A simulation comparison of methods for new product location

Includes bibliographical references (p. 29-31)

Towards personalized data-driven bundle design with QoS constraint

Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiativ

Inferring Market Structure from Customer Response to Competing and Complementary Products

We consider customer influences on market structure, arguing that market structure should explain the extent to which any given set of market offerings are substitutes or complements. We describe recent additions to the market structure analysis literature and identify promising directions for new research in market structure analysis. Impressive advances in data collection, statistical methodology and information technology provide unique opportunities for researchers to build market structure tools that can assist “real-time” marketing decision-making.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46981/1/11002_2004_Article_5088105.pd

A Consumer-Based Methodology for the Identification of New Product Ideas

This paper suggests a procedure which analytically ties a model to predict users' predispositions to purchase different "brands" in a product-market together with a search process to identify optimal new product ideas. Brands, conceptualized as attribute bundles, are located in a prespecified attribute space. The painwise preference judgments of each individual in a representative sample drawn from the population of users are analyzed using the authors' LINMAP procedure (LINear programming techniques for Multidimensional Analysis of Preferences) to determine his ideal point and salience weights for the attributes of the space. A distance model of choice is postulated for each user and used to predict his probability of choosing nonexisting products. The models developed for each user are tied to methods for searching the product space to find "best" locations for new products. The proposed procedures are discussed and evaluated in the light of relevant conceptual and empirical research. The paper concludes with a discussion of additional applications of the behavioral framework of LINMAP to other marketing decision areas.

Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry

Previous studies dealing with product growth have dealt only with substitution effects among successive generations of one product category and not with complementarity and competition provided by related product categories. Based on a broadened concept of the competitive information technology (IT) market, we develop a dynamic market growth model that is able to incorporate both interproduct category and technological substitution effects simultaneously. The market potential for each category or generation is treated as a variable rather than a constant parameter, which is typical of recently growing IT sectors such as wireless telecommunications. The model is calibrated, its plausibility discussed, and its face and predictive validity assessed using data on wireless telecommunications services from two Asian markets. Results show that the market potential (and sales growth) of one category or generation is significantly affected by others and by the overall structure of a geographic market. The model is shown to make relatively good predictions even when the data from recently introduced categories/generations are limited.information technology, wireless telecommunications service, intercategory effects, market potential, asymmetry of effect, bidirectional interrelationship

A Simulation Comparison of Methods for New Product Location

Four algorithms for locating an “optimal” new product in a multiattribute product space—Albers and Brockhoff's PROPOPP; Gavish, Horsky, and Srikanth's Method IV; May and Sudharshan's PRODSRCH; and GRID SEARCH—are compared in terms of the relative share of preferences the new product will capture under different simulated market environments. These environments were both ones for which the algorithms were designed as well as other “more realistic” environments. Results indicate that algorithm performance is sensitive to the number of customers or segments, and the presence of probabilistic choice, and less sensitive to the numbers of existing products. Gavish, Horsky, and Srikanth IV (GHS IV) and PROPOPP performed best under the market conditions for which they were designed and GHS IV proved quite robust under variation from these conditions. PROPOPP's performance deteriorated, however, in large sample size problems ( ≥ 200). PRODSRCH (a general purpose optimizer) was inferior under these special market conditions, but superior under other more general ones.

Reply

Reply to commentaries (Dodson, J. A., J. B. Brodsky. 1987. Commentary. 202–203 and Johnson, R. M. 1987. Commentary. 204–205.) to authors' paper (Sudharshan, D., J. H. May, A. D. Shocker. 1987. A simulation comparison of methods for new product location. 182–201.).