CUSTOMER SATISFACTION MEASUREMENT MODELS: GENERALISED MAXIMUM ENTROPY APPROACH

This paper presents the methodology of the Generalised Maximum Entropy (GME) approach for estimating linear models that contain latent variables such as customer satisfaction measurement models. The GME approach is a distribution free method and it provides better alternatives to the conventional method; Namely, Partial Least Squares (PLS), which used in the context of costumer satisfaction measurement. A simplified model that is used for the Swedish customer satis faction index (CSI) have been used to generate simulated data in order to study the performance of the GME and PLS. The results showed that the GME outperforms PLS in terms of mean square errors (MSE). A simulated data also used to compute the CSI using the GME approach.Generalised Maximum Entropy, Partial Least Squares, Costumer Satisfaction Models.

Design and evaluation of a virtual gearshift application

When a customer buys a new car, he or she wants it to address personal preferences with respect to its driving behavior. By utilizing virtual reality technology, a virtual prototyping environment (VPE) can be created in which the behavior of a vehicle or part of a vehicle can be evaluated and adjusted to match the driver's desires. This paper describes the design and the evaluation of a VPE for manually operated gearboxes. The test group considered the simulated "virtual" gearshift feel to be quite similar to the "real" gearshift feel of a test vehicle. By further developing this VPE, it should become possible to define gearshift feel by customer assessment through haptic simulation, after which the physical gearbox is designed in such a way that it matches the preferred shifting behavior

Online Learning of Aggregate Knowledge about Non-linear Preferences Applied to Negotiating Prices and Bundles

In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a procedure for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining aggregate (anonymous) knowledge of customer preferences with current data about the ongoing negotiation process. The developed procedure either works with already obtained aggregate knowledge or, in the absence of such knowledge, learns the relevant information online. We conduct computer experiments with simulated customers that have_nonlinear_ preferences. We show how, for various types of customers, with distinct negotiation heuristics, our procedure (with and without the necessary aggregate knowledge) increases the speed with which deals are reached, as well as the number and the Pareto efficiency of the deals reached compared to a benchmark.Comment: 10 pages, 5 eps figures, ACM Proceedings documentclass, Published in "Proc. 6th Int'l Conf. on Electronic Commerce ICEC04, Delft, The Netherlands," M. Janssen, H. Sol, R. Wagenaar (eds.). ACM Pres

Identifying influencers in a social network : the value of real referral data

Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers

Evaluating the Impact of Critical Factors in Agile Continuous Delivery Process: A System Dynamics Approach

Continuous Delivery is aimed at the frequent delivery of good quality software in a speedy, reliable and efficient fashion – with strong emphasis on automation and team collaboration. However, even with this new paradigm, repeatability of project outcome is still not guaranteed: project performance varies due to the various interacting and inter-related factors in the Continuous Delivery 'system'. This paper presents results from the investigation of various factors, in particular agile practices, on the quality of the developed software in the Continuous Delivery process. Results show that customer involvement and the cognitive ability of the QA have the most significant individual effects on the quality of software in continuous delivery

Market Share Dynamics in a Model with Search and Word-of-Mouth Communication

This paper analyzes price competition in an infinitely repeated duopoly game. In each period, consumers remember the existence and location of their previous supplier. New information is gathered via search or word-of-mouth communication. Market outcomes are history-dependent, and the Markov perfection refinement is used to narrow the set of equilibria. Firms are shown to use mixed pricing strategies in equilibrium. The resulting price dispersion generates non-trivial market share dynamics. The goal of the paper is to characterize these dynamics, and to reveal the driving forces behind them