An Economic Analysis of the Transformative Value of Internet of Things and Dynamic Pricing

Internet of Things (IoT) technology empowers firms in making better demand forecastthrough collection of a variety of consumer related data, Moreover, it also facilitatescreation of products or services that are more tightly match the preferences of theconsumers, and generating demands that were otherwise absent. Together, thetechnology prepares firms with capabilities of improving demand prediction as well asdemand generation. These capabilities, in turn, can have profound impact on thedynamic pricing strategies adopted by firms.However, these IoT capabilities not only requires substantial investments in the IoTinfrastructure, but also competencies in analytics to analyze the deluge to data collectedthrough the deployment of the IoT devices. Despite the heavy investments required, fewhave 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 inalgorithmic pricing. In this study, we investigate how new capabilities introduced by IoT,when incorporated with different pricing strategies, would affect the profit advantage ofa firm and consumer welfare.Our analysis centered around the key economic tradeoffs surrounding the costjustification of deploying the technology. That is, how a better demand prediction andenhancement of consumer valuation would interact with different pricing strategies.Answering those questions also allow us to shed light on how much of such technology isrequired. In convention wisdom, we learn that a pre-committed low-price strategy“undercharge” the high-valuation consumer, whereas a pre-committed high-pricestrategy would “price out” the low-valuation consumer, and dynamic contingent pricingstrategy would be wasteful if the arrival of high-valuation consumer does notmaterialize. Moreover, enhancement in consumer valuation through IoT could potentiallycannibalize the benefits from better precision in demand prediction as a “winningproduct” would guarantee consumer demand. Our study aims to better understand theinterplay of all these factors.As algorithmic pricing is increasingly becoming mainstream, there is a greater need tounderstand the potential impact levied by the dynamic pricing models that are applied insupporting those algorithms. More importantly, it is important to see the role of theabundancy of data that has been applied in supporting those pricing models. Theoutcome of this study provides us better insights on how we can utilize the emerging IoTtechnology for the betterment of firms and consumers.