AI-Powered Product Analytics • Feature Adoption Intelligence • User Engagement Analytics • Behavioral Segmentation
Product Intelligence Dashboard for Feature Adoption Analytics, User Engagement Monitoring & Product Decision Intelligence
This project simulates a real SaaS product analytics workflow where user interaction and feature adoption behavior are analyzed to generate actionable product intelligence insights.
The system helps analyze:
- feature adoption trends
- user engagement patterns
- behavioral segmentation
- product usage intelligence
- retention-oriented user behavior
The dashboard combines:
- feature usage analytics
- RFM-based behavioral scoring
- KMeans user segmentation
- product KPI monitoring
- interactive product intelligence dashboards
Built using:
- Streamlit
- Pandas & NumPy
- Plotly
- Scikit-learn
- KMeans Clustering
- Product feature adoption analysis
- User engagement monitoring
- Feature usage intelligence
- RFM behavioral analytics
- User segmentation workflows
- Product KPI monitoring
- Retention-focused analytics
- Interactive product dashboards
- End-to-end product analytics workflow
- Synthetic clickstream data generation
- Feature adoption intelligence
- RFM behavioral analysis
- KMeans user segmentation
- Interactive multi-page Streamlit dashboard
- Automated model evaluation
- Pytest-based testing
- Modular production-style architecture
Modern digital products generate massive volumes of event-level user interaction data. However, organizations often struggle to:
- identify which features are actively used
- understand user engagement behavior
- improve feature adoption rates
- monitor active vs inactive users
- prioritize product roadmap decisions
- optimize product-led growth strategies
Without product intelligence systems, organizations may face:
- low feature adoption visibility
- weaker retention understanding
- inefficient product prioritization
- reduced user engagement
- poor behavioral analytics visibility
This project demonstrates how analytics-driven product intelligence can support:
- product growth optimization
- engagement-focused product strategies
- feature adoption improvement
- retention intelligence workflows
This platform helps organizations analyze:
- feature adoption trends
- user engagement behavior
- retention-oriented user activity
- behavioral usage patterns
- active vs inactive user groups
- product usage intelligence
The platform demonstrates how product analytics workflows can improve:
- product decision-making
- feature prioritization
- engagement optimization
- user retention strategies
- product-led growth workflows
| Module | Function |
|---|---|
| 🏠 Home Page | Project overview & navigation |
| 📊 Overview Dashboard | Product KPIs & engagement metrics |
| 📈 Feature Usage Analytics | Feature adoption & usage trends |
| 🧮 RFM Segmentation | Behavioral user segmentation & clustering |
The analysis revealed several important product usage patterns:
- Dashboard emerged as the most actively used feature.
- API & Integration features showed lower adoption rates.
Power users demonstrated:
- low recency
- high frequency
- strong engagement intensity
At-risk users showed:
- higher inactivity periods
- declining engagement frequency
The KMeans model successfully separated users into:
- Cluster 0 — Power Users
- Cluster 1 — Regular Users
- Cluster 2 — At-Risk Users
- Cluster 3 — Dormant Users
These insights support:
- product roadmap planning
- re-engagement campaigns
- feature onboarding strategies
- retention-focused analytics
Synthetic Product Usage Data
↓
Data Cleaning & Preprocessing
↓
RFM Feature Engineering
(Recency • Frequency • Monetary)
↓
KMeans User Segmentation
↓
Behavioral Product Intelligence
↓
Interactive Streamlit Dashboard
↓
Product & Growth Decision Intelligence
| Category | Technologies |
|---|---|
| Language | Python 3.10+ |
| Data Analysis | Pandas, NumPy |
| Machine Learning | Scikit-learn, KMeans |
| Visualization | Plotly |
| Dashboard/UI | Streamlit |
| Analytics | RFM Segmentation |
| Testing | PyTest |
Product-Feature-Usage-Intelligence/
│
├── app/
│ ├── Home.py
│ └── pages/
│ ├── 1_Overview.py
│ ├── 2_Feature_Usage.py
│ └── 3_RFM_Segments.py
│
├── data/
│ ├── raw/
│ └── processed/
│
├── models/
│ └── kmeans_rfm.pkl
│
├── scripts/
│ ├── generate_synthetic_data.py
│ ├── preprocess_data.py
│ ├── build_rfm.py
│ └── train_model.py
│
├── screenshots/
│ ├── home.png
│ ├── overview.png
│ ├── feature_usage.png
│ └── rfm_segments.png
│
├── src/
│ ├── evaluate_model.py
│ ├── __init__.py
│ ├── preprocessing.py
│ ├── rfm.py
│ └── viz.py
│
├── tests/
│ └── test_predict.py
│
├── .gitignore
├── README.md
└── requirements.txt
git clone https://github.com/girishshenoy16/Product-Feature-Usage-Intelligence.git
cd Product-Feature-Usage-Intelligencepython -m venv venv.\venv\Scripts\Activate.ps1venv\Scripts\activatepython.exe -m pip install --upgrade pip
pip install -r requirements.txtpython scripts/generate_synthetic_data.pypython scripts/preprocess_data.py
python scripts/build_rfm.pypython scripts/train_model.pypython src/evaluate_model.pypytest
pytest -v
pytest -qstreamlit run app/Home.pyYour dashboard opens at:
Monitor:
- total users
- average events per user
- engagement KPIs
- RFM cluster summaries
Analyze:
- feature adoption trends
- time-series usage behavior
- feature-level engagement patterns
Explore:
- behavioral user clusters
- engagement segmentation
- recency-frequency-monetary insights
The KMeans segmentation model was evaluated using:
- Silhouette Score
- Inertia
- Cluster-level RFM analysis
The evaluation workflow helps validate:
- cluster quality
- user behavior separation
- segmentation consistency
- Churn prediction integration
- Cohort retention heatmaps
- Real-time usage ingestion pipelines
- Product funnel analytics
- Feature correlation intelligence
- User journey visualization
- Cloud deployment workflows
- Product experimentation analytics
Contributions, suggestions, and improvements are welcome.
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