Consumer Search Behavior Research Articles

International Real Estate Review

Due to housing stock heterogeneity, most academic discussions on price dispersion in the housing market have traditionally focused on the search behavior of consumers and neglected the housing and neighborhood characteristics that are related to price dispersion. This study applies a rich empirical data set from Taipei to explore the neighborhood characteristics that are associated with a higher degree of dispersion in housing price and associated likelihood of such. We track the housing transactions at the residential community level, and group the communities based on the coefficient of variation of the transaction prices in each community after controlling for community and housing characteristics. We apply a multinomial logistic regression to examine which neighborhood characteristics are more likely to be associated with higher price dispersion. We find that communities with higher price levels and built by government agencies are less likely to have high price dispersion, while those that are older, priced lower or have a minimum floor area of 50 pings are more likely to have higher price dispersion.

AKEGIS: automatic keyword generation for sponsored search advertising in online retailing

Sponsored search advertisers face several complex decisions when planning and implementing a new sponsored search advertising campaign. These decisions include the selection of keywords, the definition of landing pages, and the formulation of bidding strategies. Relatively low attention has been paid on supporting the selection of keywords in recent research and most studies on sponsored search advertising focus on the formulation of bidding strategies and strategies for budget planning. We present a novel approach for automatically generating sponsored search keywords that relies on the theory of consumer search behavior. Our approach uses an online store's internal search log to extract keywords used by consumers within their search process, because recent research has shown that especially consumers with a high conversion probability that exhibit goal-directed instead of exploratory search patterns use an online store's internal search engine. We empirically test our approach based on a store's internal search engine and identify the effects of this approach by comparing it to a state-of-the-art approach. Our analysis reveals that our approach substantially increased the number of profitable keywords, improved the store's conversion rate by approximately 41%, and decreased the average cost per click by more than 70%.

Price Uncertainty and Market Power in Retail Gasoline: The Case of an Italian Highway

We quantify the effect of consumers’ price uncertainty on gasoline prices and margins on an Italian highway. We observe the change in prices triggered by a longitudinal policy-based change in consumers’ price information from one in which drivers on the highway have no information on the prices of stations they encounter to one that allows consumers to observe the prices of four upcoming stations on a single price sign by the side of the highway. Using these data, we estimate a model of consumer search and purchase behavior and a corresponding model of gas station pricing. We then measure the impact of varying degrees of price information on equilibrium prices, including (i) no price information, (ii) the current policy, and (iii) full price information. We also compare the current policy with an alternative policy in which stations’ prices are advertised with individual price signs. We find that when consumers do not have price information, gas stations are able to charge 31% more, in terms of higher price-cost margins, than when prices are known. Our welfare analysis suggests that price information is worth €0.57 to consumers every time they take the highway. Relative to the current mandatory policy, advertising price on individual signs is worth €0.19 more to consumers. The e-companion is available at https://doi.org/10.1287/mksc.2018.1105 .

Large-Scale Demand Estimation with Search Data

In many online markets, traditional methods of demand estimation are difficult to implement because assortments are very large and individual products are sold infrequently. At the same time, data on consumer search (i.e., browsing) behavior are often available and are much more abundant than purchase data. We propose a demand model that caters to this type of setting. Our approach is computationally light and allows for flexible cross-price elasticities that are informed by search patterns. We apply the model to a data set containing search and purchase information from a retailer stocking almost 600 products, recover the elasticity matrix, and solve for optimal prices for the entire assortment.

Estimation of Preference Heterogeneity in Markets with Costly Search

We study the estimation of preference heterogeneity in markets where consumers engage in costly search to learn product characteristics. Costly search amplifies the way consumer preferences translate into purchase probabilities, generating a seemingly large degree of preference heterogeneity. We develop a search model that allows for flexible preference heterogeneity and estimate its parameters using a unique panel dataset on the search and purchase behavior of consumers. The results reveal that when search costs are ignored, the model overestimates standard deviations of product intercepts by 53%. We show that the bias in heterogeneity estimates leads to incorrect inference about price elasticities and seller markups and has important consequences for personalized pricing.

