Case Study: Eni achieves stronger long-term customer value and increased profitability with SAS Analytics

Energy company Eni uses analytics to estimate customers’ long-term value on an individual basis

Eni, a global integrated energy company active in 69 countries and the No. 3 supplier in Belgium with over 800,000 retail and 50,000 B2B connections, needed to predict the long-term value of each customer and understand the drivers behind revenue, retention, payment risk and service cost. The challenge was to identify which customers to invest in, which posed credit or service risks, and how to support strategic decisions in a highly competitive market.

Working with Python Predictions and using SAS Analytics, Eni built predictive models with more than 700 parameters, integrated diverse data sources, and deployed user-friendly dashboards and “what-if” scenarios. The solution improved resource allocation, strengthened customer relationships, enabled data-driven pricing and churn strategies, and positioned Eni to boost long-term profitability while scaling for future smart-energy data.

Eni

Zdravka Jevtimov

Customer Insights Manager