实例探究 > Essent: a case study

Essent: a case study

公司规模
1,000+
地区
  • Europe
国家
  • Netherlands
产品
  • Now Interact Predictive Intelligence
  • Now Interact Proactive Call-Back
技术栈
  • Predictive Intelligence
  • Machine Learning Algorithms
实施规模
  • Pilot projects
影响指标
  • Cost Savings
  • Customer Satisfaction
  • Revenue Growth
技术
  • 分析与建模 - 预测分析
  • 应用基础设施与中间件 - 数据交换与集成
适用行业
  • 公用事业
适用功能
  • 销售与市场营销
服务
  • 数据科学服务
  • 系统集成
关于客户
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.
挑战
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.
解决方案
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.
运营影响
  • The proactive call-back system successfully bridged the gap between online and offline contact center operations, transforming the phone into a digital tool.
  • Essent was able to offer a more personalized customer experience by intelligently identifying and engaging customers who needed offline assistance.
  • The trial proved that the static call-back was cannibalizing sales from the online channel, while the proactive call-back optimized sales conversions.
  • Essent abandoned the static call-back in favor of the proactive system, leading to increased sales and improved customer satisfaction.
  • The use of Predictive Intelligence allowed Essent to make data-driven decisions, enhancing the overall efficiency of its contact centers.
数量效益
  • 61% of the sales leads generated by the proactive call-back during the two-month trial converted.

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