CX Intelligence Platform

End-to-end Customer Experience Analytics platform designed to transform omnichannel customer service data into actionable business insights.

This project simulates a large-scale enterprise customer support operation and demonstrates modern Data Engineering, Data Analytics, Business Intelligence, and dimensional modeling practices.

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Project Overview

The CX Intelligence Platform was developed to simulate a real-world Customer Experience environment, enabling the ingestion, transformation, modeling, and analysis of customer support interactions across multiple service channels.

The solution follows a complete analytics workflow:

CSV Dataset
    ↓
Staging Layer
    ↓
ETL Process
    ↓
Dimensional Data Warehouse
    ↓
Semantic Layer
    ↓
Power BI Dashboard

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Business Scenario

A large enterprise seeks to improve customer experience performance by understanding operational trends across its customer support organization.

The project enables the analysis of:

• Customer Satisfaction (CSAT)
• Resolution Performance
• Interaction Volume
• Reopen Rate
• Average Handle Time (AHT)
• Agent Performance
• Channel Effectiveness
• Category Trends

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Project Goals

• Build a dimensional data warehouse using Star Schema modeling
• Implement repeatable ETL processes
• Create business-oriented analytical views
• Develop executive and operational KPIs
• Support Power BI dashboard development
• Demonstrate end-to-end analytics architecture

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Technology Stack

Data Engineering

• SQL Server Express
• T-SQL
• ETL Processes
• Star Schema Modeling
• Dimensional Data Warehouse

Analytics & BI

• Power BI
• DAX
• KPI Development
• Business Analytics

Data Generation

• Python
• Pandas
• Synthetic Data Generation

Version Control

• Git
• GitHub

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Repository Structure

cx-intelligence-platform/

├── sql/
│   ├── 01_database_schema.sql
│   ├── 02_data_ingestion.sql
│   ├── 03_semantic_layer.sql
│   └── 04_data_quality_checks.sql
│
├── sample_data/
│
├── Documentation/
│   ├── architecture.md
│   ├── data_dictionary.md
│   ├── powerbi_design.md
│   └── root_cause_analysis.md
│
└── README.md

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Data Model

The solution follows a Star Schema architecture.

Fact Table

• Fact_Interactions

Dimension Tables

• Dim_Agent
• Dim_Channel
• Dim_Category
• Dim_Calendar

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Semantic Layer

The project exposes business-ready analytical views for reporting and dashboard consumption.

Available Views

• vw_CustomerExperience
• vw_AgentPerformance
• vw_ChannelPerformance
• vw_CategoryPerformance
• vw_ExecutiveDashboard

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Key Performance Indicators

Customer Satisfaction (CSAT)

Average customer satisfaction score.

Average Handle Time (AHT)

Average interaction duration.

Resolution Rate

Percentage of interactions successfully resolved.

Reopen Rate

Percentage of interactions reopened after resolution.

Interaction Volume

Total interactions by period, channel, category, or agent.

Agent Performance

Operational performance indicators by agent and team.

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Data Quality

The project includes validation scripts to ensure:

• Referential integrity
• Data completeness
• Consistency checks
• ETL validation

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Sample Dataset

A synthetic dataset was generated using Python to simulate real-world customer support operations.

Dataset characteristics:

• 20,000 interactions
• Multiple support channels
• Multiple support categories
• Resolution tracking
• Reopen indicators
• Customer satisfaction scores
• Agent performance data

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Documentation

Additional project documentation is available in the /Documentation folder:

• Architecture Overview
• Data Dictionary
• Power BI Dashboard Design
• Root Cause Analysis Framework

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Author

Maurício Farias Machado

Data Analytics | Business Intelligence | Data Engineering