29,012 research outputs found
Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data
Widespread e-commerce activity on the Internet has led to new opportunities
to collect vast amounts of micro-level market and nonmarket data. In this paper
we share our experiences in collecting, validating, storing and analyzing large
Internet-based data sets in the area of online auctions, music file sharing and
online retailer pricing. We demonstrate how such data can advance knowledge by
facilitating sharper and more extensive tests of existing theories and by
offering observational underpinnings for the development of new theories. Just
as experimental economics pushed the frontiers of economic thought by enabling
the testing of numerous theories of economic behavior in the environment of a
controlled laboratory, we believe that observing, often over extended periods
of time, real-world agents participating in market and nonmarket activity on
the Internet can lead us to develop and test a variety of new theories.
Internet data gathering is not controlled experimentation. We cannot randomly
assign participants to treatments or determine event orderings. Internet data
gathering does offer potentially large data sets with repeated observation of
individual choices and action. In addition, the automated data collection holds
promise for greatly reduced cost per observation. Our methods rely on
technological advances in automated data collection agents. Significant
challenges remain in developing appropriate sampling techniques integrating
data from heterogeneous sources in a variety of formats, constructing
generalizable processes and understanding legal constraints. Despite these
challenges, the early evidence from those who have harvested and analyzed large
amounts of e-commerce data points toward a significant leap in our ability to
understand the functioning of electronic commerce.Comment: Published at http://dx.doi.org/10.1214/088342306000000231 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
A proposed marketing strategy for GO2HK.COM
Within these few years, the Internet becomes popular in the world. There are many well-known websites, such as the eBay, Amazon and Yahoo, etc.. In foreign countries, Web auction is very famous for the Internet users in foreign countries, while it is a new kind of electronic business in Hong Kong. The trend of web auction has penetrated into Hong Kong in this few years, but it is still at a growth stage of the industry life cycle.
Web auction seems to be a new type of business in electronic commerce, thus it can attract the people to participate it in future. The potential market for web auction is quite large. In Hong Kong, there are four major companies that work on the business of web auction. They are the (1) Red-dots, (2) Go2hk, (3) Yahoo and (4) Clubciti. Whereas, go2hk is the smallest company in terms of company size, it is the second in terms of the number of registered users. The industry is growing rapidly and facing a keen competition. Therefore, the company needs to have good marketing mix strategies to establish its brand name.
This project aims to purpose an appropriate marketing strategy of GO2HK.COM, which can allow the company to match the consumer needs with marketing strategy. Interviews are conducted and questionnaire surveys are initiated to look into the most preferable strategy for GO2HK.
Before doing the questionnaire survey, we have interviewed the company, in order to identify the current marketing mix first. After that, questionnaire surveys are used to access the information about the procedure of non-users, sellers and bidders.
Based on the findings, a proposed marketing strategy is recommended for go2hk. To conclude, security is the most critical aspect that both users and non-users are highly concerned
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting
Born to trade: a genetically evolved keyword bidder for sponsored search
In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click.
In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009
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