price comparison website research paper

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Peer-reviewed

Research Article

Price Comparisons on the Internet Based on Computational Intelligence

Affiliation Department of Industrial & Management Systems Engineering, Dong-A University, Busan, Korea

* E-mail: [email protected]

Affiliation School of Business Administration, Kyungpook National University, Daegu, Korea

• Jun Woo Kim, 

• Published: September 30, 2014
• https://doi.org/10.1371/journal.pone.0106946
• Reader Comments

Information-intensive Web services such as price comparison sites have recently been gaining popularity. However, most users including novice shoppers have difficulty in browsing such sites because of the massive amount of information gathered and the uncertainty surrounding Web environments. Even conventional price comparison sites face various problems, which suggests the necessity of a new approach to address these problems. Therefore, for this study, an intelligent product search system was developed that enables price comparisons for online shoppers in a more effective manner. In particular, the developed system adopts linguistic price ratings based on fuzzy logic to accommodate user-defined price ranges, and personalizes product recommendations based on linguistic product clusters, which help online shoppers find desired items in a convenient manner.

Citation: Kim JW, Ha SH (2014) Price Comparisons on the Internet Based on Computational Intelligence. PLoS ONE 9(9): e106946. https://doi.org/10.1371/journal.pone.0106946

Editor: Catalin Buiu, Politehnica University of Bucharest, Romania

Received: March 20, 2014; Accepted: August 10, 2014; Published: September 30, 2014

Copyright: © 2014 Kim, Ha. This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files. The experiment data in this paper was collected from the famous Korean price comparison site, Danawa ( www.danawa.com ) in July, 2011. The data is available at the author's home page as follows: CSV format: http://web.donga.ac.kr/kjunwoo/price_comparison/UltraThinLapTop.csv , MS Excel format: http://web.donga.ac.kr/kjunwoo/price_comparison/UltraThinLapTop.xlsx . In addition, the simple descriptions for the files are provided at http://web.donga.ac.kr/kjunwoo/price_comparison/ReadMe.txt .

Funding: This research was supported by Dong-A University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The Internet is a fundamental infrastructure that integrates distributed and heterogeneous networks, communication, and information systems to provide information-convergent computing environments [1] . In addition, new communication technologies have changed the manner in which individuals access and acquire information from various information sources [2] . Many Web sites and Web services are based on the flux of information convergence and Web users enjoy a wide access to abundant information from various sources through consolidated channels, services, and Web sites, among other means [3] .

However, end users may have some difficulty in combining, transforming, and processing massive amounts of gathered information, which may result in irrelevant search results, fraudulent transactions, and dispersed information [4] – [5] . Therefore, many users may become disoriented and face worsening problems of information overload and uncertainty when browsing information-intensive Web sites [6] .

A good example is a price comparison site (PCS), (also known as shopbots or comparative shopping agents), providing online shoppers with opportunities to acquire a wide range of information on various products. It is well known that a PCS can help online shoppers reduce the amount of time or effort required when searching for products online [7] – [10] . However, such sites are generally designed to focus mainly on the needs of “expert” shoppers. Therefore, many users tend to be overwhelmed by the enormous amount of information on a plethora of products from various vendors [11] . In addition, there are two major approaches to information-seeking through the Web, i.e., direct searching and browsing [12] . Conventional PCSs are generally suitable for direct searches, which focus on locating the required information on specific products, but do not effectively support browsing, which focuses on finding “something useful.”

For this study, an intelligent product search system was developed that enables PCSs to support novice shoppers specifically by accommodating user-defined price ranges. Herein, a “novice shopper” is defined as an online shopper who is interested in a certain product category and wishes to make a purchase within an approximate budget, but who is having difficulty selecting a specific product owing to a lack of prior knowledge on the target product category.

For this study, linguistic price ratings and linguistic product clusters were therefore devised that employ a linguistic-semantic extraction technique such as fuzzy logic [5] [13] – [14] and data mining [15] , which have emerged as useful tools for processing information collected from Web sites and providing personalized Web services [4] [6] . In addition, the present study provides important insight into various problems embedded in information-intensive Web sites such as PCSs, and suggests some service strategies for addressing these problems.

The rest of this paper is organized as follows. Section 2 provides a review of previous research on PCSs. Section 3 explains the limitations of existing PCSs and describes the overall framework of the proposed intelligent product search system. Section 4 provides the experimental results obtained by applying the proposed system to a popular PCS in Korea. Finally, Section 5 provides some concluding remarks and discusses some interesting avenues for future research.

Literature Review

As online shopping increases in popularity, PCSs have become one of the most important Web-based business intermediaries for both merchants and online shoppers [16] . Typically, comparison sites gather information on products and their prices imposed by different merchants, and enable online shoppers to select products and merchants to make purchase decisions in effective manners [17] . It is well known that such Web sites can dramatically reduce the search cost during online shopping [16] , which has led many online shoppers to begin their purchasing procedure by visiting a PCS such as Nextag.com , PriceGrabber.com , or Bizrate.com [18] – [20] .

Owing to their widespread use, PCSs have attracted a great deal of attention from researchers and practitioners. Typically, the role of a PCS is to locate the best merchant quoting the lowest price for a specific product. In this context, many previous researchers have approached the use of PCSs from social and economic perspectives, and the price dispersion has been a major topic of research [21] . Indeed, many studies have suggested that the low search cost of a PCS can facilitate the convergence of prices for identical products [8] . However, the price dispersion still remains, and some studies have reported that the extent of such price dispersion may be influenced by various factors such as the product category, number of sellers, and market imperfections [7] [22] – [24] . Similarly, user behaviors and industrial influences on PCSs in providing accessibility to the lowest prices have also been discussed and actively studied [25] – [28] .

In addition, owing to the large number of similar products and merchants on the Web, online shoppers may feel disoriented when facing the massive amount of information provided by a PCS [29] – [31] . Indeed, conventional price-comparison agents help in determining “where to buy” a specific product; however, they do not appropriately support individual shoppers in determining “what to buy.” That is, it is generally assumed that online shoppers visit a PCS after determining to purchase a specific product [16] . However, it is well known that the shopping process generally starts with the “what to buy” phase, where the shoppers determine specific products suitable for their customized needs [32] . Therefore, traditional filtering and order-based PCSs are insufficient, and a more comprehensive and intelligent purchase-decision support is required [5] [33] – [34] .

