Satisfaction Survey

Build a NocoBase app — Satisfaction Survey: an eNPS gauge, a dimension heatmap, and a grid of feedback cards. Match the layout and signature visuals of this reference prototype: https://static-docs.nocobase.com/solution/templates/14-satisfaction-survey.html

Introduction

Use your favorite AI agent and NocoBase to quickly build a customizable, reliable, and continuously evolvable employee satisfaction survey system for analyzing eNPS, overall satisfaction, response rate, employee engagement, different satisfaction dimensions, department differences, and open-ended feedback.

You can copy the prompt below and let your AI agent generate the foundation of a satisfaction survey system in NocoBase, then adjust metrics, dimensions, charts, and feedback pages through the no-code UI.

This system fits scenarios such as employee engagement surveys, quarterly satisfaction surveys, organizational health checks, team climate assessments, management feedback, compensation and benefits feedback, and employee experience analysis.

Core satisfaction metrics and trends:

Department heatmap and open-ended sentiment distribution:

Representative employee feedback:

AI employee generates analysis reports and recommendations:

What problems can an employee satisfaction survey system solve?

The hard part of employee satisfaction surveys is not just collecting questionnaires, but turning a large volume of ratings and written feedback into clear, comparable, and continuously trackable organizational insights.

If a team collects responses with ordinary forms only, HR often has to manually calculate eNPS, average scores, response rates, and engagement, then separately tally results by department and by dimension. As open-ended feedback grows, it also becomes hard to quickly judge overall employee sentiment and high-frequency issues.

With this system, you can centrally view core metrics such as eNPS, overall satisfaction, response rate, and engagement, and analyze dimensions including work-life balance, management and leadership, growth and development, compensation and benefits, team collaboration, company culture, and role identification.

The system can also display satisfaction trends, helping teams compare results across quarters or survey rounds. The department-by-dimension heatmap visually shows which teams or which dimensions are underperforming, making it easy for managers to quickly locate areas that need attention.

For open-ended answers, the system can classify sentiment as positive, mixed, or negative, and display representative employee feedback along with role, department, related dimension, and rating, connecting quantitative data with real employee voices.

Core features

Core employee satisfaction metrics

• eNPS analysis: Automatically calculate the employee Net Promoter Score and show the share of promoters, passives, and detractors.
• Overall satisfaction: Aggregate the average rating across all survey dimensions for a quick read on overall employee experience.
• Response rate and engagement: Show the actual number of respondents, survey response rate, and an employee engagement index to judge how representative the survey results are.

Satisfaction dimension analysis

• Multi-dimension scoring: Analyze dimensions such as work-life balance, management and leadership, growth and development, compensation and benefits, team collaboration, company culture, and role identification.
• Low-score issue identification: Use dimension scores to quickly locate areas where employee satisfaction is low.
• Strength discovery: Identify well-performing areas such as team collaboration and company culture as references for management improvement.

Survey trend comparison

• Cross-cycle trends: Compare changes in overall satisfaction, eNPS, and engagement across quarters or survey rounds.
• Improvement tracking: Observe whether metrics keep improving after management actions are implemented.
• Long-term data accumulation: Retain historical survey results to build a continuously comparable record of employee experience.

Department and dimension heatmap

• Cross-department comparison: Compare satisfaction performance across departments such as Engineering, Sales, Marketing, HR, and Finance.
• Dimension cross-analysis: Use a heatmap to show each department’s average score on every survey dimension.
• Key department identification: Quickly spot pronounced issues in specific departments around compensation, management, growth, or workload.

Question score distribution

• Per-question analysis: Show the distribution of answers from 1 to 5 and the average score for each question.
• Opinion polarization detection: Determine whether employee feedback is relatively consistent or clearly split between high and low scores.
• Key question tracking: Focus on questions about management communication, workload, career development, and willingness to recommend the company.

Open-ended feedback analysis

• Sentiment classification: Categorize open-ended feedback as positive, mixed, or negative and report the share of each.
• Feedback theme grouping: Organize employee comments by dimensions such as team collaboration, management and leadership, growth and development, and company culture.
• Representative feedback display: Browse feedback cards showing the comment text, employee role, department, sentiment label, and rating.

AI analysis and report generation

• AI sentiment recognition: Automatically analyze the sentiment of open-ended responses, reducing manual one-by-one classification.
• AI key-issue extraction: Summarize high-frequency themes, main pain points, and strengths from large volumes of employee feedback.
• AI improvement recommendations: Combine eNPS, dimension scores, and department differences to produce employee satisfaction analysis and follow-up action suggestions.

Why build an employee satisfaction survey system with AI and NocoBase?

The point of an employee satisfaction survey is to let teams continuously compare results, surface problems, and retain historical data across departments and survey rounds.

If you simply use ordinary vibe coding to generate a survey page from scratch, you usually only get form collection. The downstream eNPS calculation, dimension scoring, department comparison, trend analysis, open-ended sentiment classification, and feedback display still require extra development.

