Abstract
Characterizing the demand curve of products is important for pricing them optimally. However, in deriving empirical demand curves, econometricians have to contend with identification issues. Furthermore, theoretical demand curves derived using standard economic theory are divorced from empirical realities: firms rarely have information on customers’ budget constraints; theoretical utility functions are seldom derived empirically. Recognizing these issues, we propose an experimental approach for determining a product’s demand curve and, in turn, its profit-maximizing price in online environments. The proposed approach yields precise estimates and is quick and inexpensive to implement.
Highlights
E-commerce offers firms opportunities to price their goods and services dynamically using data-driven approaches [1,2,3,4,5,6]
The discussion can be generalized to firms selling goods and services online and those that monetize other scalable digital products and services such as video streaming, social media platforms, online learning delivery systems, and business analytics software
How elastic is the demand for the product? The answer lies in estimating the demand curve
Summary
E-commerce offers firms opportunities to price their goods and services dynamically using data-driven approaches [1,2,3,4,5,6]. This paper proposes an easy-to-implement intuitive experimental framework for deriving empirical demand schedules to inform pricing decisions We contend that this framework is widely applicable in the context of digital commerce and does not suffer from the drawbacks of econometric methods and standard economic theory. To alleviate these concerns and increase their customer base, firms may lower prices or intermittently offer discounts to maintain or increase the number of customers [18,19]. This strategy will undoubtedly reduce the average revenue per customer; whether the customer base grows sufficiently to yield a higher total revenue will remain to be seen. How elastic is the demand for the product? The answer lies in estimating the demand curve
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