Optimal product line design

Abstract

Through out its life cycle a company will always try to extend its product portfolio to cater the needs of the consumers and achieve significant market share. Accordingly designing and pricing new products is one of the most critical activities for a firm, and it is well-known that taking into account consumer preferences for design decisions is essential for products later to be successful in a competitive environment. Consequently, measuring consumer preferences among multi attribute alternatives has been a primary concern in marketing research. Among many methodologies developed, conjoint analysis has turned out to be one of the most widely used preference-based techniques for identifying and evaluating new product concepts. Our model is directed at determining optimal product concepts using consumers’ distinctive or segment level part-worth preference functions. Product lines are constructed directly from part-worths data obtained by conjoint analysis, which can be characterized as a one-step approach to product line design. In contrast, a two-step approach would start by first reducing the total set of feasible product profiles to a smaller set of promising items (reference set of candidate items) from which the products that constitute a product line are selected in a second step. Most of the papers on conjoint-based product line design have employed a deterministic, first-choice model of idiosyncratic preferences. Accordingly, a consumer is assumed to choose from her/his choice set the product with maximum perceived utility with certainty. However, the first choice rule seems to be an assumption too rigid for many product categories and individual choice situations, as the analyst often won’t be in a position to control for all relevant variables influencing consumer behavior (e.g., situational factors). Therefore, we incorporate a probabilistic choice rule to provide a more flexible representation of the consumer decision making process and start from segment specific conjoint models of the conditional multinomial logit type. We further consider profit as a firm’s economic criterion to evaluate decisions and introduce fixed and variable costs for each product profile. However, the proposed methodology is flexible enough to accommodate for other goals like market share (as well as for any other probabilistic choice rule).