Home Feedback Widget Understanding Insights, Metrics, and AI Analysis for Feedback Widgets

Understanding Insights, Metrics, and AI Analysis for Feedback Widgets

Last updated on May 27, 2025

Accessing Widget Analytics

  1. From your Widgets dashboard, click on any active widget

  2. You will see the widgets detailed analytics

Widget Analytics Interface Overview

The analytics interface provides:

  • Time Period Filters: Custom, Today, This week, This month, All time

  • Search Functionality: Find specific responses or keywords

  • Export Options: Download data for external analysis

  • Real-time Updates: Live data as new responses come in

Understanding the Insights Dashboard

Main Insights Sections

The Insights tab is divided into three powerful sections:

1. Insights

  • Overview Analytics: High level performance indicators

  • User Behavior Patterns: How users interact with your widgets

  • Satisfaction Trends: Quick view of overall user sentiment

2. Metrics

  • Performance Indicators: Detailed engagement statistics

  • Response Analysis: Breakdown of feedback types and sources

  • Technical Analytics: Device, browser, and geographic data

3. AI Analysis

  • Automated Insights: AI-generated summaries and patterns

  • Interactive Chat: Ask questions about your data

  • Trend Recognition: AI-identified patterns in feedback

Responses Analysis

Individual Response Management

  • Satisfaction Ratings: Visual emoji indicators with numerical scores (1.0-5.0)

  • Tell us about your experience: Complete user comments and suggestions

  • Email: User contact information (when provided)

  • User ID: Unique identifiers for tracking user journeys

  • Country: Geographic location data

  • Action: Context of what user was doing when providing feedback

Detailed Response Views

Clicking "View Detail" reveals comprehensive information:

Metrics Deep Dive

Key Performance Indicators

The Metrics section provides essential engagement statistics:

Primary Metrics

  • Impressions : Number of times widget was displayed to users

  • Engagements : Number of user interactions with the widget

  • Submissions : Total completed feedback responses

  • Link Clicks : Clicks on any links within the widget

  • Average Submission Time : Time users take to complete feedback

Advanced Analytics Charts

Responses by Feedback Type

Pie Chart Breakdown:

  • CSAT Responses: Customer satisfaction feedback (10 responses)

  • Bug Reports: Technical issue reports (5 responses)

  • Feature Requests: User suggestions for new features (3 responses)

  • Net Score: Net Promoter Score feedback (7 responses)

Geographic Analysis - Responses by Country

Global Distribution:

  • Nigeria: 22 responses (majority of feedback)

  • United Kingdom: 3 responses

  • Other Regions: Additional geographic breakdown

  • Regional Insights: Understanding user base distribution

Technical Analytics

Responses by Device:

  • Mobile Devices: Smartphone and tablet usage

  • Desktop Computers: Traditional computer access

  • Device-Specific Insights: Optimization opportunities for popular devices

Responses by Operating System:

  • iOS: Apple device users

  • Android: Google platform users

  • Windows: Microsoft system users

  • Technical Optimization: Understanding technical user base

Analytics Interpretation

These metrics help you understand:

  • Widget Performance: How effectively widgets capture user attention

  • User Engagement: Quality of user interaction with feedback requests

  • Response Quality: Completion rates and submission success

  • Technical Optimization: Device and platform performance insights

AI Analysis and Chat Features

Crowd's AI Analysis provides revolutionary feedback analysis capabilities:

Generate AI Summary Feature

  • One-Click Analysis: Generate comprehensive insights instantly

  • Pattern Recognition: AI identifies trends humans might miss

  • Automated Reporting: Quick summaries without manual analysis

  • Time Savings: Get insights in minutes instead of hours

Interactive AI Chat

Key Features:

  • Natural Language Queries: Ask questions in plain English

  • Data Exploration: "What are users most unhappy about?"

  • Trend Analysis: "How has satisfaction changed this month?"

  • Comparative Insights: "Which countries give the highest ratings?"

What AI Can Tell You:

  • Sentiment Patterns: Overall emotional trends in feedback

  • Common Themes: Recurring topics in user comments

  • Satisfaction Drivers: What makes users happy or unhappy

  • Improvement Opportunities: Areas requiring attention

  • Seasonal Trends: Time-based patterns in feedback

  • User Segment Insights: Different user group behaviors

Using AI Chat Effectively

Sample Questions to Ask AI:

  • "What are the main reasons for low ratings?"