There have been several studies dealing with purchase-decision support of PCSs. Yuan [22] argued that prices are insufficient for finding recommendable products, and proposed an intelligent comparison-shopping agent that provides online shoppers with personalized product rankings generated through the application of reinforcement learning to product/merchant information and consumer behavior/preferences. Garfinkel et al. [35] and Garfinkel et al. [36] developed a recommendation system that embeds an integer-programming model allowing users to choose the best products while taking into account cost savings through a bundling of products. Lim et al. [9] proposed a rule-based comparison-shopping framework using the eXtensible Rule Markup Language architecture, which computes the exact personalized delivery cost to find the optimal merchant.

Intelligent Price Comparisons for Online Shoppers

Existing studies are limited in that they generally assume that shoppers are experts whose search strategies are direct, that is, the shoppers have clear product knowledge or preferences. In contrast, this study focuses on novice shoppers who have no clear and sufficient prior knowledge of the target product category during online shopping, and are often anonymous PCS users.

Moreover, uncertainties in an online shopping environment should be dealt with in order to provide novice shoppers with comprehensive purchase-decision support. There are two types of uncertainties for novice shoppers. First, their objectives inherently tend to be vague in that they may not have decided on the manufacturer, seller, or acceptable price of the product they are considering. Second, many PCSs use prices quoted by the merchants, which contain price dispersions, errors, and click baits. Such uncertainties, noise, and fuzziness have seldom, if ever, been considered in the context of online shopping; however, PCSs should become more robust to such factors.

To address these two issues, this study proposes an intelligent product search system that is developed by refining and extending the fuzzy-semantic information management system [5] . The proposed system extracts the linguistic semantics from the product price dispersion on the Web, and produces a personalized product list for individual online shoppers. In doing so, the system uses a novel semantic procedure based on fuzzy logic and data mining, and is robust to the uncertainties, noise, and fuzziness of online shopping environments. Consequently, it is expected that the proposed product search system enables novice shoppers to make purchase decisions in a more convenient and intelligent manner.

3.1 Decision-making process for a conventional price comparison site

Modern PCSs provide users with a vast amount of information on a broad range of products, including appliances, computers, clothing, and cosmetics. Therefore, an individual user first selects a product category they are interested in, and the PCS then displays a list of products belonging to the selected category. Because online shoppers tend to be price sensitive, this list often contains the lowest prices for each product. This is the situation with which PCS visitors are commonly faced.

However, because a number of sellers charge different prices for identical products, users need to select a specific product from the product list to check the list of sellers and their prices. For example, Figure 1 shows a popular PCS in Korea. The upper panel in the figure lists products belonging to the category of “laptop computers,” and the lower panel lists the prices and sellers of a specific product.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0106946.g001

Considering a list of sellers offering a selected product, the user identifies the seller offering the best deal and can click on the hyperlink to that seller's Web page, where the user can obtain more information on the seller's offerings before making an actual purchase. Note that the role of a PCS differs from that of an online shopping mall in that individual users cannot make purchases directly on a PCS. That is, a PCS acts as an intermediary between individual online shoppers and sellers, and the main benefit of visiting such sites is that individual users can obtain information required for making a purchasing decision. Figure 2 summarizes the purchase decision-making process for existing PCSs.

https://doi.org/10.1371/journal.pone.0106946.g002

However, users may have difficulty in making a purchasing decision using a PCS because of the massive amount of information provided by such sites. Although PCSs typically allow users to filter or sort the product and seller lists, as shown in Figure 1 , for an efficient search, this problem still remains for the following two reasons:

(a) An individual user can be a novice with respect to the product that they wish to buy. That is, the user may have little prior knowledge regarding the target product category. In this case, it may be difficult for a novice shopper to efficiently filter through a list provided by a PCS and search for a desired product.

(b) User searches are often not direct in that a user may want to buy a product within a general product category despite not having decided on a specific product. Although users may browse and investigate the product and seller lists in an ad-hoc manner, this can be a time-consuming task because of the large amount of information provided by a modern PCS.

In this study, a novice online shopper is defined as a user satisfying both (a) and (b) above. In addition, it is clear that prices represent one of the most important factors in purchasing decisions, and that online shoppers tend to initiate the purchase decision-making process by establishing an approximate budget for their purchase items. For example, a novice shopper may intend to buy “a laptop computer for about $500” or “a laptop computer for $500 to $600.” In this case, the shopper who may lack sufficient domain knowledge regarding the product category is likely to be overwhelmed by the massive amount of information provided by the product and seller lists. Thus, they may have difficulty using an existing PCS and buying a product within a pre-determined product category.

Nevertheless, PCSs should be able to support online shoppers in an effective manner if they can appropriately facilitate the processing of the collected information. In this regard, focusing on novice shoppers interested in buying products under rough budget constraints, this study devises a novel framework under which PCSs can provide their users with intelligent support. Consequently, the following issues should be addressed:

(1) PCSs should provide novice shoppers with appropriate domain knowledge regarding the target product categories. For example, PCSs can determine whether a user's budget is more suitable for lower- or higher-priced products. Identifying the features of a product that provide a good fit based on the novice shopper's budget should help the shopper better understand the target product category and adjust their initial budget based on such features and their specific needs.

(2) PCSs should identify products that can be recommended to a novice shopper under the target product category. The novice shopper can then focus on the recommended products, which reduces their burden in terms of having to investigating a large amount of information.

In achieving objectives (1) and (2) above, the fuzziness of modern online shopping environments must be considered. A set of products that catch an individual user's interest are considered a fuzzy set, and not a crisp one. For example, consider a novice shopper wanting to buy a laptop computer within the price range of $500 to $600. Listing those products whose prices fall between $500 and $600 is a relatively easy task, but the user may also be interested in a laptop computer whose price falls within a different range. Similarly, a laptop computer that is $480 may be acceptable to a certain extent, although it may be less preferable than computers priced between $500 and $600.

