Heuristics for product-line design using conjoint analysis

Recently proposed methods for product-line selection use the total utilities of candidate items to construct product lines maximizing seller’s return or buyers’ welfare. For conjoint (hybrid conjoint) data, enumerating the utilities of candidate items can be computationally infeasible if the number of atrributes and attribute levels is large and most multi-attribute alternatives are feasible. For such problems, constructing product lines directly from part-worths data is preferable. The authors propose such methods, extending Kohli and Krishnamurti’s dynamic-programming heuristic for selecting a single item maximizing share to structure product lines maximizing share, seller’s return, or buyers’ (utilitarian) welfare. The computational performance of the heuristics and their approximation of product-line solutions is evaluated using simulated data. Across problem instances, the dynamic-programming heuristics identify solutions that are no worse, in terms of approximating optimal solutions, to the solutions of heuristics for the current two-step approaches to product-line design. An application using hybrid data for a consumer-durable product is described.