Stelios Tsafarakis

UTA-Rec: a recommender system based on multiple criteria analysis

UTARec, a Recommender System that incorporates Multiple Criteria Analysis methodologies is presented. The systems performance and capability of addressing certain shortfalls of existing Recommender Systems is demonstrated in the case of movie recommendations. UTARecs accuracy is measured in terms of Kendalls tau and ROC curve analysis and is also compared to a Multiple Rating Collaborative Filtering (MRCF) approach. The results indicate that the proposed Multiple Criteria Analysis methodology can certainly improve the recommendation process by producing highly accurate results, from a user oriented perspective.

Consumer Choice Behaviour and New Product Development: An Integrated Market Simulation Approach

The extremely high costs associated with the commercial failure of a new product, stresses the importance of a model that will effectively forecast the market penetration of a product at the design stage. The purpose of our study is to discover heuristics that will better explain market share, an issue of considerable concern to industry, which also, if successfully pursued, will increase the value of the analytical tools developed for managers. A method easy to implement is presented, which improves the value of market simulations in conjoint analysis. The proposed approach deals with two issues common to traditional market simulations in the context of conjoint analysis applications—the lack of differential impact of attributes across alternatives and the absence of accounting for differential substitution across brands (ie, the Independence from Irrelevant Alternatives problem). We deal with the first issue by ‘tuning’ utilities with individual level exponents, as opposed to a common exponent under the ‘ALPHA’ rule (the current state of the art approach). These exponents derive from the range, skewness and kurtosis of the distribution of utilities that a respondent assigns to various products. While these exponents are individual specific, the effects of the coefficients are assumed to be homogeneous across consumers to preserve model parsimony, while accounting for observed heterogeneity in the data. The second issue is studied in the model via a similarity ‘correction’ for each pair of products. The performance of the approach is validated both on real data from a market survey concerning milk, and on simulated data through the design of a Monte Carlo experiment. The results of the simulation for different market scenarios indicate that the approach appropriately exhibits the theoretical properties that are necessary for the efficient representation of consumer choice behaviour. In addition, the proposed model outperforms the state of the art methodology, as well as some more traditional approaches, with regard to the forecasting accuracy on market shares estimation, both on the real and the simulated data sets. The results obtained have important implications for marketing managers concerning the design of new products. A new concept can be tested before it enters the production stage, using data obtained from a market survey. The high predictive accuracy of the model may assist a firm in minimizing the uncertainty and risks associated with a new product launch. The case study with data from a real market survey, illustrates the practical applicability of the approach.

Applications of MCDA in Marketing and e-Commerce

This chapter emphasizes on the major components under which MCDA applications in marketing and e-commerce have been developed and describes characteristic examples of research works that apply MCDA methodologies in marketing and e-commerce. The chapter is divided into two main sections separating the MCDA applications in the marketing discipline from those that appear in the ecommerce field. In each section fundamental notions of marketing and e-commerce are discussed accordingly and some characteristic examples of research works are analytically mentioned. The aim of this work is to endow candidate researchers that are interested in applyingMCDA methodologies in marketing and e-commerce with adequate background information to further develop their scopes and ideas.

Biologically Inspired Approaches to Strategic Service Design Optimal Service Diversification Through Evolutionary and Swarm Intelligence Models

This article introduces nature-inspired modeling to strategic service management. It determines optimal service diversification through an evolutionary mechanism of natural selection and population genetics as well as a model of cooperative behavior and collective intelligence in swarms. Specifically, we design and implement Genetic and Particle Swarm Optimization algorithms to stated-preference data derived from a conjoint experiment measuring consumer preferences for service attributes in a retail setting. The proposed procedure provides key insights to strategic service management such as optimal service design, optimal mix of service offerings in terms of consumer demand, and local adaptation of service portfolios. It demonstrates how diversification meets heterogeneous customer preferences and how localized solutions address cross-country differences. The findings suggest that variation in service portfolios elevates customer utility, in the sense that diversified offerings better match heterogeneous customer needs. In an intuitive fashion, consumer diversity is such that a uniform service portfolio is inferior to differentiated offerings, especially with regard to salient service attributes. The results also illustrate that localized diversification strategies are necessary for multistore, multimarket operations. Our method has valuable implications for managers aiming to improve how they design their services. A new tool is introduced which handles tangible and intangible service elements and allows service design optimization by predicting which elements create the most compelling service contexts from a customer perspective. The tool also facilitates localized diversification decisions by adapting critical service attributes to local markets. Bio-inspired models shed new light on marketing phenomena and reveal opportunities for empirical research.