Here, another case of fuzziness lies in the price of a given product. PCSs generally offer seller and price lists even for a single product. That is, the prices of a particular product on the Web may vary widely, and products are usually filtered and sorted according to their price. For most PCSs, the lowest price is generally used as the representative price of a given product. However, the lowest price of a given product is sometimes of little use for the following reasons: First, an exceptionally low price may be a mistake. Second, such prices tend to include products with limited specifications. Finally, such prices are generally achieved through special promotional campaigns. In this context, to enhance the relevance and understandability of the retrieved information, PCSs should address the limitations of using a crisp price in an appropriate manner.

3.2 Decision-making process using linguistic prices and linguistic product clusters

An intelligent product search system is devised to enhance the usability and relevance of PCSs for online shoppers (refer to Figure 3 ). The proposed system assumes that an individual user has already determined the target product category and has a rough budget. Therefore, the novice shopper first selects a product category based on the procedure for the particular PCS, and then sets a price range corresponding to their rough budget.

https://doi.org/10.1371/journal.pone.0106946.g003

The intelligent product search system then extracts the fuzzy semantics of each product within the product category to produce the fuzzy-semantic fitness, which is a row vector containing a product's membership grade for linguistic price labels such as “cheap,” “good fit,” and “expensive,” to determine whether the product fits the price range set by the user. Next, the system clusters the products based on the fuzzy-semantic fitness vectors to obtain linguistic product clusters consisting of products with similar membership grades.

The proposed system uses these product clusters and the fuzzy-semantic fitness of each product to construct a personalized search result that provides the shopper with a better understanding of the target product category and that facilitates their purchasing decision.

3.2.1 Linguistic prices based on fuzzy semantics.

Fuzzy logic is a popular heuristic technique used for reasoning regarding the uncertainty inherent to words with ambiguous meanings. For an explanation of the fuzzy semantic extraction phase, consider an online shopper who wants to buy a laptop computer for $500 to $600. Existing PCSs can provide a list of laptop computers within this price range, but omit those computers that are priced below $500 or above $600, even though such computers may be attractive to the user. In this context, a set of products that fit the price range set by the user should be a fuzzy set.

Let x denote the price of a given product and the interval [ P min , P max ] denote the user-defined price range. If x << P min , then the product is not recommended because its price is too low to satisfy the user. Similarly, if x >> P max , then the product is not affordable. Suppose that three linguistic values, cheap ( L 1 ), good fit ( L 2 ), and expensive ( L 3 ), can be assigned to a given product. Each linguistic value has an associated fuzzy set that indicates the degree of membership a specific price has within the set representing the associated linguistic value. For example, the fuzzy set associated with cheap maps each numeric price to a value between zero and 1. The output of the mapping indicates the probability that each price is a member of the cheap fuzzy set. Similar statements hold true for the fuzzy sets associated with good fit and expensive .

https://doi.org/10.1371/journal.pone.0106946.g004

(1) Divide R into n intervals of the same length l ( =  R / n ) such that the prices are divided into n classes [ LP min , LP min + l ), [ LP min + l , LP min +2 l ), …, [ LP min +( n –2) l , LP min +( n –1) l ), [ LP min +( n –1) l , LP min + nl ], where LP min denotes the lowest price of a product in the target category.

3.2.2 Linguistic product clusters.

https://doi.org/10.1371/journal.pone.0106946.g005

After the clustering analysis is complete, the intelligent product search system provides the user with linguistic product clusters and their centroids, thereby enabling the user to gain knowledge regarding the target product category and select product clusters of interest in a convenient manner. When the user selects a product cluster, the proposed system lists those products belonging to the cluster by sorting the products based on the Euclidean distance ( Dist ) between each product's fuzzy-semantic fitness vector and the centroid of the cluster because the characteristics of the centroid induce the user to select that product cluster. This procedure allows individual online shoppers with a rough budget to find appropriate products from a sorted list.

Experimental Results

4.1 application to a price comparison site.

To illustrate how the proposed system works, the procedure shown in Figure 3 was applied to one of the most popular PCSs in Korea. For this experiment, it was assumed that an online shopper was considering “ultra-thin laptop computers” as the target product category, and that the PCS they used gathered information on 95 products. Table 1 shows a sample of the collected products (to see the data used in this study, refer to Data S1 [37] ). Here, KRW indicates South Korea's currency, the Korean won (as of January 2014, 1,070 KRW is equivalent to $1 USD). As shown in the table, there are many products whose prices vary considerably.

https://doi.org/10.1371/journal.pone.0106946.t001

The PCS currently provides its users with a summarized list, as shown in Table 2 . The products on the list are initially sorted based on their popularity, but can also be sorted according to price, the number of sellers, the release date, or in alphabetical order of the product names based on the user's preference. Note that the table shows the lowest prices and cannot provide users with a deep insight into the products.

https://doi.org/10.1371/journal.pone.0106946.t002

If an individual user selects a specific product (e.g., product 1) from the list in Table 2 , then those sellers offering this product and their prices are also listed, as shown in Table 3 . In addition, clicking on the name of a seller allows the user to visit the seller's online shopping mall, where they can actually purchase the product. Conventional PCSs typically allow users to filter products according to their prices, and users naturally focus on those products that fall within a specific price range to reduce the amount of retrieved information. However, users may be overwhelmed by the massive amount of information shown in the lists in Tables 2 and 3 , and thus may have difficulty in making a purchasing decision through a conventional PCS.

https://doi.org/10.1371/journal.pone.0106946.t003

4.2 Linguistic price comparisons and personalization

Now, let us consider an individual user who wants to buy an ultra-thin laptop computer within the price range of 800,000 to 1,000,000 KRW. Although conventional PCSs generally provide a list of products with the lowest prices within the specified price range, several problems remain. For example, product 1 in Table 1 is excluded from the filtered product list, although its lowest price is very close to 1,000,000 KRW and may be attractive to the user. Similarly, product 10, whose highest price is very close to 800,000 KRW, is also excluded. Therefore, the user cannot identify these products through the filtered product list. In contrast, although products 5 and 7 are considered expensive because their highest prices far exceed 1,000,000 KRW, they are included in the filtered product list. These problems are addressed by employing the proposed system to provide the user with relevant information for a purchasing decision.

https://doi.org/10.1371/journal.pone.0106946.t004

https://doi.org/10.1371/journal.pone.0106946.t005

https://doi.org/10.1371/journal.pone.0106946.t006

The proposed system grouped products into seven clusters and assigned linguistic labels (from Low-end to High-end) to each cluster. In addition, the system sorted these clusters based on the labels. Therefore, a novice shopper can acquire knowledge quickly regarding the structure of product prices within the target category.

