Quantifying Customer Sacrifice for Use in Product Customization Problems

Abstract

Every time a customer selects a product from the shelf they make a purchase decision based on trade-offs between available offerings. The available products often exhibit feature excess at a price premium, feature deficiency at a price discount, or some combination of both. By purchasing one of these products a customer experiences some degree of sacrifice. This paper proposes the use of choice-based conjoint analysis and hierarchical Baysian modeling to calculate the perceived utility of a customer’s ideal product and the perceived utility of the best current alternative in the market. A customer’s sacrifice gap, a quantity that mass customization seeks to minimize, is defined as the difference between these values. This paper quantifies a market-average sacrifice gap and uses it in a theoretical product platform customization scenario. This scenario examines the effects of offering customization options on one attribute of a product at a time on a customer-centric objective (sacrifice gap) and a firm-centric objective (aggregate contribution). The results are also used to examine how customer sacrifice is minimized at an individual-level.

The Perspective of Quality Management System Development in the Era of Industry 4.0

“…The concept of 'open quality' or 'quality 4.0' is defined as the adaptive ability of products or services, at all stages of the life cycle, to satisfy the needs of a specific consumer through partnerships with stakeholders and digital management of the value chain. Quality 4.0 is based on bridging the gap between consumer requirements and product properties which arises due to the need to adapt mass products to meet the individual needs of a person or organization, on the transition to mass customization, as shown in the literature (Ceylan et al, 2018), and reducing the customer sacrifice (Porterfield & Ferguson, 2012).…”

Design for Marketing Mix: The Past, Present, and Future of Market-Driven Product Design

“…Finally, there were two papers that took completely different approaches to optimization problem formulation. A customization environment was considered in [93], changing the objective to one focused on minimizing customer sacrifice. Using probability of purchase and assigning a cost to the customization variables, the goal of the objective was to identify the components that should be made available for selection in a build-to-order environment.…”