Market segmentation using predictive technology and shared-service contracts

Abstract

‘Shared-service contracts’ provided by Original Equipment Manufacturers (OEMs) are a standard feature of capital-intensive, long-life products. In such contracts, OEMs assume a predetermined portion of the products’ long-term failure opportunity costs by conducting routine and breakdown maintenance tasks for their customers. Recently, OEMs have been utilizing predictive technology to improve service productivity. It enables OEMs to reduce failure opportunity costs mentioned while using the data generated to develop products at lower costs. We extend the adverse selection framework to model the market segmentation of products offered with shared-service contracts and predictive technology. Our model includes: (i) the cost of product development and operation, (ii) the failure opportunity cost driven by long-term product failures, (iii) the cost of designing product reliability, and (iv) the cost of incorporating predictive technology, which reduces the first two cost components. Here, the OEM targets a basic and a premium offering at their respective customer types. We find that using predictive technology improves the quality and price of OEM products, thereby increasing their profitability. However, predictive technology enables the OEM to design a product line with lower reliability. In addition, the OEM is more likely to choose a ‘premium-only’ strategy when predictive technology is present.