NocoBase can connect surveys, questions, answers, departments, roles, and survey rounds, and present the analysis through charts, heatmaps, and feedback cards. Teams can also adjust dimensions, scoring methods, and pages to match their own organizational structure and survey framework.

AI can go further with open-ended responses. You can have AI automatically judge whether feedback is positive, mixed, or negative, and group answers into dimensions such as management, growth, collaboration, and culture, reducing the manual effort HR spends sorting large volumes of text.

A satisfaction survey system built this way is not a one-off questionnaire, but an organizational feedback system that keeps accumulating data, compares trends, analyzes team differences, and tracks improvement over time.

FAQ

1. Can it automatically calculate the employee Net Promoter Score (eNPS)?

Yes. Based on employees’ ratings for “would you recommend the company,” the system automatically distinguishes promoters, passives, and detractors, and calculates the eNPS score along with the share of each group.

Managers can see not only the final score, but also whether a change in eNPS comes from fewer promoters or more detractors.

2. Besides eNPS, what other core metrics can I view?

You can also view overall satisfaction, survey response rate, employee engagement, and the actual number of respondents.

For example, if the satisfaction score is high but the response rate is too low, the results may not represent the whole workforce; the engagement index helps HR judge how many employees are actively invested.

3. Can results be analyzed by different satisfaction dimensions?

Yes. The system can separately report dimensions such as work-life balance, management and leadership, growth and development, compensation and benefits, team collaboration, company culture, and role identification.

This is more valuable than a single overall score, because the team can know precisely whether problems mainly come from management, compensation, or career development.

4. Can I compare satisfaction differences across departments?

Yes. The department-by-dimension heatmap shows each department’s average score on every dimension.

For example, a department may perform well in team collaboration but score noticeably low in work-life balance or compensation and benefits. HR can then design more targeted improvement plans instead of applying the same measures company-wide.

5. Can AI analyze employees’ open-ended feedback?

Yes. AI can identify written responses as positive, mixed, or negative feedback, and further group them into dimensions such as management, growth, collaboration, culture, and compensation.

It can also extract recurring problems and positive themes from large volumes of answers, reducing HR’s manual reading and classification work.

6. Can AI directly generate survey conclusions and next-step action recommendations?

Yes. AI can combine ratings, eNPS, department differences, and open-ended feedback to produce an employee survey analysis report and a follow-up plan.

For example, the report in the screenshot identifies priority issues such as work-life balance, compensation, management responsiveness, and career development, points out which departments need extra attention, and suggests improvement actions.

7. Can employee feedback stay anonymous?

Yes. You can configure anonymous or named modes based on your survey rules.

For anonymous surveys, you can prevent regular managers from seeing individual identities and only show department- or dimension-level aggregates. For named surveys, permissions determine what HR, department heads, or administrators can see.

Anonymity rules should be clearly explained before employees submit, to avoid concerns about how the data will be used.

8. Can department heads view only their own department’s results?

Yes. NocoBase supports permissions by role and data scope.

For example, department heads can only view their own department’s aggregated scores and feedback trends, HR can view cross-department analysis, and the system administrator handles survey configuration. For departments with very small sample sizes, you can also restrict detail views to reduce the risk of identifying individual employees from their feedback.

9. Can I track who viewed or modified the survey data?

Yes. You can enable operation history and audit logs as needed, recording key actions such as survey configuration, result modification, permission changes, and report access.

Employee surveys involve sensitive internal information; keeping operation records helps the company control data access and trace issues when accidental changes or data disputes occur.

10. Can Claude Code, Codex, Cursor, or OpenCode help build this system?

Yes. AI coding agents such as Claude Code, Codex, Cursor, and OpenCode can connect to NocoBase and generate surveys, questions, answers, satisfaction dimensions, and analysis pages from prompts.

Afterwards, HR can still adjust questionnaire items, statistics, permissions, and charts through NocoBase’s no-code UI, without redeveloping every time the survey changes.

11. How is this different from an ordinary online questionnaire or a survey page generated by vibe coding?

Ordinary online questionnaires are better suited to one-time data collection, and vibe coding can quickly generate a survey form, but permissions, historical data, cross-cycle trends, department comparison, open-ended analysis, and access auditing usually still need to be handled separately.

NocoBase keeps surveys, answers, departments, roles, and historical cycles in one system. AI handles analysis and recommendations, while NocoBase handles data relationships, access control, and long-term maintenance.

12. Is this system suitable for tracking employee satisfaction over the long term?

Yes. The system can keep storing survey results across quarters or years and compare changes in eNPS, satisfaction, engagement, department differences, and open-ended themes.

Companies can also enable single sign-on, permission management, audit logs, workflows, notifications, and plugin extensions as needed. Compared with one-off survey tools, it is better suited to building a long-term, comparable employee feedback system with proper data governance.