Comparison-Shopping Channel Selection by Small Online Vendors

Author(s):  
Yun Wan ◽  
Nan Hu

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.

2009 ◽  
Vol 56 (4) ◽  
pp. 1040-1051 ◽  
Author(s):  
Srinivas Kota ◽  
Lalit Gupta ◽  
Dennis L. Molfese ◽  
Ravi Vaidyanathan

2021 ◽  
Author(s):  
◽  
Yu Ren

<p>Spectrum today is regulated based on fixed licensees. In the past radio operators have been allocated a frequency band for exclusive use. This has become problem for new users and the modern explosion in wireless services that, having arrived late find there is a scarcity in the remaining available spectrum. Cognitive radio (CR) presents a solution. CRs combine intelligence, spectrum sensing and software reconfigurable radio capabilities. This allows them to opportunistically transmit among several licensed bands for seamless communications, switching to another channel when a licensee is sensed in the original band without causing interference. Enabling this is an intelligent dynamic channel selection strategy capable of finding the best quality channel to transmit on that suffers from the least licensee interruption. This thesis evaluates a Q-learning channel selection scheme using an experimental approach. A cognitive radio deploying the scheme is implemented on GNU Radio and its performance is measured among channels with different utilizations in terms of its packet transmission success rate, goodput and interference caused. We derive similar analytical expressions in the general case of large-scale networks. Our results show that using the Q-learning scheme for channel selection significantly improves the goodput and packet transmission success rate of the system.</p>


2019 ◽  
Vol 144 ◽  
pp. 112-123
Author(s):  
Stephen S. Oyewobi ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz ◽  
Adeiza J. Onumanyi

2020 ◽  
Vol 105 ◽  
pp. 113558
Author(s):  
Lei Yen ◽  
Abebe Belay Adege ◽  
Hsin-Piao Lin ◽  
Ching-Huai Ho ◽  
Ken Lever

2012 ◽  
Vol 10 (8) ◽  
pp. 1682-1689
Author(s):  
R. Kaniezhil ◽  
C. Chandrasekar

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