Demand Analysis and Customized Product Offering Design on E-Commerce Platform

TL;DR

This paper develops a novel demand analysis method for e-commerce platforms that incorporates product customization factors like quantity and pack size, aiming to better predict consumer demand and optimize revenue.

Contribution

It introduces an extended domain of consumer choice and a generalized price response function tailored for e-commerce, enhancing demand prediction accuracy.

Findings

Demand function can be more accurately modeled with the extended choice domain.

Customized product offerings improve revenue optimization.

The proposed price response function has standard properties applicable to online markets.

Abstract

It can be observed that the purchasing decision of an individual consumer in an electronic marketplace is determined by a set of factors, such as personal characteristics of the consumer, product pricing, minimum price-quantity combination offered, decision-making space, and underlying motivation of the consumer. These factors are combined to form a consumer's choice problem domain, which plays a pivotal role in the product offering. In this study, we attempt to focus on how the products? Offered can be customized by incorporating the quantity and pack size of the products along with the factors above to form a more extensive domain for examining the combined effects of all of these factors on demand. Accordingly, the demand function is defined by a novel method invoking the extended domain of choice problem in the electronic marketplace. Consequently, the predictable uncertainty associated with the consumer's demand function may disappear, increase the likelihood of earning optimum revenue through customized combinations of the components of the extended domain of choice problem, and improve the understanding of the fluctuations in consumer demand. Finally, we propose a generalized price response function with standard properties applicable to E-Commerce.