Designing Optimal Products: Algorithms and Systems

The high cost of a product failure makes it imperative for a company to assess the market penetration of a new product at its early design. In this context, the Optimal Product Line Design problem was formulated thirty five years ago, and remains a significant research topic in the area of quantitative marketing until today. In this chapter we provide a brief description of the problem, which belongs to the class of NP-hard problems, and review the optimization algorithms that have been applied to it. The performance of the algorithms is evaluated, and the best two approaches are applied to simulated data, as well as a real world scenario. Emphasis is placed on Genetic Algorithms, since the results of the study indicate them as the approach that better fits to the specific marketing problem. Finally, the relevant marketing systems that deal with the problem are presented, and their pros and cons are discussed.

Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover

The optimal product line design is an NP-hard optimization problem in marketing that involves a number of decisions, such as product line length and configuration. Simulated annealing constitutes the best performing approach so far, but with extremely large running times. In the current study simulated annealing is hybridized with an evolutionary algorithm to improve its search efficiency and alleviate its performance dependence on the selection of the parameters related to its cooling schedule. The presented approach outperforms genetic algorithms and classic simulated annealing, through the use of crossover as a neighborhood operator, along with the restricted tournament selection as the replacement strategy of the evolutionary algorithm’s population. Moreover, the paper describes the way that the proposed hybrid metaheuristic can be used for redesigning a firm’s product line. The issue of redesigning product lines becomes even more important in periods of economic crisis, as firms must adapt their offerings to new evolving patterns of consumer buying behavior and reduced levels of consumer’s purchasing power. The applicability of the proposed approach is illustrated through the case of the 2008 automotive industry crisis, by showing how the North American car manufacturers could have redesigned their lines on time, based on the configuration of the competitive products in the market as well as the new customer preferences emerged during the economic recession.

Optimizing differentiation and commonality levels among models in car line-ups

We implement a swarm intelligence mechanism to design optimal car lines.We aim to optimize differentiation and commonality levels among models in the line.Our mechanism utilizes stated preference data derived from a conjoint experiment.A prototype system is also developed to facilitate managerial decision making.Insights are provided into how new and existing car models must be combined. The product life cycle of cars is becoming shorter and carmakers constantly introduce new or revised models in their lines, tailored to their customer needs. At the same time, new car model design decisions may have a substantial effect on the cost and revenue drivers. For example, although a new car model configuration with component commonality may lower manufacturing cost, it also hinders increased revenues that could have been achieved through product differentiation. This paper develops and illustrates a state of the art, nature-inspired approach, to design car lines that optimize the degree of differentiation vs commonality among models in the line. More specifically, we apply a swarm intelligence mechanism to stated preference data derived from a large-scale conjoint experiment that measures consumer preferences for passenger cars in a sample of 1164 individuals. The proposed two-step methodology is also incorporated into a prototype system, which has been developed in an attempt to facilitate managerial decision making. Our approach provides interesting insights into how new and existing car models can be combined in a product line and identifies the desired balance between differentiation and commonality levels among models within a product line, which elevates customer satisfaction.

An integrated market simulation model for the design of new products

Market simulators assist managers in product design and pricing decisions, through advanced heuristic algorithms for consumer buying behavior modeling. We propose a model for use in such systems, which combines the effective incorporation of the two critical properties a simulator should theoretically reflects, with exceptionally performance in actual choice shares estimation. Differential Impact and Substitution are exhibited through the representation of customer heterogeneity and product similarity in the choice rule, while high predictive accuracy is displayed with the implementation of the Stochastic Logarithmic Search algorithm.

Consumer-oriented new product development: a review of recent developments

Firms need to continuously develop new products or redesign their existing ones, due to the intense competition they are facing, as well as the rapidly changing economical and sociopolitical environment. In this context, consumer behaviour modelling has become an important and inextricable part of successful new product development during the last decades. This paper constitutes a survey of the literature in consumer-oriented new product development. We review a total of 60 research propositions in consumer behaviour modelling as a part of the product development procedure. The findings indicate a trend for integrative methodologies that approach the new product development process from both a marketing and an engineering perspective. The incorporation of dynamic consumer behaviour models into the product development methodologies seems to be the most promising area for future research.

Manual Intervention and Statefulness in Agent-Involved Workflow Management Systems

Lack of adaptability within WorkFlow Management Systems (WFMS) has been early identified as one of their limitations. WFMS suffer from disadvantages such as not supporting the dynamic incorporation/modification of process models and poor adaptability of process models at runtime. The static workflow definition and its passive interpretation does not allow WFMS to demonstrate flexible behavior and to deal with real-life situations, such as fast changing customer requirements and enterprise goal shifts. In this work we propose the design and development of two features (manual intervention and statefulness), which are expected to tackle this limitation. Our work considers and agent-based environment for the WFMS implementation.