“Phygital” Experiences: Optimization, Tailoring, and Pricing Contract Strategies on Online Platforms1

Technology-driven tools, such as augmented reality (AR) and virtual reality (VR), bring products to life in a virtual space for online consumers and are poised to become the new standard. With more effective tools, consumers are more likely to find the products that best meet their needs. While these tools enhance online purchasing decisions, consumers vary in their ability to use them effectively. As e-shopping continues to grow, firms must carefully consider whether and how to offer these tools. In this paper, we analytically study the firm’s (1) tool provision, (2) the optimization of a tool that brings a physical experience to e-shopping, and how they differ depending on (3) the heterogeneity of consumer expertise in using the tool. We also consider (4) pricing decisions, different channel contracts, and (5) the alignment of profits with the platform through which the firm sells its products with respect to tool optimization and tailoring incentives. We find that a tool need not always be perfect; experienced users can be given an imperfect tool. Although optimal effectiveness can be higher when a tool is tailored to inexperienced users, sellers do not pursue this strategy, as it primarily benefits the platform. While the platform does not always have the same incentives as the seller in dimensions such as tool provision, pricing contracts, and tailoring preferences, we find that this misalignment in tailoring preferences can be mitigated for certain product categories. Additionally, our results show that optimal tool effectiveness is always higher under a sell-on contract, making it a potential win-win solution. Furthermore, we demonstrate that tool provision can shift the platform’s preference from a sell-to to a sell-on contract, which generally benefits the seller. Altogether, we show that tool provision should be considered jointly with tailoring to different consumer experience levels. Our results remain robust when accounting for a mix of inexperienced and experienced consumers, product returns, risk-averse consumers, and cost barriers.