Consumer search on the Internet

This paper analyzes search frictions in online markets using data depicting the web browsing and purchasing behavior of a large panel of consumers. In this data, consumer search behavior is observed prior to a transaction. I use data on consumers shopping for books online to link prices and consumer search patterns at different bookstores, estimating consumer search costs in the context of a fixed-sample search model. The search patterns indicate that consumers visit relatively few firms and exhibit a strong search preference for prominent retailers. I control for search intensities at different retailers during consumers’ search process and find that search cost estimates are lower than when assuming consumers sample equally among alternatives. Accounting for heterogeneity in consumer search intensities across retailers reduces search cost estimates from $2.30 to $1.24 per search. I examine search cost heterogeneity by using a rich set of consumer characteristics and relating them to search patterns and search costs estimates. I use a flexible random effects model in which the number and order of firms visited by the consumer are her optimal ordered choices, allowing search cost cutoffs to depend on regressors. The estimates indicate that consumer search costs are related to their observable characteristics, such as income, where individuals with income greater than $100,000 incur relatively higher search costs.

Comparison of Periodic Behavior of Consumer Online Searches for Restaurants in the U.S. and China Based on Search Engine Data

Increased knowledge about the online search behavior of restaurant consumers is valuable to restaurant management and marketing professionals. However, people in different countries may demonstrate distinctive online search behaviors. There has been a lack of cross-cultural research on the online search behavior of restaurant consumers. In this paper, the periodic nature of online search behavior demonstrated by restaurant consumers from U.S. and China is analyzed and compared using Fourier transform and Parseval’s theorem. The search interest records from Google and Baidu, respectively, are used. The results reveal that the online search behavior of restaurant consumers in the U.S. is strongly governed by weekly cycles but less dependent on annual cycles; however, the analogous consumer behavior in China exhibits less dependence on weekly cycles. The theoretical and practical implications of the research are discussed.

An Empirical Study on the Impact of Micro-credit Marketing on Consumer Information Search Behavior

With the continuous growth of WeChat users, many merchants use micromessaging platform as an important channel for marketing promotion. Based on TRA and TPB theory, this paper puts forward the relationship between perceived risk, product knowledge, interaction, information quality, Psychological impact on consumer search behavior and explore the impact of private relations on the two, build the model and put forward the relevant assumptions, through the questionnaire survey and empirical analysis to verify the model and assumptions, according to the results for the WeChat business to provide marketing recommendations to help WeChat marketing benign development.

The order effect of advertisers on consumer search behavior in sponsored search markets

This research examines consumer search behavior across multiple ordered advertisers in sponsored search markets. To consider the sequential nature of consumer search, we model search behavior as a set of interdependent ordered decisions: namely, the entry, click, and stop decisions. Using individual-level data obtained from a search engine, we identify the key determinants of consumer search behavior in the sponsored section of search results. Our results reveal that there is significant variation in the effects of advertisers' latent brand equity and website quality on the search decisions. Thereby, search behavior heavily depends on the ordered composition of advertisers in sponsored listing. We highlight the ability of our model to quantify the order effect of advertisers on the number of clicks on sponsored ads, which can assist the search engine and advertisers in predicting the click counts and assessing the value of ad positions.

P.1.a.019 - Epigenetic and transcriptional changes in the CRH gene are related to severity of suicide attempt and a general psychiatric risk score in adolescents

We compare mobile and desktop user progress through the conversion funnel. Using detailed log-files of an online flight search engine, we analyze consumer search behavior and model the stages consumers go through along the funnel in both mobile and desktop platforms. We ask: Do mobile and desktop users go through similar funnel stages? Are the funnel stages of both platforms characterized differently? To analyze the progress through the funnel, we use hidden Markov models (HMM) that capture latent funnel stages based on observable search-behavior variables indicative of convergence towards a purchase. We find that while similar stochastic processes characterize funnel stages on both platforms, the distribution of visits across stages in the two platforms is significantly different: a larger percentage of mobile visits resides in more advanced stages, indicating that mobile consumers use the website when they are more advanced in the funnel, where less cognitive effort is required.

