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Multinational Education & Publishing Corporation Improves Customer Service with AI
技术
- 分析与建模 - 预测分析
- 分析与建模 - 实时分析
适用行业
- 教育
服务
- 数据科学服务
挑战
The multinational education and publishing corporation was facing a challenge in improving the performance of its contact center agents. With eight teams distributed across four different countries, the company was only able to review less than 1% of customer interactions. This lack of visibility into customer interactions was leading to longer call durations and average handle times (AHT), which were out of range. The company was also experiencing a rising number of customer requests, which further exacerbated the situation. The company needed a solution that would not only reduce AHT and increase efficiency but also improve the performance of its contact center agents.
关于客户
The customer is a multinational education and publishing corporation that provides a wide range of products and services to its customers worldwide. These include online courses, eTexts, textbooks, learning platforms, rental books, and much more. The company has a large customer base and operates contact centers in four different countries. These contact centers are responsible for handling a high volume of customer requests on a daily basis. The company is committed to improving the performance of its contact center agents in order to drive customer trust.
解决方案
The company implemented the Observe.AI platform to gain visibility into 100% of its customer interactions. The platform's AI-powered interaction monitoring feature, Observe.AI Moments, allowed the company to track key points of interest on calls that offer signals on agent performance as it relates to customer satisfaction, process adherence, call drivers, compliance, and more. The company also used sentiment analysis to analyze customer and agent interactions on a deeper level by automatically detecting negative or neutral interactions on calls. The company's contact center teams created a 'Communication Gap' Moment, which specifically tracks negative sentiment to flag miscommunication between an agent and the customer. This enabled the company's teams to quickly gain contextual sentiment insights that are used to identify where agent training efforts needed to be focused. The company also used Built-In Evaluation Forms to go beyond scoring calls and deliver contextual, actionable feedback to agents in a single window view. With Agent Performance Analytics, the company's contact center teams were able to identify outliers in performance, surface opportunities to celebrate great performance, and correct or clarify based on real data.
运营影响
数量效益
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