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on Information and Communication Technologies |
| By: | Nicholas Economides; Joacim Tag |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:ste:nystbu:07-27&r=ict |
| By: | Anindya Ghose (Stern School of Business, New York University); Sha Yang (Stern School of Business, New York University) |
| Abstract: | The phenomenon of sponsored search advertising where advertisers pay a fee to Internet search engines to be displayed alongside organic (non-sponsored) web search results is gaining ground as the largest source of revenues for search engines. Using a unique panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates, bid prices and keyword ranks. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms’ decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. Our analysis provides some evidence that advertisers are not bidding optimally with respect to maximizing the profits. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. Finally, we conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet. |
| Keywords: | Online advertising, Search engines, Hierarchical Bayesian modeling, Paid search, Clickthrough rates, Conversion rates, Keyword ranking, Bid price, Electronic commerce, Cross-Selling, Internet economics. |
| JEL: | C33 C51 D12 L10 M31 M37 L81 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:0735&r=ict |
| By: | Jie Jennifer Zhang (College of Business Administration, University of Texas at Arlington); Bing Jing (Cheung Kong Graduate School of Business); |
| Abstract: | Online price comparison agents (shopbots) allow consumers to instantaneously receive price and other information from many online retailers. Online consumer clickstream data from ComScore Inc.demonstrate that consumers are increasingly using shopbots to conduct search. This phenomenon raises such questions as "how do shopbots change consumers’ search behavior?" and "do they reduce consumers’ online search?" Conventional wisdom suggests that consumers are expected to search less because shopbots have displayed prices and other relative information from retailers on the search result page(s). Surprisingly, this study demonstrates the opposite result. That is, consumers are actually visiting more online retailer web sites after using shopbots. This finding suggests that after searching for an item through a shopbot and receiving the price information, consumers will continue to look for detailed information about the online retailers by visiting their web sites. The empirical finding is explained by an analytical model, which shows that on the one hand shopbots reduce the marginal benefit of searching additional online stores; on the other hand they reduce the cost of search. Therefore whether shopbots reduce consumer search depends on the cost of reducing per unit of risk, which is decided by a number of factors, such as marginal search costs, price dispersion and quality differentiation among stores, price and quality correlation, and consumers’ relative preference for service quality. |
| Keywords: | Sequential Search; Online Behavior; Shopbots; Internet Retailing; Clickstream Data; Service Quality |
| JEL: | L15 C12 D11 D12 D83 |
| Date: | 2007–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:0734&r=ict |
| By: | Anja Lambrecht (London Business School); Katja Seim (Wharton School, University of Pennsylvania); Catherine Tucker (MIT) |
| Abstract: | Online applications and services automate communications and transactions between firms and consumers, promising large efficiency gains. However, consumers have been slow to use these online technologies intensively, despite widespread adoption of the internet. Customers frequently undergo a staggered adoption process that may involve sign-up, experimentation, trial, and substantial usage until they fully embrace internet services. We ask whether delays in moving through the initial stages of this adoption process contribute to consumers ultimately not using the service intensively. Such behavior would be consistent with laboratory findings on consumer memory. We explore this question using data from a German retail bank where only 24% of the customers who sign up for the bank's online banking service use it substantially. We use exogenous variation in delays in the adoption process, caused by vacations and public holidays in different German states, to identify this effect. We find that delays in the early stages of adoption significantly reduce a customer's probability of moving to substantial usage: A 10-day delay of a customer's first online login reduces the likelihood that she will ever use the technology substantially, by 33%. This effect is more severe for demographic groups with less online experience. |
| Keywords: | patents, technology adoption, adoption process, online services, banking |
| JEL: | M3 M30 M31 L1 L16 L86 |
| Date: | 2006–10 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:0740&r=ict |