Estimating Search Benefits from Path-Tracking Data: Measurement and Determinants

We study consumer search behavior in a brick-and-mortar store environment, using a unique data set obtained from radio-frequency identification tags, which are attached to supermarket shopping carts. This technology allows us to record consumers’ purchases as well as the time they spent in front of the shelf when contemplating which product to buy, giving us a direct measure of search effort. We estimate a linear regression of price paid on search duration, in which search duration is instrumented with a search-cost shifter. We show that this regression allows us to recover the marginal return from search in terms of price at the optimal stopping point for the average consumer. Our identification strategy and coefficient interpretation are valid for a broad class of search models, and we are hence able to remain agnostic about the details of the search process, such as search order and search protocol. We estimate an average return from search of $2.10 per minute and explore heterogeneity across consumer types, product categories, and category location in the store. We find little difference in the returns from search across product categories, but large differences across consumer types and locations. Our findings suggest that situational factors, such as the location of the category or the timing of the search within the shopping trip, are more important determinants of search behavior than category characteristics such as the number of available products. Data are available at https://doi.org/10.1287/mksc.2017.1026 .

Geographical dispersion of consumer search behaviour

ABSTRACTThis article investigates whether consumer search behaviour differs across zip codes within the U.S. As an application, daily gasoline price data covering virtually all gas stations within the U.S. are employed to estimate the distribution of search costs in each zip code. The results show that there are significant differences across zip codes regarding the expected number of searches achieved before consumers purchase gasoline. In order to have a systematic explanation, such differences are further connected to geographic, demographic, and economic conditions of the zip codes in a secondary analysis. The corresponding results imply several strategies for gas stations in order to maximize profits/markups; suggestions follow for policy makers and regulators to reduce redistributive effects of information barriers across locations.

Insurance Search and Switching Behavior

This paper looks into the search behavior of consumers in the market for health insurance contracts. We consider the recent health insurance reform in The Netherlands, where a private-public mix of insurance provision was replaced by a system based on managed competition. Although all insurers offer the same basic package (determined by the government), there is substantial premium dispersion. We develop a consumer search model containing the main features of the Dutch health insurance system. This model provides us with a number of hypotheses, which we test using data from the Dutch Health Care Consumer Panel. We find that the simple consumer search model describes the choice for insurance coverage well, but fails in explaining individual search decisions. We argue that search costs are heterogeneous and related to knowledge about the system. In this case, group contracts offered by insurers, might cause thirddegree price discrimination and reduce access to health care for some groups of individuals.

Attribute Search in Online Retailing

Online retailing has created an empirical opportunity to examine consumer search behavior using click stream data. In this article we examine the implications of greater variety online for consumer search intensity, and equilibrium prices. We test our hypothesis using consumer data on online search and purchase behavior from the comScore Web Behavior Panel. We find that search intensity systematically decreases in categories with broader product ranges, and equilibrium prices rise. Our findings suggests that broader product ranges in online retail markets can produce anti‐competitive effects that are mediated through equilibrium responses in consumer search behavior.

Geographical Dispersion of Consumer Search Behavior

This paper investigates whether consumer search behavior differs across zip codes within the U.S.. As an application, daily gasoline price data covering virtually all gas stations within the U.S. are employed to estimate the distribution of search costs in each zip code. The results show that there are significant differences across zip codes regarding the expected number of searches achieved before consumers purchase gasoline. In order to have a systematic explanation, such differences are further connected to geographic, demographic and economic conditions of the zip codes in a secondary analysis. The corresponding results imply several strategies for gas stations in order to maximize profits/markups; suggestions follow for policy makers and regulators to reduce redistributive effects of information barriers across locations.

