An Economic Analysis of the Transformative Value of Internet of Things and Dynamic Pricing
Project: Research › GRF
DescriptionInternet of Things (IoT) technology empowers firms in making better demand forecast through collection of a variety of consumer related data, Moreover, it also facilitates creation of products or services that are more tightly match the preferences of the consumers, and generating demands that were otherwise absent. Together, the technology prepares firms with capabilities of improving demand prediction as well as demand generation. These capabilities, in turn, can have profound impact on the dynamic pricing strategies adopted by firms. However, these IoT capabilities not only requires substantial investments in the IoT infrastructure, but also competencies in analytics to analyze the deluge to data collected through the deployment of the IoT devices. Despite the heavy investments required, few have studied how the improved capabilities would translate to actual benefits for firms, especially considering that a plethora of pricing strategies are available for firms, including the increasingly prevalent dynamic pricing strategies are anchored upon in algorithmic pricing. In this study, we investigate how new capabilities introduced by IoT, when incorporated with different pricing strategies, would affect the profit advantage of a firm and consumer welfare. Our analysis centered around the key economic tradeoffs surrounding the cost justification of deploying the technology. That is, how a better demand prediction and enhancement of consumer valuation would interact with different pricing strategies. Answering those questions also allow us to shed light on how much of such technology is required. In convention wisdom, we learn that a pre-committed low-price strategy “undercharge” the high-valuation consumer, whereas a pre-committed high-price strategy would “price out” the low-valuation consumer, and dynamic contingent pricing strategy would be wasteful if the arrival of high-valuation consumer does not materialize. Moreover, enhancement in consumer valuation through IoT could potentially cannibalize the benefits from better precision in demand prediction as a “winning product” would guarantee consumer demand. Our study aims to better understand the interplay of all these factors. As algorithmic pricing is increasingly becoming mainstream, there is a greater need to understand the potential impact levied by the dynamic pricing models that are applied in supporting those algorithms. More importantly, it is important to see the role of the abundancy of data that has been applied in supporting those pricing models. The outcome of this study provides us better insights on how we can utilize the emerging IoT technology for the betterment of firms and consumers.
|Effective start/end date||1/01/18 → …|