Optimal Pricing and Introduction Timing of Technology Upgrades in Subscription-Based Services

Many technologies that fuel subscription-based services improve over time. Examples are mobile phones, software suites (Microsoft Office, Adobe Creative Cloud), subscription services (Netflix), and cloud service providers. Additionally, modern subscription services are increasingly personalized to individual subscribers, and as a result, discriminatory pricing is ever-present in the marketplace, allowing firms to reward existing customers with discounts and special offers on upgrades. However, customers may be averse to switching to improved services because of costs related to redesigning business processes, downtime, or customer inertia. We propose a model of technology upgrades featuring discriminatory pricing based on customers’ upgrade experience. We characterize the optimal pricing policy for the service provider and develop an efficient algorithm for computing optimal prices. We also characterize the optimal timing of technology introductions and show that it is generally optimal to introduce new technologies in periodic intervals after some time. In the context of subscription-based services, many technologies improve over time, and service providers can provide increasingly powerful service upgrades to their customers but at a launching cost and the expense of the sales of existing products. We propose a model of technology upgrades and characterize the optimal pricing and timing of technology introductions for a service provider who price-discriminates among customers based on their upgrade experience in the face of customers who are averse to switching to improved offerings. We first characterize optimal discriminatory pricing for the infinite horizon pricing problem with fixed introduction times. We reduce the optimal pricing problem to a tractable optimization problem and propose an efficient algorithm for solving it. Our algorithm computes optimal discriminatory prices within a fraction of a second even for large problem instances. We then show that periodic introduction times, combined with optimal pricing, enjoy optimality guarantees. In particular, we first show that, as long as the introduction intervals are constrained to be nonincreasing, it is optimal to have periodic introductions after an initial warm-up phase. When allowing general introduction intervals, we show that periodic introduction intervals after some time are optimal in a more restricted sense. Numerical experiments suggest that it is generally optimal to have periodic introductions after an initial warm-up phase. Finally, we focus on a setting in which the firm does not price-discriminate based on customers’ experience. We show both analytically and numerically that in the nondiscriminatory setting, a simple policy of Myerson (i.e., myopic) pricing and periodic introductions enjoys good performance guarantees. Funding: This material is based upon work supported by INSEAD and University Pierre et Marie Curie [Grant ELICIT], as well as by the National Science Foundation [Grant 2110707]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2364 .