In this project we developed and validated of a new sentiment analysis engine for conversational data, called CustSent, in collaboration with LivePerson Inc.. We then develop the novel concept of emotional load – the load that employees must bear due to the emotional strain inherent in the service interactions in which they engage. Using contact center data we investigate the impact of Emotional Load on agents and the progression of the service interaction.
We investigate how the transparency of the medical process and wait time information influence ED patients. In collaboration with Clalit Health Services, we developed a web-based app that delivers information to ED patients through their mobile phones. The development combines methods of process mining, queueing theory, and human-centered UX design. The system operates at Carmel Medical Center. Our research examines the impact of information transparency on ED efficiency and patient behavior.
Contact centers (CS) are considered the future of service delivery, offering service via texting, social media, and apps. These provide companies with unique opportunities, such as providing service proactively only to the customers that need it the most, but are also prone to new operational challenges, such as concurrency management and information uncertainty. CS data allow us to investigate the dynamics of service production and the behaviors of customers and agents. In a series of projects, we create new service models for CS and control policies for those systems.