Case Study — Decision-Focused Energy Retail Analytics
- Domain: Energy Retail Analytics
- Focus: Customer Segmentation & Collection Risk
- Data Size: ~2.7M records
- Tools: Python, Pandas, Seaborn
- Output: Decision-oriented operational insights
This project analyzes energy consumption behavior, customer segmentation, and payment collection risk to generate decision-support insights for operational teams.
The analysis enables organizations to:
- Understand district-level consumption differences
- Identify high-value and high-risk customer segments
- Prioritize collection strategies using measurable financial impact
- Support operational planning through data-driven insights
The project is structured as a three-notebook analytical pipeline: Data Exploration → Visualization → Decision Storytelling
Energy retail companies face several operational challenges:
- Consumption patterns and customer structures differ significantly across districts
- Applying the same collection strategy to all customers reduces operational efficiency
- The combination of high consumption and delayed payment represents the highest financial risk
This project provides a data-driven answer to:
Which customer segment and which district require which operational action?
The analytical workflow consists of three complementary layers:
- District-level consumption intensity and customer density analysis
- Customer class distribution comparison
- Seasonality and extreme consumption behavior analysis
- District profile scorecard creation
- Customers segmented based on consumption level and payment behavior
- Segment size and characteristics analyzed
- “High Consumption + Late Payment” segment clearly isolated
- Identification of drivers influencing payment behavior
- Risk concentration analysis by district, account class, and consumption segment
- Evaluation of payment channels together with collection risk
Focus
- Data structure validation (
.info,.describe,.head) - Customer count comparison by district
- Dataset merging and record validation
- Missing, negative, and extreme value detection
- Consumption statistics by account class
- Export of cleaned datasets for downstream analysis
Outputs
- Cleaned datasets saved as intermediate files (pickle format)
Focus
- District-level account class distributions
- Seasonal consumption trends
- Collection channel and district distributions
- On-time vs late payment ratios
- Consumption distribution and outlier visualization
Outputs
- Analytical charts supporting comparative insights
Focus
- District comparison analysis and drivers
- Customer segmentation and behavioral interpretation
- Collection risk identification
- Business-oriented action recommendations
Structure Problem Definition → Hypotheses → Analysis → Findings → Business Recommendations
- Consumption and customer profiles vary significantly across districts
- The High Consumption + Late Payment segment represents the most critical financial risk
- Customer class and profile strongly influence payment behavior
- Segment-based collection strategies significantly improve operational efficiency
This analysis enables operational teams to:
- Identify high-risk customer segments early
- Prioritize collection and communication resources based on financial impact
- Design segment-specific payment incentives (auto-pay, installment plans, reminders)
- Support district-level operational planning with data-driven evidence
Example Action Maps
- High Consumption + Late Payment: early warning alerts, auto-pay incentives, structured payment plans
- High Consumption + On-Time Payment: loyalty programs, auto-pay adoption expansion
- Low/Medium Consumption + Late Payment: low-cost reminder systems, online/bank payment channel promotion
case_study_02_energy-consumption-customer-risk-analytics/
├── README.md
├── data/
│ └── elektrik_veri_hashed.xlsx
├── notebooks/
│ ├── notebook_01_data_exploration.ipynb
│ ├── notebook_02_visualization.ipynb
│ └── notebook_03_decision_storytelling.ipynb
├── outputs/
│ └── figures/
│ ├── 01_district_account_structure.png
│ ├── 02_monthly_consumption_trend.png
│ ├── 03_payment_status_distribution.png
│ ├── 04_district_account_class_consumption.png
│ ├── 05_district_scorecard.png
│ ├── 06_customer_segment_distribution.png
│ ├── 07_segment_avg_consumption.png
│ ├── 08_payment_behavior_by_segment.png
│ ├── 09_consumption_by_payment_boxplot.png
│ ├── 10_bank_collection_rate_by_district.png
│ └── 11_late_payment_rate_by_district_segment.png
└── requirements.txt
The original dataset contains sensitive operational information and is not publicly shared.
All customer identifiers are anonymized/hashed, and therefore the dataset included in this repository is either anonymized or excluded for privacy reasons.
pip install -r requirements.txt-
notebook_01_data_exploration.ipynb - Performs data validation and cleaning - Exports intermediate cleaned datasets
-
notebook_02_visualization.ipynb
- Uses cleaned datasets to produce comparative visualizations
-
notebook_03_decision_storytelling.ipynb
- Generates decision-oriented analytical storytelling and operational recommendations
- Python: pandas, numpy
- Visualization: matplotlib, seaborn
- Environment: Jupyter Notebook
This project demonstrates how energy retail companies can identify high-risk customer segments and prioritize collection strategies through data-driven segmentation and consumption analytics.
The insights support more efficient operational planning and measurable financial impact.
The project illustrates how consumption analytics, customer segmentation, and payment behavior modeling can be integrated into a scalable decision-support framework.
Such frameworks can be operationalized through automated analytical pipelines and decision dashboards to support real-world collection prioritization strategies.