Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading^*

TLDR

Firms design products to satisfy consumer preferences while remaining manufacturable, yet marketing and engineering goals are usually optimized separately, leading to suboptimal decisions when the two problems are interrelated. This paper adopts analytical target cascading to coordinate marketing and engineering design problems and formally identify the joint optimal product solution. The ATC framework integrates conjoint, discrete choice, and demand‑forecasting methods with a parametric engineering design model, and is illustrated on dial‑readout household scales using real choice data. The study finds that the most profitable product lies below marketing‑only predictions but above engineering outputs based on initial marketing targets.

Abstract

Firms design products that appeal to consumers and are feasible to produce. The resulting marketing and engineering design goals are driven by consumer preferences and engineering capabilities, two issues that conveniently are addressed in isolation from one another. This convenient isolation, however, typically will not result in optimal product decisions when the two problems are interrelated. A method new to the marketing community, analytical target cascading (ATC), is adopted here to explore such interrelationships and to formalize the process of coordinating marketing and engineering design problems in a way that is proven to yield the joint optimal solution. The ATC model is built atop well‐established marketing methodologies, such as conjoint, discrete choice modeling and demand forecasting. The method is demonstrated in the design of dial‐readout household scales, using real conjoint choice data and a parametric engineering product design model. Results indicate that the most profitable achievable product can fall short of predictions based on marketing alone but well ahead of what engineering may produce based on original marketing target specifications. A number of extensions can be accomplished readily using techniques from the extant marketing and design optimization literature.