Transforming the Contact Center with Actionable AI and Machine Learning

Running a contact center has inherent challenges — striking the perfect balance between customer satisfaction, agent satisfaction, and efficiency isn’t simple. Most organizations seek to understand their contact center performance by measuring agent and customer metrics but, as anyone who’s ever had a call center job can tell you, accessing all the information you need to do so isn’t always as straightforward as you need it to be. To further complicate matters, the major shift during the pandemic has left agents stretched thin with high levels of burnout, management struggling to oversee, coach and ensure compliance, and technology systems needing drastic transformation to support the digital and remote first era.

Customer expectations have never been higher. Today’s contact center agents are struggling with multiple challenges that make meeting them more difficult than ever. Most contact centers are short-staffed due to high turnover and agent burnout. Furthermore, many agents feel that their companies aren’t providing the tools and customer data they need to successfully assist and resolve customer issues. Those with remote call center jobs may feel disconnected from the resources they need to upskill or just get the support they need when problems arise.

Not only are customer expectations higher overall, but they are connecting with contact centers at 20% higher rates. Meanwhile, the ability to oversee their agents has shifted dramatically, especially for contact centers that have moved to remote, home-based agents. It is harder than ever to identify agent training gaps while ensuring compliance to policy, maintaining security, and identifying (and preventing) fraud.

With high burnout leading to increased turnover, businesses need to be able to do more with less while making it easier for their contact center employees to succeed in meeting company goals and customer expectations. In other words, they need to help solve burnout at its root cause (rather than simply working around it) by providing better tools and data to both agents and supervisors. They must also leverage performance metrics that provide high business value for customer satisfaction and business growth. This means that new, smarter tech and a new approach overall are needed.

The future of contact center technology lies in AI and connected data. An AI-powered contact center will allow you to serve your customers more effectively while increasing efficiency and reducing burnout.  But to be successful in leveraging an AI-powered contact center, businesses should follow five key steps.

First, clearly identify business goals and objectives for their desired growth. Second, assess the options and select the right platform that solves for specific business and contact center challenges, and is designed for business users so that the data and insights are accessible and actionable. Third, bring together all customer experience data into one place for a holistic, cross-channel approach. Fourth, define the workflows and metrics that help agents succeed and drive progress towards their business goals. These workflows should be enabled by AI to reduce workload burden, increase accuracy and improve business outcomes. Finally, use the results to identify training and coaching opportunities. Setup business rules to automatically score as many as all your conversations against the coaching criteria, making it easy to identify what can be measured automatically and what needs to be sent for evaluation with a supervisor for coaching.

Learn more about how SuccessKPI leverages the power of AI and machine learning to solve the most critical and challenging contact center issues.

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