Shift Feedback

Collect and manage feedback from employees about their shift experiences.

MangoApps

Overview

Shift Feedback enables systematic collection of employee feedback about their work experiences, shift conditions, and operational insights. This app helps managers understand workplace dynamics, identify improvement opportunities, and maintain high employee satisfaction through structured feedback collection and analysis.

Scheduling

Highlights

Collect structured feedback from employees after every shift.

Rate shifts on a 1-to-5 scale with optional tags and written comments.

View personal feedback history with rating and tag filters.

Track feedback stats including average rating and high/low rating counts.

Link feedback to specific shifts and attendance records for context.

Identify trends in shift experience to improve workplace conditions.

Capabilities

Feedback Collection

Post-shift rating on a 1–5 scale
Optional written comment with each submission
One submission per attendance record (duplicate guard)
Anonymous submission option (admin-controlled)
Configurable feedback window after checkout
Ad-hoc shift feedback (off by default)
Early-checkout feedback (off by default)
Bulk retroactive feedback import Not supported

Question Configuration

Predefined tag set (busy, calm, stressful, understaffed, etc.)
Primary tag selection per submission
Multi-tag selection per submission
Admin toggle to enable / disable tag feature
Custom tags (admin-configured)
Custom free-form question builder Fixed schema only

Sentiment & AI Analysis

High / low rating scopes (≥4 and ≤2)
AI agent: feedback trends & statistics via Ask AI
AI agent: filter by period, rating range, and tag
AI agent: personal feedback history query
Admin toggle to disable AI agent per business
NLP sentiment scoring on comment text Not available

Manager Review

Manager dashboard: browse all feedback with filters
Filter by rating, location, and date range
Drill into individual feedback detail view
Related feedback from same shift shown in detail view
Low-rating in-app alert notifications to managers
Manager written response to an employee submission

Reporting & Export

Summary stats: total, average rating, high/low counts
Response rate against completed shifts (30-day window)
Unique employee contributor count
CSV export (manager)
Trend analysis (week / month / quarter)
PDF or scheduled report delivery Not supported

Limits & Specs

Rating scale: 1–5 stars
Eligible shifts per employee: Up to 3 most recent (admin-configurable: 1–10)
Feedback window default: 1 week (admin-configurable: 24 h – 2 weeks)
Built-in feedback tags: 9 (busy, calm, stressful, understaffed, well_managed, equipment_issues, technical_problems, good_team, difficult_customers)
Data retention default: 365 days (admin-configurable: 90 days – unlimited)
Pricing: Included with MangoApps Workforce

Use cases

Post-shift check-in

After completing a shift, employees submit a quick rating and optional comments about their experience.

Identify problem areas

Managers filter feedback by low ratings to find shifts or locations with recurring issues that need attention.

Track sentiment over time

Review average feedback ratings across weeks or months to measure the impact of workplace changes.

Tag-based analysis

Use feedback tags to categorize issues (e.g., staffing, equipment, workload) and identify the most common concerns.

Employee self-review

Employees browse their own feedback history to reflect on shift experiences and track patterns.

FAQ

Feedback is collected after a shift is completed. Each feedback entry is linked to a specific shift and, when available, the corresponding attendance record.

Feedback is associated with the submitting employee. Consult your administrator about any anonymization policies configured for your business.

Tags are short labels like "staffing", "equipment", or "workload" that categorize feedback. They make it easy to filter and analyze common themes across many responses.

Yes. Managers can browse feedback submitted by team members, filter by rating or tag, and drill into individual responses for detail.