Comparison-Shopping Channel Selection by Small Online Vendors: An Exploratory Study

Comparison-Shopping Channel Selection by Small Online Vendors: An Exploratory Study

Yun Wan (University of Houston-Victoria, USA) and Nan Hu (Singapore Management University, Singapore)
Copyright: © 2009 |Pages: 18
DOI: 10.4018/978-1-59904-978-6.ch007

Abstract

Comparison-shopping is becoming the mainstream marketing channel for B2C ecommerce. More and more small online vendors are using shopbots to bring in customers. There are mainly two types of shopbots: those general shopbots that provide product comparison cross multiple heterogeneous product categories (like shopping.com) and the specialized shopbots that provide comparison within a single or a few highly-related product categories (like addall.com on books and music CD). The effectiveness of shopbot selection strategy by small online vendors is the focus on this paper. By analyzing data from shopbots and online vendors, the authors found there is significant positive correlation between the number of shopbots an online vendor participates and its traffic rank, which indicates the general effectiveness of using shopbots as a marketing channel. They also found that for online vendors competing on a niche product like college textbook, participating specialized shopbots brings in higher traffic. For competing in mainstream market, there is less significant correlation between participating general shopbots and higher traffic rank for vendors. They conclude that using general shopbots is a reactive strategy for small online vendors while using proper specialized shopbots could provide an edge for small online vendors.
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Introduction

The expansion of the B2C electronic market keeps attracting brick and mortar vendors to join in the online market. However, it is not easy for new online vendors, especially small online vendors, to compete effectively with behemoths like Amazon.com or WalMart.com by using traditional Web advertising strategies like banner ads. Nor do small online vendors have the deep pockets to invest heavily in website design to enhance consumer experiences.

Recently the emergence of comparison-shopping services, like shopping.com and mysimon.com, provides a relatively cost-effective way for most small online vendors to use their most important advantage over established portals– the lower price and flexible product offering. As a result, we observed an increasing number of online vendors participating comparison-shopping in the last four years.

Sensing the opportunity in Shopbots, private equities flooded into this market and various comparison-shopping services have mushroomed since 1997. Among them, the most popular Shopbots include Shopping.com, Shopzilla.com, and PriceGrabber.com. Established players also began to provide their comparison-shopping services like Yahoo Shopping, MSN Shopping and Froogle by Google.

Thus, we are observing an increasingly dynamic market consisting of online shoppers, online vendors, and shopbots. In this market, channel selection by small online vendors, or how small online vendors select shopbots as their new channel to bring in online shoppers, is important for our understanding of this market as we will explain in more details in subsequent sections.

Due to the dynamic nature of online retailing and the comparison-shopping market, we lack comprehensive statistics in this domain. Web technology already enables a one-man-store to be set up in one hour and, at the same time, intensive competition could make a large number of such stores disappear overnight. Since tracking the full details of this fast-changing new market, including individual vendor strategies, is almost impossible, we take a step back by studying a “snapshot” of this dynamic and share some preliminary findings in this direction.

Subsequent sections are organized as follows: In part I, we introduce the new online channel of comparison-shopping and then review the current status of and existing studies on comparison-shopping. In part II, we propose our study’s methodology, research model, and hypotheses. We also explain our data collection procedure. In part III, we present our findings by analyzing the obtained data. In part IV, we draw our conclusion and propose future research directions.

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