Overview
Our comprehensive analysis employed a multi-phase approach to extract actionable insights from telecom customer feedback and regulatory complaints. We combined large-scale data extraction, AI-powered analysis, and statistical validation to understand the critical failure points driving customer frustration and CCTS escalations.
Data Collection
Phase 1: Review Extraction
- Initial Extraction: 30,000+ app reviews collected across iOS App Store and Google Play Store
- Providers: Rogers (MyRogers app) and Bell (MyBell app)
- Platforms: Separate extraction for iOS and Android to identify platform-specific issues
- Tools: Automated scraping tools with rate limiting to ensure data integrity
Phase 2: Data Selection and Cleaning
- Deduplication: Removed duplicate reviews based on review ID and timestamp
- Validation: Verified review authenticity and removed bot/spam content
- Standardization: Normalized date formats, ratings, and platform identifiers
- Final Dataset: 12,785 reviews selected for individual analysis
- Selection Strategy: Analyzed all iOS reviews and a representative portion of Android reviews to ensure comprehensive insights while maintaining analysis quality
Phase 3: CCTS Data Integration
- Source: Commission for Complaints for Telecom-television Services
- Period: August 2024 - January 2025 (6 months)
- Complaints: 15,913 formal complaints with categorization
- Purpose: Validate app-related issues against regulatory escalations
Analysis Methodology
AI-Powered Individual Review Analysis
Key Innovation: Each of the 12,785 reviews was analyzed individually using Anthropic's Claude API rather than batch processing, ensuring nuanced understanding of customer frustrations.
Sentiment Classification
Positive/Negative/Neutral categorization with confidence scoring
Issue Categorization
Technical/Billing/UX/Features/Support classification
Severity Assessment
Critical/High/Medium/Low impact rating
Service Impact Prediction
Likelihood of requiring customer service escalation
Statistical Analysis
95%
Confidence intervals calculated
2
Platform comparison (iOS vs Android)
2
Provider comparison (Rogers vs Bell)
15+
Years of temporal analysis
Validation Process
- Cross-Validation: Compared review insights with CCTS complaint categories
- Sample Verification: Manual review of 500 randomly selected reviews
- Statistical Significance: Ensured all reported differences exceeded margin of error
- Edge Case Identification: Focused on the 0.06% experiencing breaking points
Key Methodological Decisions
1. Focus on Written Reviews
Rationale: Written reviews represent users at "breaking points"—moments of extreme frustration that predict support escalations and CCTS complaints. While only 0.06% of users write reviews¹, they reveal the exact failure modes that drive regulatory complaints.
2. Sentiment vs. Rating Analysis
Finding: Star ratings (2.64/5 average) tell a different story than app store displays (4.4/5). We prioritized sentiment analysis over ratings to understand the emotional drivers of complaints.
3. Journey-Based Categorization
Approach: Rather than simple topic classification, we mapped reviews to customer journey stages:
- Authentication (Login/Password)
- Transaction (Bill Payment/Plan Changes)
- Information (Usage/Balance Checks)
- Support (Help/Contact)
4. Platform-Specific Analysis
Discovery: iOS users showed 84.2% negative sentiment vs 58.1% for Android, revealing platform-specific architectural issues rather than general app problems.
Quality Assurance
Data Integrity
100%
Verification rate of selected reviews
100%
iOS reviews analyzed
95%
Confidence level with ±0.8% margin
5%
Manual spot-checking rate
Analysis Validation
- AI Accuracy: Spot-checked 5% of categorizations manually
- Consistency: Cross-validated findings across multiple analysis passes
- Triangulation: Confirmed insights using review text, ratings, and CCTS data
Limitations and Considerations
1. Self-Selection Bias
- Reviews represent frustrated users, not general population
- Positive experiences likely underrepresented
- Mitigation: Clearly distinguished "breaking point" insights from general user experience
2. Temporal Variations
- Older reviews may reflect resolved issues
- Recent reviews weighted more heavily in recommendations
- App updates may have addressed some reported problems
3. Platform Constraints
- App store reviews have character limits
- Some technical details may be truncated
- Supplemented with CCTS data for complete picture
Deliverables
1. Quantitative Analysis
- Individual Analysis: 12,785 reviews individually analyzed and categorized
- Statistical Breakdowns: By provider, platform, and category
- Confidence Intervals: For all major metrics
2. Qualitative Insights
- Journey Failure Points: Identification of critical breaking points
- Edge Case Patterns: Recognition of recurring failure modes
- Strategic Recommendations: Based on prevention opportunities
3. Strategic Framework
- ROI Models: For edge case prevention initiatives
- Implementation Roadmap: With realistic timelines
- Competitive Differentiation: Strategies for market advantage
Data Collection
→
AI Analysis
→
Statistical Validation
→
Strategic Insights
→
Actionable Recommendations
This methodology ensures our findings are statistically robust, practically actionable, and strategically valuable for transforming Rogers' customer experience from reactive complaint management to proactive edge case mastery.
¹Industry estimate - requires verification with actual user base data