MerSci helps utilities use all relevant data sources that underpin machine learning models to enhance grid asset management and forecasting systems, boost energy-efficiency initiatives, and enrich customer service offerings and engagement, all through real-time predictive insights.
MerSci helped a major European utility company which provides gas and electricity to corporate, SME and residential customers with a high customer defection rate in post-liberalization years of the energy market in Europe to identify the drivers of this problem and to devise and implement a strategy to counter it.
Leveraging the company’s internal data sources (CRM, power usage, pricing, etc.), in addition to external sources such as social media and 3rd party customer satisfaction surveys, MerSci was able to identify the most explicative variables for churn and test some of the prevailing assumptions such as the existence of the correlation between subscribed power and consumption and linkage between channel sales and churn.
An integrated ML-based solution was developed to assign risk scores to the propensity of churn to the customers and recommend customer-centric solutions to minimize the churn rate.
The integrated analytics pipeline with an accompanied dashboard provided a real-time notification system that enabled the company identify the customers at risk of churn and suggest the most efficient preventive measures to lower the risk of churn which resulted in 66% reduction in customer attrition rate.
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