Marketing teams activating promoters for review requests and case study opportunities. The source material emphasizes statistical rigor (minimum 100 responses per segment for significance, 95% confidence intervals) and includes industry benchmarks (SaaS B2B good: +30 to +40, excellent: +50+).