A business intelligence dashboard built around the exact KPIs that call center operations teams track daily (AHT, CSAT, abandon rate, FCR) with a predictive layer that forecasts performance before problems escalate.
Built with Python, scikit-learn, and Streamlit. Deployed on Streamlit Cloud.
- Executive Overview — 4 KPI cards (AHT, CSAT, abandon rate, FCR) with delta vs previous period and shift-level breakdown
- Trend Analysis — Daily time series with configurable rolling averages
- Agent Performance — Ranked composite score table with tier classification (Top / Mid / Risk) and coaching flags
- ML Predictor — Random Forest model forecasts tomorrow's abandon rate from operational inputs
- Queue × Monday interaction is the strongest predictor of abandon rate (feature importance: 0.363) — the backlog effect at week start is the dominant driver
- Day of week is the second most important feature (0.254) — temporal patterns outperform agent-level features for abandon rate prediction
- Queue depth alone accounts for 0.243 importance — operational implication: staffing decisions should be driven by queue forecasting, not historical averages
- Monday morning abandon rates are consistently 40–60% above weekly average
- Night shift CSAT is 0.3–0.5 points below morning shift average — training gap or fatigue signal
- Agent experience correlates strongly with FCR but plateaus on AHT after 12 months
Algorithm: Random Forest Regressor + Linear Regression (baseline)
Target: Abandon rate (continuous regression)
Features: 12 engineered features
Validation: 5-fold cross-validation
Train/Test: 80/20 split (2,112 train / 528 test)
RF R²: 0.919
RF RMSE: 0.0150
RF MAE: 0.0120
LR R²: 0.922
CV R² (mean): 0.908 ± 0.005
Baseline RMSE: 0.0528
Improvement: 3.5x RMSE reduction vs naive baseline
Top features by importance:
| Feature | Importance |
|---|---|
| queue × monday interaction | 0.363 |
| day_of_week | 0.254 |
| calls_in_queue | 0.243 |
| is_monday | 0.133 |
| calls_handled | 0.002 |
All 12 features: day_of_week · is_monday · is_night_shift · shift_encoded · calls_in_queue · aht_seconds · experience_months · calls_handled · csat_score · fcr_rate · queue×monday interaction · queue×night interaction
| Property | Value |
|---|---|
| Rows | 2,640 |
| Agents | 20 |
| Date range | July–December 2024 (6 months) |
| Grain | Agent × day |
| Simulation | Realistic distributions with temporal patterns |
Simulation logic: Monday morning backlog effect · Friday afternoon rush · Night shift fatigue penalty · Senior agent FCR/AHT improvement curves · Queue depth → abandon rate correlation
callcenter-analytics/
├── app.py Main Streamlit application (4 pages)
├── generate_data.py Dataset simulation script
├── train_model.py Model training + evaluation
├── kpi_calculator.py KPI aggregation functions
├── models/
│ ├── regressor.pkl Trained models (RF + LR, serialized)
│ └── reg_metrics.json Evaluation metrics
├── callcenter_data.csv Simulated dataset
├── README.md
└── requirements.txt
git clone https://github.com/diegopalencia-research/callcenter-analytics.git
cd callcenter-analytics
pip install -r requirements.txt
python generate_data.py # creates callcenter_data.csv
python train_model.py # trains model, saves to models/
streamlit run app.py # launch dashboard| KPI | Definition | Industry Target |
|---|---|---|
| AHT | Average Handle Time — talk + hold + wrap-up time per call | < 300 sec (5 min) |
| CSAT | Customer Satisfaction Score — post-call survey 1–5 | > 4.2 |
| Abandon Rate | % callers who hang up before reaching an agent | < 5% |
| FCR | First Call Resolution — % resolved without callback | > 70% |
This dashboard operationalizes findings from:
Palencia, D. (2024). Computational Feature Extraction for Human Performance Prediction. OSF Preprints.
The call center context serves as an empirical domain for testing whether temporal and behavioral operational features can predict quality outcomes at the individual agent level a research question extending the phonological prediction framework from Project 01.
Live App: https://callcenter-analytics.streamlit.app/ · GitHub: https://github.com/diegopalencia-research/callcenter-analytics
Diego José Palencia Robles Data Science & NLP Projects — Applied AI & Analytics + Machine Learning
- GitHub; @diegopalencia-research: https://github.com/diegopalencia-research
- LinkedIn: https://www.linkedin.com/in/diego-jose-palencia-robles/
MIT License