Analyses for Service Interaction Networks with Applications to Service Delivery

One of the distinguishing features of the services industry is the high emphasis on people interacting with other people and serving customers rather than transforming physical goods like in the traditional manufacturing processes. It is evident that analysis of such interactions is an essential aspect of designing effective and efficient services delivery. In this work we focus on learning individual and team behavior of different people or agents of a service organization by studying the patterns and outcomes of historical interactions. For each past interaction, we assume that only the list of participants and an outcome indicating the overall effectiveness of the interaction are known. Note that this offers limited information on the mutual (pairwise) compatibility of different participants. We develop the notion of service interaction networks which is an abstraction of the historical data and allows one to cast practical problems in a formal setting. We identify the unique characteristics of analyzing service interaction networks when compared to traditional analyses considered in social network analysis and establish a need for new modeling and algorithmic techniques for such networks. On the algorithmic front, we develop new algorithms to infer attributes of agents individually and in team settings. Our first algorithm is based on a novel modification to the eigen-vector based centrality for ranking the agents and the second algorithm is an iterative update technique that can be applied for subsets of agents as well. One of the challenges of conducting research in this setting is the sensitive and proprietary nature of the data. Therefore, there is a need for a realistic simulator for studying service interaction networks. We present the initial version of our simulator that is geared to capture several characteristics of service interaction networks that arise in real-life. Contact: (sameepmehta, pvinayak)@in.ibm.com Center for Global Logistics and Manufacturing Strategies (GLAMS), Indian School of Business, Hyderabad IBM India Research Laboratory, New Delhi University of North Carolina, Chapel Hill The author was with IBM India Research Lab when the work was done.