RESULTS
>90%
model accuracy
4.6x
greater model lift vs. contractual thresholds
11
data ingestion flows
300+
QA checks
Executive Summary
A leading European energy company lacked visibility into customer churn risk. EPAM and AWS built an AI-driven data platform and machine learning (ML) model that enabled proactive retention with 90%+ prediction accuracy.
SERVICES
- Artificial Intelligence
STRATEGIC PARTNER
- Amazon Web Services (AWS)
INDUSTRY
- Energy & Resources
CUTTING CUSTOMER CHURN
Leveraging AI to Solve the Energy Retention Gap
Retaining customers is a challenge in the competitive and highly regulated energy sector. To maintain its market position, a leading European conglomerate required advanced analytics to better understand customer behavior and improve service delivery. The company lacked a unified data platform to transform raw demographic and transactional data into predictive insights, resulting in the inability to identify at-risk customers. This lack of visibility in churn drivers hindered the effectiveness of marketing campaigns and prevented proactive customer retention strategies.
The company turn to EPAM and AWS for help in harnessing AI for predictive insights. Leveraging AWS cloud data and analytics services, we developed ML models to pinpoint and ultimately prevent churn among electricity customers. We set out to deliver high-quality, data-driven insights in a scalable data and analytics platform. By identifying the reasons behind customer churn and suggesting actions to reduce it, we delivered immediate ROI — achieving over 90% accuracy in churn prediction.
MAKING IT REAL
Using ML to Compel Insights from Data
Building a predictive model using ML is not just a technical challenge — the insights also must be accessible and useful. If marketers can’t easily employ the predictions to tailor customer retention campaigns, the data can’t deliver results.
We tackled the challenges with a strategic, holistic approach. AWS services were key to the project, bringing high availability, scalability, automation and speed. By building a robust, AI-powered data pipeline, we created a unified view of customer behavior, enabling real-time, data-driven decision making.
Approach Highlights
Data Governance
Single Source of Truth
L Models on AWS SageMaker
Useful, Actionable Insights
FROM POC TO PRODUCTION
Applying ML to Keep Customers Happy
Over the course of a year, we moved from a successful proof of concept (POC) to a real-world ML model that is in production, providing reports to marketers that show the probability of individual customer churn over the subsequent two months.
Our preliminary successes include:
ML Model Accuracy:
Achieved more than 90% model accuracy (contractual expectation: 65-70%)
ML Model Robustness:
Realized 4.6x greater model lift compared to contractual thresholds
ML Model Inputs:
Implemented 11 data ingestion flows with an average of 92 daily execution files
ML Model Testing:
Executed 300+ QA checks and created 62 QA test cases
THE FUTURE IS PERSONALIZED
Better Marketing with ML
With the churn model up and running, we sought to identify a minimum of 20% of the client's total customers that were most likely to take their business elsewhere, i.e., “churners.” The marketing team tested campaigns, dividing those customers identified as likely churners into a test group and a control group. The test group received tailored marketing campaigns (including gamification features) and the control group did not. The team observed an immediate improvement in retention rates for those customers receiving marketing attention.
Going forward, the client’s marketing teams can now better tailor ads and other campaigns to individual customers, a more cost-effective form of customer retention. In the future, we will continue to work on broader, data-driven customer segmentation to support targeted campaigns and a more personalized customer journey. We also plan to collaborate with our client to use ML to predict customer churn in gas utilities.
While EPAM gained new insight into the energy industry, our work also proved that AI-driven personalization can reduce customer churn — experience we can now use across industries.
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