If an online shopper is interested in choosing a specific product cluster, they can select that cluster to see the products it contains. Because the shopper in the present example used a price range of 800,000 to 1,000,000 KRW for the product search, the shopper is interested in products labeled Low-middle. The shopper then selects cluster 2 in Table 6 , and is provided with a list of products belonging to that cluster, as shown in Table 7 . The products in cluster 2 are sorted in ascending order of Dist . The fuzzy-semantic fitness vectors of the products in the same cluster are similar to each other. Therefore, these products are expected to satisfy shoppers interested in products labeled Low-middle. Furthermore, product 10, which was excluded through conventional PCS filtering, is identified, as shown in Table 7 .

https://doi.org/10.1371/journal.pone.0106946.t007

If an online shopper wanting to find products labeled Middle-high selects cluster 6 in Table 6 , they are then provided with the list of products shown in Table 8 . Similarly, it can be seen that product 1, which was excluded by the conventional PCS, is included in the product list, although its priority is relatively low depending on the distance from the centroid.

https://doi.org/10.1371/journal.pone.0106946.t008

As demonstrated above, the intelligent product search system proposed in this study can provide online shoppers with a quick insight into the target product category and knowledge about the distribution of prices within that category, and help them identify the appropriate products. In addition, this system does not exclude products even if they are outside the user-defined price range. In this way, online shoppers can make better purchasing decisions even when they do not have sufficient prior knowledge regarding the target product categories.

4.3 Prototype of the intelligent product search system

A prototype system was developed using a common Web programming language, Java Server Page, and MS-SQL Server was used as the data repository. Moreover, the Google Charts library was deployed to provide the users with a visual aid.

For the price range specified by the user, the prototype system first generated a cluster summarization page. Figure 6 shows a summarization of the product clustering in the ultra-thin laptop computer category for a price range of 800,000 to 1,000,000 KRW. In the upper part of this page, the price dispersion of each product cluster was represented through a candlestick chart. The top and bottom of a body were determined based on the minimal and maximal values of the average product price. The end points of a vertical line were specified based on the minimal and maximal prices of the corresponding cluster. Moreover, the details of the price dispersion of a product cluster can be checked by clicking on the candlestick chart. The figure also shows the details of the Middle 3 cluster, for example.

https://doi.org/10.1371/journal.pone.0106946.g006

In the lower part of the cluster summarization page, the number of items and average popularity of each product cluster were visualized through a bar chart. Therefore, the users can conveniently obtain insight into the products included in this product category and narrow the scope of their search.

After choosing a product cluster to be further investigated, the user can move to a product list page by clicking on the product cluster name in the lower-left corner of the cluster summarization page. As shown in Tables 7 and 8 , the products included in the selected product cluster were first sorted based on the distance to the cluster centroid, and the users may sort them using other criteria such as the product name, lowest price, highest price, and number of sellers.

Figure 7 shows the product list page for the product cluster Middle 3 . The first product, “SENS NT-X280-PA55S,” can be labeled as Middle considering its price range. On the contrary, the price range of the 7th item, “NT-X430-PS35,” does not overlap with the user-specified price range. Indeed, it is reasonable for the product cluster to include this product because most of its prices are very close to 1,000,000 KRW; however, such products are not considered in a traditional PCS. In this way, the proposed intelligent product search system enables users to find attractive products in more convenient manner, and provides online merchants with the opportunity for potential sales.

https://doi.org/10.1371/journal.pone.0106946.g007

Because, in a conventional PCS, products with different price labels are listed on a single page, where hundreds of products are occasionally listed, interpreting such an enormous amount of information is very inefficient, and users with only a rough budget may be discouraged to make a purchase. Therefore, it can be seen that the semantic procedures, product clusters, and visual aids are very useful for providing users with proper guidance, and the proposed system will be very helpful in supporting novice shoppers.

Conclusions and Future Research

Information-intensive Web sites provide a wide access to a diverse range of information sources. However, the browsing behaviors of many users are not directed in that users do not focus on locating specific targets and often experience problems of information overload and uncertainty. In particular, novice users have a considerable difficulty in making decisions based on information provided by such Web sites. Therefore, providing online users with useful information is a major challenge facing future Web environments.

As an example of information-intensive Web sites that can accommodate the needs of online users, this study considered a conventional PCS that has faced a user's vague search objectives and the uncertainty surrounding online shopping environments. To address these problems, an intelligent product search system was developed to provide online shoppers with quick insight into a product category and help them identify appropriate products in a more convenient manner.

The proposed system extracted the linguistic semantics hidden in product price dispersion using fuzzy logic, and employed the k -means clustering algorithm to generate linguistic product clusters for personalized results. In this regard, the characteristics of well-organized linguistic product clusters were outlined and used for a clustering analysis.

Once the price range is specified, the proposed system first displays a summarization page of the product clusters. This page shows the price labels, price dispersions, numbers of included products, and average popularity of the product clusters, and the users can conveniently choose a cluster suitable for their needs. After a product cluster is selected, a product list page is generated by taking the user-specified price range and user preferences into account; in addition, visual aids also help the users understand the search results. Consequently, online shoppers can find suitable products in a more effective way.

Although the proposed system addresses important issues inherent to a conventional PCS, there are still several research topics to further investigate. First, the intelligent product search system is limited in that it only considers product prices and user-defined price ranges. In this regard, future research should consider other factors that can be useful for generating personalized product recommendations, such as the user's preferred vendors, the product specifications, and promotional campaigns. Moreover, the advantages of the proposed system should be empirically validated through future research, which applies the proposed system to a wide range of product categories.

Supporting Information

The information of “ultra-thin laptop computers” collected by a price comparison site.

https://doi.org/10.1371/journal.pone.0106946.s001

Acknowledgments

This work was supported by the Dong-A University research fund. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Contributions

Conceived and designed the experiments: JWK HSH. Performed the experiments: JWK HSH. Analyzed the data: JWK HSH. Contributed reagents/materials/analysis tools: JWK HSH. Wrote the paper: JWK HSH. Data gathering: JWK.

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Abdul Bashiru Jibril

Elsamari botha, christian nedu osakwe.

