Retaining Customers with the Help of Machine Learning
Retaining Customers with the Help of Machine Learning
At a glance
CLIENT
CLIENT
STRATEGIC PARTNER
- STRATEGIC PARTNER
SERVICE
- SERVICE
INDUSTRY
- INDUSTRY
In partnership with AWS, EPAM helped a large European company utilize artificial intelligence (AI) to better serve customers. We transformed the company’s data into useable input for a machine learning (ML) model to predict customer churn with more than 90% accuracy. Then, we delivered highly visual analytics, so the marketing team could tailor campaigns to meet its customer retention goals.
Leveraging AI to Focus on Customers
Retaining customers is a challenge in the competitive and highly regulated energy sector. With manual processes for retention, such as phone calls, proving inefficient, a leading European conglomerate turned to EPAM and AWS to harness 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.
Data Governance
Data Governance
RESULTS
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 Accuracy: Achieved more than 90% model accuracy (contractual expectation: 65-70%)
ML Model Accuracy: Achieved more than 90% model accuracy (contractual expectation: 65-70%)
ML Model Accuracy: Achieved more than 90% model accuracy (contractual expectation: 65-70%)
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.
PARTNER WITH US
Want to harness AI to boost customer retention? Discover how EPAM and AWS can help you turn data into actionable insights.