How Avatars and Games Improve Customer Service
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We use the same feedback loops as online games in order to improve customer service at our call centers.
“Game dynamics in the workplace helps [call center] agents and their managers to set priorities, and also provides agents with a common challenge that they can collaborate on and talk about.” — Ben Hanrahan
“Game dynamics in the workplace helps [call center] agents and their managers to set priorities, and also provides agents with a common challenge that they can collaborate on and talk about.” — Ben Hanrahan, Xerox researcher
Call center agents often say they feel uncertain about their performance, and that they receive too little feedback from their managers on how they are interacting with customers. This is not very comforting for businesses, such as airlines and credit card companies that rely on these agents as front-line ambassadors.
Xerox researchers have invented software that uses gaming techniques to provide feedback to call center agents and encourage teamwork.
Our studies on call centers indicate that embedding game dynamics in the workplace helps agents and their managers to set priorities, and also provides agents with a common challenge that they can collaborate on and talk about.
I led a team of social scientists at the Xerox Research Centre Europe who studied various aspects of how call center agents work, and we gained a better understanding of their challenges. The result is the Xerox Agent Performance Indicator software (Xerox API).
Over the course of the workday, Xerox API provides agents with continuous feedback on their performance in a fun, interactive way. Using green, yellow and red color codes, agents can see how they are doing in areas such as time spent on a call, number of calls handled, and other key performance indicators. Traditional feedback methods involve a manager emailing performance reports to agents once or twice a day.
Agents who use the technology show improvement on standard performance measures such as the amount of time spent solving a customer problem. The tool also has helped streamline the relationship between managers and their team.
This innovative approach to providing support and immediate feedback to call center agents has been well-received, earning us an Innovation Award from the National Contact Center Association in The Netherlands where it was first deployed.
The technology has been deployed to over 1,500 agents working in Xerox contact centers in Europe that provide customer care services for 25 Xerox customers.
Xerox plans to roll out the tool to other countries (about 150), including the United States. Xerox has more than 48,000 call center employees who handle more than a million consumer interactions every day via the phone and Web.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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