University of Pennsylvania

Latest Content From University of Pennsylvania

Whitepaper: "Influentials" And "Imitator": How To Better Forecast The Sale Of New Products

by University of PennsylvaniaJan 01, 2008

Marketers have long tried to deepen their understanding of how new products gain acceptance among customers, a process known as product diffusion. Companies are especially interested in diffusion in markets that consist of two segments ""Influentials"" (knowledgeable people who keep abreast of product innovations and readily accept them) and ""Imitators"" (people whose purchasing decisions are swayed by their savvier counterparts). Targeting influential prospects who are more in touch with new developments than most people and converting them into customers, the thinking goes, allows companies to benefit from a ""Social multiplier"" or ""social contagion"" effect in marketing campaigns.

Whitepaper: Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data

by University of PennsylvaniaJan 01, 2008

A call center is a centralized hub that exists solely for the purpose of making or attending calls to or from customers or perspective customers. In today’s economy, call centers have not only become the primary point of contact between customers and businesses but also a major investment for many organizations. Due to the magnitude of these operations, call center supervisors need to staff their organization efficiently in order to provide a satisfactory level of service at reasonable costs. This paper proposes a multiplicative model for modeling and forecasting within-day arrival rates to a US commercial bank’s call center.

Whitepaper: Statistical Analysis of a Telephone Call Center: A Queueing-Science Perspective

by University of PennsylvaniaJan 01, 2008

A call center is a service network in which agents provide telephone-based services. Customers that seek these services are delayed in tele-queues. This paper published by University of Pennsylvania summarizes an analysis of a unique record of call center operations. The data comprise a complete operational history of a small banking call center, call by call, over a full year. Taking the perspective of queuing theory, the authors decompose the service process into three fundamental components: arrivals, customer patience, and service durations. Each component involves different basic mathematical structures and requires a different style of statistical analysis.

Whitepaper: Analysis of Call Center Data

by University of PennsylvaniaJan 01, 2008

This paper focuses on the issue of efficiency within a call center. A call center is a place where calls are answered by service agents and it can handle a considerable volume of calls at the same time. This service is typically operated with the help of automated voice response systems which screen and forward the calls to the service agents. The central problem in such a service is the concept of queueing.

Whitepaper: Monitoring Process Quality In Offshore Outsourcing: A Model And Findings From Multi-Country Survey

by University of PennsylvaniaJan 01, 2008

This research paper by University of Pennsylvania investigates how recent advances in Information Technology and Telecommunications have led to real-time monitoring of processes at the site of the provider by a buyer located across the globe. Results are furnished of a comprehensive, multi-year, multi-country survey of the efficacy of monitoring and other instruments of governance in offshore outsourcing projects. The paper provides a game-theoretic model of the dynamics of the buyer-supplier interaction and estimates the modeling primitives from the survey data. This solves the game, analyzes the equilibrium characteristics, and show rich empirical support for the findings from the survey.

Whitepaper: ROCI: A Distributed Framework for Multi-Robot Perception and Control

by University of PennsylvaniaJan 01, 2008

This paper published by University of Pennsylvania presents ROCI, a framework for developing applications for multi-robot teams. In ROCI, each robot is considered as a node which contains several modules and may export different types of services and capabilities to other nodes. Each node runs a kernel that mediates the interactions of the robots in a team. This kernel keeps an updated database of all nodes and the functionalities that they export. As an example, the authors present an obstacle avoidance task implemented using their framework and also discuss the use of ROCI in a multi-robot scenario.