  • "Which features do users request most often?"

  • "How does mobile satisfaction compare to desktop?"

  • "What bugs are reported most frequently?"

  • "Which countries have the highest satisfaction?"

  • "What improvement suggestions appear most often?"

AI Response Types:

  • Statistical Summaries: Quantitative insights with percentages

  • Trend Analysis: Changes over time with explanations

  • Categorical Breakdowns: Organized insights by topic

  • Actionable Recommendations: Specific suggestions for improvement

AI Analysis Benefits

  • Speed: Instant insights without manual data processing

  • Accuracy: AI processes all data without human oversight errors

  • Comprehensive: Analyzes patterns across all feedback simultaneously

  • Objective: Unbiased analysis of user sentiment and feedback

  • Scalable: Handles increasing volumes of feedback automatically

Advanced Analytics Features

Custom Time Period Analysis

Set specific date ranges to analyze:

  • Campaign Performance: Analyze feedback during specific marketing campaigns

  • Product Launch Impact: Measure satisfaction before and after releases

  • Seasonal Trends: Understanding patterns during different times of year

  • Issue Resolution: Track satisfaction changes after fixing reported problems

Cross-Reference Analytics

Combine feedback widget data with:

  • Website Analytics: Correlate satisfaction with user behavior

  • Sales Data: Understand relationship between satisfaction and conversions

  • Support Tickets: Compare widget feedback with formal support requests

  • User Journey: Map feedback to specific user experience touchpoints

Export and Integration Options

  • Data Export: Download raw data for advanced analysis

  • API Integration: Connect feedback data with other business tools

  • Automated Reports: Set up regular analytics summaries

  • Team Sharing: Distribute insights to relevant stakeholders

Using Data for Business Decisions

Product Development Insights

Prioritizing Features:

  • Use feature request frequency to guide development roadmap

  • Identify most requested improvements from user comments

  • Understand user pain points requiring immediate attention

Quality Assurance:

  • Track bug report patterns to identify systematic issues

  • Monitor satisfaction trends after bug fixes

  • Use device/browser data to prioritize compatibility testing

Customer Experience Optimization

Satisfaction Improvement:

  • Identify specific user journey points causing dissatisfaction

  • Use geographic data to address regional experience issues

  • Monitor satisfaction trends to measure improvement efforts

User Retention:

  • Correlate satisfaction scores with user behavior patterns

  • Identify at-risk user segments through feedback analysis

  • Develop targeted retention strategies based on feedback themes

Marketing and Sales Intelligence

Customer Sentiment:

  • Use satisfaction data for customer testimonials and case studies

  • Understand brand perception through qualitative feedback

  • Identify customer advocates through high satisfaction scores

Market Research:

  • Geographic feedback patterns reveal market preferences

  • Device usage data informs marketing channel optimization

  • User suggestions guide product positioning and messaging

Best Practices for Analytics

Regular Analysis Schedule

  • Daily: Quick checks for urgent issues or concerning trends

  • Weekly: Comprehensive review of satisfaction trends and new feedback

  • Monthly: Deep dive analysis using AI chat and comprehensive reports

  • Quarterly: Strategic analysis for business planning and product roadmap

Effective AI Chat Usage

  • Specific Questions: Ask targeted questions rather than general queries

  • Follow-up Queries: Build on AI responses with deeper questions

  • Cross-Reference: Combine AI insights with manual observation

  • Action Planning: Use AI insights to create specific improvement plans

Data-Driven Decision Making

  • Quantify Impact: Measure satisfaction changes after implementing feedback

  • Prioritize by Volume: Address issues mentioned by multiple users first

  • Balance Feedback: Consider both positive and negative feedback for complete picture

  • Track Progress: Monitor satisfaction trends after making changes

Team Collaboration

  • Share Insights: Distribute relevant analytics to appropriate team members

  • Regular Reviews: Schedule team meetings to discuss feedback trends

  • Action Assignment: Assign specific team members to address feedback themes

  • Success Tracking: Measure team efforts through satisfaction improvements

By mastering these insights, metrics, and AI analysis features, you will transform user feedback into powerful business intelligence that drives meaningful improvements and enhanced user satisfaction.