An Investigation into Consumer Search and Evaluation Behaviour: Effect of Brand Name and Price Perceptions

Past researchers extended the conceptualized model of perceived value to include the effects of multiple extrinsic cues used as indicators of quality. The need for replication as well as extending the research findings has, however, been expressed unequivocally by the researchers as it remains to be seen if the existing relationships would hold for consumers hailing from emerging nations. The present study extends the theoretical and empirical evidences regarding the structural relationships of consumers’ perceptions of marketing cues, namely, price and brand name, with the perceptions of quality, risk, sacrifice and value in the Indian context. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) based analysis of questionnaire-based responses of young Indian consumers, the study reports that high price is associated with the presence of better product features, assuming that a good quality product always comes with a high price attached and there is a possible shifting from looking to lower price-product options to alternatives which boast of a favourable brand name indicated by a higher price level. The study holds implications for marketers in regard to brand name positioning as well as planning communication and retail strategies.

Deal-Seeking Versus Brand-Seeking: Search Behaviors and Purchase Propensities in Sponsored Search Platforms

Using a database of 11,001 unique sponsored search keywords, we investigate the relationship between the characteristics of keywords oriented around deal-seeking and brand-seeking and consumer search behaviors and buying propensities. On the basis of the search depth versus search breadth framework, we hypothesize that deal-seeking keywords elicit a search of greater breadth, whereas brand-seeking keywords induce a search of greater depth. We also explore the moderating effect of product type (search or experience goods) on the relationship between keyword characteristics and consumer search behaviors and how high-demand seasons (e.g., scheduled sales) influence consumers’ search and purchase behaviors. In addition, we estimate the effectiveness of keywords on the basis of their sales-per-cost performance. The findings indicate that search queries containing deal-seeking keywords are associated with higher click-through rates and conversion rates than are search queries without such keywords. We also find that the positive effect of deal-seeking keywords on click-through rates is more pronounced for experience goods than for search goods. However, we identify a negative interaction between experience goods and brand-seeking keywords. A comparison of deal-seeking and brand-seeking keywords in terms of cost effectiveness reveals that deal-seeking keywords generate approximately three times the sales of those produced by brand-seeking keywords.

How consumers search online‒and on the go

From: Bart Bronnenberg, Jun Kim, and Carl Mela, “Zooming in on choice: How do consumers search for cameras online?” Working paper (2014), and Stephan Daurer, Dominik Molitor, Martin Spann, and Puneet Manchanda, “Consumer search behavior on the mobile internet: An empirical analysis.”MSI Report No. 15–111.

Price-Match Announcements in a Consumer Search Duopoly

Using a model of sequential search, we show that announcements to price-match raise prices by altering consumer search behavior. First, price-matching diminishes firms’ incentives to lower prices to attract consumers who have no search costs. Second, for consumers with positive search costs, price-matching lowers the marginal benefit of search, inducing them to accept higher prices. Finally, price-matching can lead to asymmetric equilibria where one firm runs fewer sales and both firms tend to offer smaller discounts than in a symmetric equilibrium. Price increases grow in the proportion of consumers who invoke price-matching guarantees and in the level of equilibrium asymmetry.

Role of Advertising and Consumer Interest in the Motion Picture Industry

AbstractAdvertising is commonly used as a major marketing tool by many firms to support their new product release. Quantifying the effectiveness of pre-release advertising campaigns, however, is both challenging, since no sales data are available, and costly, because of the need to conduct consumer surveys. This is especially true for movie industry for which the majority of advertising dollars are spent before the movie’s release. Using the recent availability of online data on consumer search behaviors on a popular website dedicated to the movie industry, we construct a consumer interest measure to help the decision makers evaluate their advertising effectiveness. We build a dynamic model to show how this cost-efficient measure of consumer interest can be used to capture pre-release advertising dynamics, and the impact of advertising on weekly movie revenues starting with the opening week. Using a procedure based on