Customer Clustering for Debt Recovery & Retention
Energy Sector
10% Improvement in Debt Collection + 40% Increase in Targeted Campaign Effectiveness
10% Improvement in Debt Collection
40% Increase in BTL Campaign Effectiveness
Core Element in Retention Strategy
Introduction
As part of the strategic evolution from DataSphere to Simov Labs, this case demonstrates a proven Big Data and Machine Learning intervention that transformed debt collection and customer retention for a leading energy distributor through advanced customer segmentation.
Business Challenge
A major energy distributor struggled with ineffective debt collection due to irregular payment patterns and high delinquency not being properly identified. This reduced the success of recovery campaigns. There was a lack of granular customer segmentation to understand behavioral differences and specific needs. Analytical insights were not integrated into the company's data infrastructure, resulting in low adoption across business units and limiting data-driven decision-making.
Implemented Solution
- 1Developed Machine Learning-based customer clustering models using Hadoop and Python to identify distinct segments, including a high-delinquency subgroup.
- 2Integrated clustering results into the customer dimension of the existing Data Warehouse for easy, cross-functional access by all business units.
- 3Provided functional and technical support to identify actionable strategies and quantify potential impact.
- 4Delivered visualizations and reporting using Power BI connected to Oracle Database, enabling commercial and financial teams to interpret and act on the segments effectively.
Obtained Results
10% improvement in debt collection through better identification of delinquent clients and optimized recovery strategies.
40% increase in effectiveness of BTL (below-the-line) communication campaigns by targeting the right customer segments.
Strengthened retention strategy: The clustering model became a foundational element for preventive and personalized retention actions.
Boosted data adoption: Integration into the Data Warehouse increased usage of customer and cluster-related cubes across the organization, advancing the company's overall data strategy.
Evolution in Simov Labs
This high-impact segmentation project is now fully part of our portfolio at Simov Labs. We evolve these Big Data and ML clustering approaches into modern, cloud-native implementations (Snowflake, Databricks, BigQuery) with production-ready AI/ML for dynamic segmentation, real-time behavioral prediction, automated retention scoring, and deeper ROI measurement.
Aligned with DAMA and TDWI governance standards, our current solutions emphasize business-first analysis, radical honesty in opportunity assessment, and true partnership—ensuring segmentation delivers sustainable competitive advantage in areas like collections, retention, and personalized engagement. No promises. Just results.
Ready to unlock actionable customer segments and drive measurable improvements in collections and retention?
Book a free 30-minute consultation. We'll discuss your segmentation goals and where data can deliver the most impact.