While online price comparison websites have burgeoned, there is scant understanding of how they influence online consumer behavior. This study addresses this gap in the literature by investigating the influence of price comparison websites on online switching behavior, and also suggests some additional factors that may be considered when looking at this relationship. We argue that shopper innovativeness, their perceived usefulness of the ad, and their customer service experience consciousness are important factors to consider when evaluating the impact of price comparison websites on eSwitching behavior. We also argue that the most appropriate theoretical lens through which to investigate this relationship is that of the consumer empowerment paradigm. A conceptual model is proposed and tested. Our analysis of 345 sample respondents finds that perceived usefulness of ads and customer service experience expectations are important enablers to price comparison websites use. Similarly, we find that shopper innovativeness and customer service expectations, in addition to price comparison websites use, are significant enablers to eSwitching behaviour. However, contrary to prediction, we find that shopper innovativeness has little to no influence on shoppers’ use of price comparison websites; we also observed similar patterns concerning the link between the perceived usefulness of online ads and eSwitching behaviour. In conclusion, our research contributes to better understanding the influence of price comparison websites on online switching behavior, and the factors that might influence this relationship.

Introduction

While online price comparison websites have burgeoned, there is scant understanding of how they influence online consumer (eSwitching) behavior. This study addresses this gap in the literature by investigating the influence of price comparison websites on online switching behavior. While Osakwe and Chovancová ( 2015 ) examined female shoppers’ perceptions towards the use of price comparison websites, Jung et al. ( 2014 ) examined work shoppers’ response to price comparison websites, and Pourabedin et al. ( 2016 ) investigated the role of customer value and attitudes in online channel switching behavior, there are no studies to the authors’ knowledge that investigates the relationship between eSwitching behavior and price comparison websites. The emerging question, therefore, is whether the use of price comparison websites can lead to eSwitching behavior. This study by addressing this question seeks to close the void in our understanding concerning the relationship between price comparison websites and eSwitching behavior. This study also suggests some additional factors that may be considered when looking at this relationship.

Accordingly, and based on a survey of existing research (Broniarczyk and Griffin 2014 ; Hudders et al. 2019 ; Osakwe and Chovancova 2015 ; Xu et al. 2013 ; Zhang et al. 2012 ), it is argued that shoppers’ innovativeness, their perceived usefulness of online ads, and their customer service experience consciousness are important factors to consider when evaluating the impact of price comparison websites on eSwitching behavior and this forms the overarching objective of our paper. We also argue that the most appropriate theoretical lens through which to investigate this relationship is that of the consumer empowerment paradigm (e.g. Broniarczyk and Griffin 2014 ; Camacho et al. 2014 ; Kucuk 2009 ). In other words, by employing the consumer empowerment paradigm, this paper’s objective is to investigate the relationship between (the use of) price comparison websites and eSwitching behavior in addition to the investigation of the determinants behind these phenomena, which we have identified above.

Altogether, this allows us to contribute to the literature on online shopping in at least two important ways. First, is that we contribute to the online shopping literature particularly concerning the presentation of evidence that the use of price comparison websites, especially with regard to young and existing online shoppers, significantly empowers shoppers to engage in eSwitching behavior. The second contribution pertains to the investigation of the determinants behind the perceived use of price comparison websites in addition to the investigation of the empirical question on the determinants of eSwitching behavior. In particular, we find that shoppers’ traits primarily customer experience consciousness and innovativeness are enablers to eSwitching behavior, while positive perceptions towards the use of online ads as well as customer experience consciousness engender the use of price comparison websites.

Because it is commonly known that young adults, especially student population, are usually heavy users of online services and in general more receptive to emerging technologies (see also Yoo and Donthu 2001 ), we chose to test the study hypotheses using a (university) student sample; which is also consistent with studies on consumer switching in the technology context (e.g. Bhattacherjee et al. 2012 ). At the same time, it is worth noting that our sample is primarily made up of experienced internet users and who are familiar with online shopping. Taken together, this empirical context is appropriate for our investigation.

The following literature review addresses each of these constructs in turn, before suggesting the theoretical model that was tested. This is followed by a brief overview of the methodology, and findings of an exploratory study to test the model. We conclude with a short discussion of these findings.

Literature Informing Hypotheses Development

Consumer empowerment paradigm.

The consumer empowerment paradigm in marketing is an important extension of the psychological empowerment construct, long studied, in the psychology literature (cf. Zimmerman 1995 ; Cattaneo and Chapman 2010 ). Camacho et al. ( 2014 :294), while citing previous research, describe empowerment as “strategies or mechanisms that equip people with sufficient knowledge and autonomy to allow them to exert control over a certain decision”. Similarly, it has been noted in the marketing literature that “empowerment requires mechanisms for individuals to gain control over issues that concern them, including opportunities to develop and practice skills necessary to exert control over their decision making” (Pires et al. 2006 , p. 938). In light of previous discussions and among them Broniarczyk and Griffin ( 2014 ), Kucuk ( 2009 ), and Pires et al. ( 2006 ), the current investigation argues that this paradigm can help us gain understanding into the perceived use of price comparison websites and eSwitching behaviour in particular among existing shoppers. In general, the internet equips users with the tool to gain access to credible and quality information and this by implication confers more power and freedom of choice to users and in this case online shoppers. Moreover, since it is known that customers often rely on expert advice (in this instance, price comparison websites) when making purchase decisions (Camacho et al. 2014 ), such advice - well-intentioned – can dramatically reduce switching cost. One of the benefits of comparison tools and in this case price comparison websites is that they offer shoppers a range of choices (Broniarczyk and Griffin 2014 ), thus allowing shoppers to shop across stores. Online switching behaviour (or eSwitching) is considered to be a possible outcome of this process. Similarly, since it is has been suggested in the literature that digital ads may be an important source of consumer empowerment today (Hudders et al. 2019 ), it is considered therefore to play an influential role in shoppers’ behavioural tendencies to use price comparison websites and to consequently eSwitch. Consequently, the following hypothesis is proposed:

• H1: Price comparison websites’ perceptions positively influence eSwitching behaviour.

At the same time, it is known that intrapersonal factors, described as “how people think about themselves and includes domain-specific perceived control and self-efficacy, motivation to control, perceived competence, and mastery” (Zimmerman 1995 , p. 588), is a key aspect of empowerment. Accordingly, we consider intrinsic factors, or more precisely traits, like the customer service experience and consumer/shopper innovativeness, to be strong propelling force for price comparison websites use and eSwitching behaviour. Before discussing each of these in turn, we turn to eSwitching behaviour.

eSwitching Behaviour

The reasons why customers engage in switching intentions and/or behaviour remains an important topic in the literature to this day (Bhattacherjee et al. 2012 ; Fan and Suh 2014 ; Gopta et al. 2004 ; Malhotra and Malhotra 2013 ; Mosavi et al. 2018 ; Pourabedin et al. 2016 ). Multiple reasons exist for this kind of behaviour (Chuang and Tai 2016 ; Keaveney 1995 ; Malhotra and Malhotra 2013 ; Mosavi et al. 2018 ; Zhang et al. 2012 ) but a factor often identified as a key determinant is the attractiveness of alternatives (Chuang and Tai 2016 ; Xu et al. 2013 ; Zhang et al. 2012 ). In the context of this study, consumers might easily switch between online stores when it is believed they can get better price deals elsewhere. Price comparison websites lower the barriers to switching by offering such alternatives in one central place, with links that could easily navigate to said alternatives. Because price comparison websites are price aggregators, it offers the opportunity for shoppers to easily know the prices of each competing online stores and this consequently reduces switching cost. This study hypothesized (see H1) that these price comparison websites directly influence eSwitching behavior. The following section describes the former.

Price Comparison Websites

Comparison shopping can be defined as “the practice of comparing the prices of items from different sources to find the best deal” (Hajaj et al. 2015 : 563). Price comparison websites provide the online alternative to this and we, therefore, define online price comparisons as the online tool that allows for the comparison of item prices from different sources to find the best deal. The use of price comparison websites has been acknowledged as an important search information tool, which has strong potential to alter shopping behaviour both in the online and offline environment (Bodur et al. 2015 ; Broeckelmann and Groeppel-Klein 2008 , Osakwe and Chovancová 2015 ; Passyn et al. 2013 ). Osakwe and Chovancová ( 2015 :597) describe price comparison websites as “a near-frictionless marketing intermediary that can drive down online shoppers’ search costs”.

Because shoppers can easily compare prices of similar firms and/or brands in a matter of seconds, the active use of price comparison websites not only reduces search costs but also empowers shoppers to buy from firms offering the best deal. This study argues that the greater the perceived usefulness of price comparison websites, the more it is expected that shoppers will use the information available on these sites in their pre- and post-purchase decisions. It, therefore, acts as a key mediator in the relationship between key influencing factors and online switching behavior. From a consumer empowerment perspective, it is pertinent to consider how these websites influence the relationship between eSwitching and shopper innovativeness, perceived usefulness of an ad and the consumers’ service experience consciousness. Each of these now discussed in turn.

Shopper Innovativeness

Consumer/shopper innovativeness, or what some scholars may well refer to as variety seeking propensity, is a well-researched concept in the literature (e.g. Agarwal and Prasad 1998 ; Bhattacherjee et al. 2012 ; Xu et al. 2013 ; Mishra 2015 ). In this instance and following prior literature (Agarwal and Prasad 1998 ; Bhattacherjee et al. 2012 ), we define shopper innovativeness as a trait reflecting the willingness on the part of the shopper to experiment and/or try out any new products or range of services . Moreover, because consumer innovativeness has been implied in the internet browser context to positively relate to switching intentions among a similar sample of respondents like this study (Bhattacherjee et al. 2012 ), it stands to reason, that both the relationship between shopper innovativeness and eSwitching behavior, and its relationship to price comparison website perceptions begs investigation. Hence, we proposed that:

• H2: Shopper innovativeness positively influences eSwitching behavior.
• H3: Shopper innovativeness is positively related to price comparison website perceptions.

Customer Service Experience Consciousness

The role of customer service experience is well researched in academic and business literature (Berry et al. 2002 ; Khan et al. 2015 ; Meyer and Schwager 2007 ; Osakwe and Chovancová 2015 ). However, its relationship to online switching behavior and price comparison websites remains underdeveloped in empirical research. Service experience consciousness reflects a trait among shoppers who are highly demanding in service encounters, and thus tend to exhibit a higher dissatisfaction threshold. This trait, for example, has been reported to be an important enabler to customer-perceived use of price comparison websites (Osakwe and Chovancová 2015 ), and is therefore included in the proposed conceptual model:

• H4: Customer service experience consciousness is positively related to price comparison website perceptions.

We extend this research by arguing that since this set of shoppers invests a significant amount of cognitive, emotional and even intellectual resources into service interactions/encounters with the firm, they are therefore more demanding, difficult to please and more likely to move from one store to another in search for better services at all times. Customer service experience consciousness has already been identified as a trait reflecting the likelihood of a customer to become highly dissatisfied with service encounters and since customer dissatisfaction has been linked to switching intentions in prior research (e.g. Fan and Suh 2014 ), it can be posited therefore that customer service experience consciousness and eSwitching behavior are related. Moreover, following previous research (Keaveney 1995 ; Liang et al. 2013 ), we argue that service inconvenience, impolite behavior on the part of service personnel, and occurrence of core service and service encounter failures will be less tolerated by those scoring high in customer service experience consciousness and thus in this case impacting eSwitching behavior. Consequently, the following hypothesis has been developed:

• H5: Customer service experience consciousness is positively related to eSwitching behavior.

The final construct, from an empowered customer perspective, we felt pertinent to include in the study is that of the perceived usefulness of online ads.

Perceived Usefulness of Online Ads

Consumers’ attitudinal response to advertisements in general and online ads, in particular, is mixed (Le and Vo 2017 ; Shavitt et al. 1998 ; Schlosser et al. 1999 ), yet it is considered an important contributing factor when investigating online consumer behavior (Ducoffe and Carlo 2000 ; Mehta 2000 ; Osakwe and Chovanocva 2015 ; Paliwoda et al. 2007 ). Some consumers have ill-feelings about online ads, others may be indifferent to online ads, while some have positive perceptions about online ads. In theory, however, ads are often meant to inform consumers and offer choices which they can easily choose from. Online ads, therefore, empower consumers concerning his/her buying decisions (cf. Ducoffe and Curlo 2000 ). Therefore, in this instance, online ads can confer significant power to consumers (Hudders et al. 2019 ), especially when it is perceived to be informative and valuable. In an online services context, strong perceptions towards online ads provide fertile ground for shoppers to become increasingly price-sensitive (Osakwe and Chovancová 2015 ). This may be particularly true with regard to the use of price comparison websites. This study, therefore, extends this line of the suggestion by including eSwitching behaviour as an alternative outcome of online ads’ perceived usefulness. Consequently, the following hypotheses were developed:

• H6: Customers’ perceived usefulness of ads positively influences their perception of price comparison websites.
• H7: Customers’ perceived usefulness of ads positively influences their eSwitching behavior.

The above literature review and proposed hypotheses can be summarized in the theoretical model proposed in Fig.  1 . This model extends prior research (e.g. Osakwe and Chovancová 2015 ) by our assessment of enablers to the perceived use of price comparison websites and eSwitching behavior, especially concerning young and existing online shoppers.

Empirical model

The methodology used to test the proposed model is outlined in the following section.

Empirical Study

Survey data and method.

More specifically, student sample was used in the study because this is an important consumer segment for studying online behaviour and has also been extensively employed in the literature (Fan and Suh 2014 ; Hong 2015 ; Ozok and Wei 2010 ; Wu et al. 2011 ). This study recruited participants from one of the state universities in the Czech Republic using a convenience-based sampling approach which we consider to be most practical in this case. This study uses both online and self-administered surveys, nevertheless, most of the completed responses were from the self-administered questionnaire. Because we wanted to ensure that those who participated in the study have fairly good internet experience with online purchases, in the end – particularly after deleting responses from six non-online shoppers - we had in total 345 valid responses. Therefore, the empirical focus is on existing online shoppers.

The majority of sample respondents were female (59%), aged between 17–24 (80%), and undergraduates (66%). In this study, statistical analyses were performed using both IBM SPSS and WarpPLS (Kock 2017 ). Finally, the research constructs - except for demographics - were measured using a five-point scale (ranging from completely disagree to completely agree).

Construct Measurement Validation

In order to improve the face and construct validity of the research constructs, constructs were adapted constructs from the literature. In particular, the measures for customer service experience consciousness, perceptions regarding the use of online ads and price comparison websites were based on Osakwe and Chovancová ( 2015 ), while the measure for consumer innovativeness was based on Daghfous et al. ( 1999 ) and finally the measure for eSwitching was modified from Kim et al. ( 2006 ) in addition to reading from the broader literature.

The research hypotheses were tested by using the PLS-path modeling technique and precisely using mode A algorithm. The inspected composite reliability scores were as follows: 0.85 (online ads perceptions/OAD), 0.84 (price comparison websites use/PCWs), 0.80 (shopper innovativeness/INNOV), 0.71 (customer service experience consciousness/CSEC), and 0.74 (eSwitching behavior/eSWITCH). At the same time, all indicator loadings and weights were statistically significant at p  < 0.01, besides all the indicator loadings but two exceeded the 0.6 scores required for this kind of exploratory work. In terms of convergent validity, average variance extracted (AVE) scores range from 0.59 (OAD), 0.58 (PCW), 0.58 (INNOV), 0.46(CSEC), to 0.49 (eSWITCH). Although not reported here, following Fornell and Larcker ( 1981 ) discriminant validity was established for the constructs.

Structural Model

Model fit and quality criteria were inspected based on SRMR and R-squared contribution ratio (RSCR). We obtained 0.09 (SRMR value and thus acceptable since it is less than 0.1) and RSCR scores of 0.99 (which approximates to the ideal cut-off value of 1) (Kock 2017 ).

Regarding the hypothesized relationships, there is evidence for all but two (see Table  1 for details). Notably, the control variable i.e. gender neither statistically impacted price comparison websites use nor eSwitching. Finally, model predictive power concerning shoppers’ use of PCW was 14%, whereas for eSwitching it was 30%; meaning that the empirical model explains about 14% and 30% variations in the use of price comparison websites and eSwitching respectively.

Table 1.

Structural Model statistics

Short Discussion and Conclusion

This study has been able to identify antecedent factors leading to switching behaviour in the context of service and in particular in online stores beyond the usual suspects in the literature, for instance, attitudes towards switching (cf. Pourabedin et al. 2016 ). Through the consumer empowerment paradigm, we find, not surprisingly, the perceived use of price comparison websites relates strongly with eSwitching. This novel finding in some ways mirrors the conclusion in past research about the role that search-intentions play in customers’ channel switching (Gopta et al. 2004 ). The point is that shoppers who use price comparison websites mainly use it for bargain hunting. This suggests that to reduce this positive effect on online switching behaviour, online retail merchants, particularly with a focus on young shoppers, will need to do more in the area of sales promotion and loyalty coupons as this might be one of the most effective ways to reduce the incidence of eSwitching and even customer churn.

Also, we find that a higher possession of the following traits in shoppers namely innovativeness and their perception of the customer service experience empowers these shoppers to engage substantively in online switching behavior. Shopper innovativeness had the greatest impact on eSwitching. Since it has been suggested that individuals who are more likely to experiment with new ideas and/or products are more prone to switching (Bhattacherjee et al. 2012 ; Xu et al. 2013 ) and perceived innovativeness of the services provider inhibits customer switching intentions (Malhotra and Malhotra 2013 ), the study’s finding, therefore, is a reinforcement to extant research.

Because shoppers who possess a higher level of service experience conscientiousness than others may be more prone to service dissatisfaction, the finding, therefore, mirrors previous discussions about the role of customer dissatisfaction in customer switching behaviour (cf. Chuang and Tai 2016 ; Fan and Suh 2014 ). Moreover, as predicted and consistent with previous research (Osakwe and Chovancová 2015 ), this study finds that positive perceptions concerning online ad usefulness, in addition to service expectations, increasingly empower shoppers to use price comparison websites.

