Role of Consumer Targeting in E-commerce Marketplaces:Sponsored Versus Organic Product Listing
Social Science Research Network(2020)
University of Texas at Dallas | University of Florida
Abstract
E-commerce marketplaces have been slowly replacing organic product listing with sponsored product listing in prominent positions of consumer search results. Marketplaces receive listing fees in addition to sales commission in sponsored listings, whereas they receive only the latter in organic listings. Moreover, marketplaces have access to vast amounts of consumer data which enables the marketplaces to target consumers profitably. We demonstrate that consumer targeting plays a central role in how the listing type affects various stakeholders. We show that marketplaces indeed have an incentive to switch to sponsored product listing from organic product listing, even though such a switch intensifies the price competition between sellers and reduces the marketplaces’ sales commission. The more intense price competition between sellers benefits consumers, but it, along with the listing fees, hurts sellers. The marketplace’s switch to the sponsored product listing hurts the social welfare by increasing the mismatch between preferred and purchased products for some consumers. The primary driver of these impacts is that the marketplaces’ incentive to precisely target consumers diminishes if they switch to sponsored product listing. An improvement in the targeting precision softens the price competition between sellers as well as the competition between them for the desirable slot in sponsored product listing; while the former effect increases the marketplace’s sales commission,the latter effect decreases listing fees. Therefore, when sales commission is the sole revenue source, as in the case of organic product listing, marketplaces have a higher incentive to improve targeting precision compared to when listing fees is also a revenue source, as in the case of sponsored product listing.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper