Essent: a case study
Company Size
1,000+
Region
- Europe
Country
- Netherlands
Product
- Now Interact Predictive Intelligence
- Now Interact Proactive Call-Back
Tech Stack
- Predictive Intelligence
- Machine Learning Algorithms
Implementation Scale
- Pilot projects
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Utilities
Applicable Functions
- Sales & Marketing
Services
- Data Science Services
- System Integration
About The Customer
Essent is the largest energy company in the Netherlands, serving over 2.3 million gas and 2 million electric customers. Known for its forward-thinking approach, Essent was the first European company to launch a commercial fast-charge station for electric vehicles. The company is committed to innovation, particularly in enhancing customer experience through advanced contact channel technologies. Essent partnered with Now Interact, an omnichannel vendor specializing in Predictive Intelligence, to transform its sales and service operations. The goal was to create more personalized customer experiences and improve the efficiency of its contact centers.
The Challenge
Essent, the largest energy company in the Netherlands, wanted to enhance its customer experience and increase sales by offering a call-back service to consumers looking to purchase gas or electricity. However, the challenge was to implement this service in a cost-effective manner. Static call-back options were not efficient as they were visible to all online visitors, including those who did not need offline assistance, leading to unnecessary costs. Essent needed a solution that could intelligently identify and engage only those customers who would benefit from a call-back, thereby optimizing resource utilization and improving sales conversion rates.
The Solution
Essent collaborated with Now Interact to implement a proactive call-back system powered by Predictive Intelligence. Initially, a split test was conducted between static and proactive call-back options. The static call-back was always visible to all online visitors, while the proactive call-back used machine-learning algorithms to identify and engage only those customers who would benefit from offline assistance. The proactive call-back system analyzed data to understand the intent of each online visitor, automatically determining whether a call-back would be beneficial. This approach ensured that only the most valuable customers, who were likely to abandon their online purchase without assistance, were targeted. The trial ran for eight weeks, during which the performance of both static and proactive call-back systems was monitored. The results demonstrated that the proactive call-back significantly outperformed the static option, leading to higher sales conversions and better resource utilization.
Operational Impact
Quantitative Benefit
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