An AI-powered customer analytics platform that transforms raw customer data into actionable business insights through machine learning, interactive dashboards, and automated analytics.
Thato Maelane
Amarachi Florence
- Philip Odiachi
- Mavis
Developed during the DataVerseAfrica Internship – Cohort 3
The African Customer Intelligence Platform is an AI-powered business analytics solution designed to help organizations quickly uncover valuable insights from customer data.
Instead of spending days creating reports or manually analyzing spreadsheets, users can upload customer datasets and instantly receive interactive dashboards, predictive analytics, customer segmentation, sentiment analysis, geographic insights, and strategic business recommendations.
Although initially developed using banking customer data, the platform is flexible enough to support industries such as:
- Banking
- FinTech
- Telecommunications
- Retail
- Insurance
- Healthcare
- Customer Service
- E-commerce
- Transform raw customer data into actionable business intelligence.
- Identify customer behavior and trends.
- Detect high-risk customer segments.
- Measure customer sentiment.
- Analyze digital adoption.
- Discover high-value customer locations.
- Support strategic business decision-making.
- Deliver insights through an easy-to-use web application.
- Customer overview
- KPI summaries
- Business performance indicators
- Interactive charts
- Customer segmentation
- Risk analysis
- Customer profiling
- Trend detection
- Predictive analytics
Analyze customer feedback to identify:
- Positive sentiment
- Neutral sentiment
- Negative sentiment
Helping organizations understand customer satisfaction.
Visualize customer distribution across different regions to identify:
- High-value customer locations
- Regional opportunities
- Market concentration
Measure customer engagement with digital services including:
- Mobile application usage
- Digital banking adoption
- Customer activity trends
The platform automatically generates business recommendations based on customer behaviour and analytics.
Examples include:
- Improve customer retention
- Increase digital engagement
- Reduce customer churn
- Target high-value customers
- Improve customer satisfaction
Using over 5,200 customer records, the platform identified:
- Approximately 40% of customers were classified as high risk.
- Only 25% actively used the mobile application.
- Customer complaints negatively affected overall sentiment.
- High-value customers were concentrated in major cities such as Lagos and Abuja.
- Upload customer dataset
- Data preprocessing
- Data validation
- Feature engineering
- Customer analytics
- Machine learning predictions
- Visualization generation
- Business recommendation engine
- Dashboard presentation
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Plotly
- Scikit-learn
- XGBoost
- Streamlit
- Power BI
- FastAPI / REST API
African-Customer-Intelligence-Platform/
│
├── README.md
├── app.py
├── api.py
├── model.pkl
├── customer_dataset.csv
├── presentation.pptx
├── project_report.pdf
├── requirements.txt
├── Images/
└── Models/
- Customer Segmentation
- Customer Risk Analysis
- Customer Lifetime Insights
- Geographic Distribution
- Sentiment Analysis
- Digital Adoption Analysis
- Customer Behaviour Analysis
- Executive Dashboard
- Automated Business Recommendations
- Data Collection
- Data Cleaning
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Model Development
- Model Evaluation
- Dashboard Integration
- API Development
- Streamlit Deployment
- Business Intelligence Reporting
As the Project Leader, my responsibilities included:
- Leading a multidisciplinary team throughout the project lifecycle.
- Coordinating project planning and task allocation.
- Designing the machine learning workflow.
- Guiding data preprocessing and feature engineering.
- Integrating analytics into the Streamlit application.
- Ensuring successful collaboration and timely project delivery.
- Presenting project outcomes and strategic recommendations.
- Team Leadership
- Project Management
- Machine Learning
- Predictive Analytics
- Customer Analytics
- Business Intelligence
- Data Visualization
- Exploratory Data Analysis (EDA)
- Feature Engineering
- API Development
- Dashboard Development
- Streamlit
- Power BI
- Python Programming
- Model Deployment
pandas
numpy
matplotlib
seaborn
plotly
scikit-learn
xgboost
streamlit
fastapi
joblib- Real-time customer monitoring.
- Cloud deployment on AWS or Azure.
- Integration with SQL databases.
- Customer churn prediction.
- Recommendation system.
- Fraud detection module.
- Role-based authentication.
- AI chatbot for business insights.
- Automated report generation.
- Multilingual support for African markets.
https://african-financial-risk-dashboard-d.streamlit.app/
Thato Maelane (Project Leader)
Bachelor of Engineering Technology (Electrical Engineering)
Tshwane University of Technology
Amarachi Florence (Assistant Leader)
Philip Odiachi
Mavis