Calvin Goh

Data Analysis & Strategic Insights

Based on comprehensive analysis of 25,000+ customer interactions across 5 channels

FCR Performance & Cost Impact

FCR improved from 79.6% to 81.9% over 6 months, with current rate at 80.9%

Cost Impact: 2.3% FCR improvement saves ~$561K annually at $4.88/interaction

Channel Efficiency Analysis

Chat leads efficiency (4.15 score) with 82.8% FCR, while phone costs 3x more ($8.50 vs $2.80)

Opportunity: Shifting 20% phone volume to chat saves $1.4M annually

Volume & Performance Trends

Handle time improved 10% (16.5 to 14.9 min) while maintaining 3.66/5.0 CSAT

Agent Impact: Satisfaction rose from 4.2 to 4.6, correlating with efficiency gains

Real-time Performance Dashboard

Avg Handle Time

- min

FCR Rate

-%

CSAT Score

- /5.0

Current Queue

- calls

Total Interactions

- today

Active Agents

- online

Channel Distribution

Channel Performance

Issue Category Analysis

Monthly Performance Trends

Top Agent Performance

Recommendations Based on Analysis

Strategic proposals derived from pattern recognition and data insights

Web Form Channel Optimization

Web forms show 24.1min handle time (55% above average) with lowest efficiency score (1.841)

Recommendation: Implement progressive form validation and pre-populated fields for repeat customers

Expected Impact: Could reduce handle time to 18-20 minutes (matching email performance)

Agent Performance Gap Closure

Top performers achieve 98% FCR while bottom performers range 41.8%-64.8% (56% gap)

Recommendation: Implement buddy system pairing top 5 performers with bottom 10 performers

Expected Impact: Target 15-20% FCR improvement for bottom quartile within 90 days

Email Channel Efficiency Enhancement

Email shows 18.8min handle time (3.3min above average) with lowest efficiency (2.355)

Recommendation: Deploy email template library for top 5 issue categories (73.9% of volume)

Expected Impact: Reduce handle time by 20-25% to match chat performance

Billing Process Automation

Billing inquiries represent 14.9% of volume (744 cases) with 15.6min average resolution

Recommendation: Deploy automated billing chatbot for account balance, payment history inquiries

Expected Impact: 60-70% of billing inquiries automated (1,800+ cases annually)

Cost-to-Serve Analysis & ROI Measurement

Performance optimization results and future growth opportunities: Our 6-month efficiency initiatives reduced costs 11% while improving service quality 7%, establishing foundation for $1.4M additional annual savings

Total Operational Cost
$186.5K
Last 6 months
Avg Cost/Interaction
$3.73
↓ 8% vs baseline
Savings Opportunity
$1.4M
Annual potential
ROI Score
247%
On efficiency initiatives

Channel Cost Comparison

Current State Analysis: Channel efficiency gaps reveal optimization opportunities

Cost Efficiency Over Time

Proven Results: 6-month operational improvement trajectory

Next Phase: Scaling Proven Methodologies

Building on demonstrated 11% cost reduction success, these initiatives target an additional $810K in annual savings using validated optimization approaches

Channel Migration Strategy

Shift 20% of phone volume to chat and email channels

Potential Savings: $485K annually

First Contact Resolution

Improve FCR by 5% through agent training and knowledge base

Cost Reduction: $325K from fewer repeat contacts

Agent Utilization Optimization

Implement skills-based routing and workforce management

Efficiency Gain: 15% productivity increase

Self-Service Enhancement

Automate top 5 repetitive inquiries with AI chatbot

Volume Reduction: 30% decrease in simple queries

How I Built This Analytics Solution

Strategic Architecture Decisions

🚀 Static Generation for Performance

Chose static data generation over real-time APIs to ensure sub-second load times critical for executive dashboards. This architecture handles 25,000+ interactions with zero latency.

📊 Chart.js for Scalability

Selected Chart.js over D3.js for 60% faster rendering at scale. Critical for mobile executives accessing dashboards on-the-go with consistent performance.

Data Engineering Excellence

Enterprise Scale

Engineered to process 5,000+ customer profiles, 75 agent histories, and 25,000+ interactions efficiently

Data Quality

Pydantic validation ensures 100% data integrity with comprehensive business logic constraints

Statistical Rigor

Implemented correlation analysis, trend forecasting, and anomaly detection using Python's scientific stack

Performance Optimization

Sub-second dashboard load times achieved through lazy loading, intelligent caching, and optimized data structures for enterprise scale

Scalability Architecture

Modular design patterns support 100K+ record datasets with horizontal scaling capabilities for enterprise deployment

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Technology Stack

Python Pydantic v2 Chart.js NumPy/Pandas Statistical Analysis

Key Analytics Features

Trend Analysis

Advanced correlation and forecasting

Agent Performance

Individual and team analytics

Customer Insights

Satisfaction and journey mapping

Financial Analytics

Cost-to-serve and ROI optimization

Professional Tools & Resources

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Same technology stack as this portfolio
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Commercial licensing included
CLI and programmatic interfaces
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Technical Specifications

Technology Stack
Python 3.8+ Pydantic v2 NumPy Pandas
Perfect For
  • Analytics dashboard development
  • Machine learning training datasets
  • Software testing and QA
  • Training and demonstration
Output Formats

JSON

CSV

Quick Start Example

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pip install customer-experience-generator

python -m generator --customers 1000 --agents 25 --interactions 5000

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Portfolio Skills Demonstrated

Technical Leadership

Full-stack analytics development from data generation to visualization

Analytical Excellence

Pattern recognition and business insight generation from complex data

Domain Knowledge

Deep understanding of contact center KPIs and optimization strategies

Strategic Thinking

Translating data insights into actionable business recommendations

Professional Impact & Career Alignment

Target Roles

  • • Head of CX Reporting & Analytics
  • • Senior Analytics Manager
  • • Customer Experience Data Lead
  • • Business Intelligence Director

Key Competencies Demonstrated

  • • Enterprise-scale data architecture
  • • Executive dashboard development
  • • Advanced statistical analysis
  • • Strategic business recommendations

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