Although this study initially proposed that consumer innovativeness and perceived use of price comparison websites are strongly related, evidence, however, shows it to be marginal, at best. Therefore, further research is needed to explore not only this insignificant finding but even further the supported research evidence reported in this paper. In other words, there is a need for more analysis on the research issues discussed in this work because until they are reassessed our findings are at best preliminary and limited to a specific sample. Furthermore, since the strength of relationships was never hypothesized, it is important therefore for future analysis to validate the assumption that shopper innovativeness, compared to others, has the strongest impact on online switching behaviour. Meanwhile, an important limitation of this analysis is that it was conducted using a student population and so makes it difficult to generalize beyond the target population. This consequently reinforces our call for further research on this topic. Finally, this study despite its limitations has added to the customer switching behaviour literature in addition to the heavily under-researched research stream of price comparison websites through the demonstration of the antecedents for eSwitching and shoppers use of price comparison websites based on the consumer empowerment paradigm.

Acknowledgement

This work was supported by the Internal Grant Agency of FaME through TBU in Zlín No. IGA/FaME/2020/002; and further by the financial support of research project NPU I no. MSMT-7778/2020 RVO - Digital Transformation and its Impact on Customer Behaviour and Business Processes in Traditional and Online markets.

Contributor Information

Marié Hattingh, Email: [email protected] .

Machdel Matthee, Email: [email protected] .

Hanlie Smuts, Email: [email protected] .

Ilias Pappas, Email: [email protected] .

Yogesh K. Dwivedi, Email: moc.liamg@ideviwdky .

Matti Mäntymäki, Email: [email protected] .

Michael Adu Kwarteng, Email: zc.btu@gnetrawK .

Abdul Bashiru Jibril, Email: zc.btu@lirbiJ .

Elsamari Botha, Email: az.ca.bsu@cme .

Christian Nedu Osakwe, Email: az.ca.nus@osirhc .

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The role and impact of comparison websites on the consumer search process in the US and German airline markets

• Original Research
• Open access
• Published: 04 January 2016
• Volume 16 , pages 127–148, ( 2016 )

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• Christopher P. Holland 1 , 2 ,
• Julia A. Jacobs 2 &
• Stefan Klein 2  

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The paper examines how consumers search for airline tickets based on a comparative analysis of the US and German markets. Data from comScore is analysed using an innovative application of set theory. ComScore is a leading commercial provider of business intelligence and consumer analytics based on its worldwide panel of two million online users. The search process is modelled using the concept of the consideration set based on primary search with the airline websites and secondly by the use of online travel agents and meta-search engines, which are termed comparison websites. Three generic search models are proposed: (1) primary search with airline websites only; (2) search of comparison websites only; (3) a combination of primary search and comparison websites. Each generic search model accounts for a significant proportion of overall users in both markets. The consideration sets are 2.58 in Germany and 2.74 in the United States. It is shown that the use of comparison websites significantly increases the propensity to conduct additional primary search based on analysis of all major airline pairs in both markets. The theoretical and managerial implications of the results are described and future research opportunities are outlined.

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1 Introduction

Online travel agents (OTAs) are powerful companies in the airline and travel markets where the market leaders Expedia and Priceline account for gross bookings of $50.4 billion and $50.3 billion respectively (Expedia 2015 ; Priceline 2015 ). These companies play a crucial role in the online search process for all forms of travel planning (Xiang et al. 2015 ). One of the key features of an OTA in the airline market is a compilation and comparison of competing offers that meet the consumer’s requirements in terms of their choice criteria such as origin–destination and date of travel, and a comparison of available offers. OTAs therefore facilitate the consumer search process by offering a fast and efficient method for consumers to search and evaluate a range of competing offers, that is, they support multi-criteria decision making and provide market transparency (Buhalis and Licata 2002 ). OTAs are therefore economically and strategically prominent intermediaries in the global travel market.

In this paper, the term online travel agent, e.g. Expedia, refers to a website that offers search across airlines, price comparison and booking functionality. In addition, there are meta-search engines such as Kayak, which also offer price comparison but without booking capability. The focus of this paper is on the influence of comparison functionality, whether this is done through an OTA or a meta-search engine, on direct search with airline websites. We therefore use the term comparison website to include both OTAs and meta-search engines. We also distinguish between OTAs and meta-search engines for specific examples.

The travel industry has been at the forefront of online search and booking, and has an established and well-documented history of technology innovation and disruption of distribution systems going back to Computerized Reservation Systems (CRSs) (Inkpen 1998 ). The airlines have also invested heavily into online marketing and distribution so that consumers can search and book flights online, and also buy related travel services such as hotels, car hire and holiday packages, directly from an individual airline. Given numerous constraints of fleet management in a complex network, an airline’s seat capacity on any single connection is fixed in the short term. Airlines respond with dynamic pricing and extensive yield management in order to maximize revenues.

An airline ticket constitutes a personalized contract specifying the carrier (i.e. airline offering the service), an origin–destination pair, a time and date of travel, a service bundle (e.g. piece and weight of luggage, seat selection, food and entertainment) and price. These characteristics are important because they make it possible to compare offers from competing airlines based on a small set of key criteria. From a consumer marketing perspective, online research is a crucial part of the customer journey and it is reasonable to assume that consumers would tend to conduct extensive search processes in order to find suitable flights, and to minimize the price.

The focus of this research is to empirically study the search process using an innovative application of set theory, which allows us to distinguish between: (1) direct search on the airline websites; (2) use of comparison websites; and (3) analyse the interaction effects between (1) and (2). The US and German markets were chosen because of their size and sophistication. The US is the largest airline market worldwide and has a highly developed online market. Germany is the 5th largest airline market in the world and is the largest online market in Europe (Pearce 2014 ). These two markets are therefore very good indicators of advanced online search behaviour in airline markets.

The structure of the paper is as follows. A literature review of the search process is presented that is organised around the themes of the consideration set and previous research into OTAs and meta-search engines. A research framework is proposed that integrates direct search with the use of comparison websites. An explanation of the innovative methodology that uses set theory to analyse online panel data is given and the results for the US and German markets are presented. A discussion of the results and limitations of the study are given, and the managerial implications of the results are described. Finally the conclusions and the theoretical contribution of the study are presented.

2 Literature review

Our literature review covers (a) the construct of the ‘consideration set’ and (b) studies of comparison websites.

2.1 The consideration set

Even though the consideration set is an important concept in marketing (Brown and Wildt 1992 ), it has received relatively limited empirical attention during the pre-Internet era. Hauser and Wernerfelt ( 1990 ) conducted an authoritative survey and found only ten papers that covered 23 product categories in total. The consideration set is an established and widely used marketing concept that has been documented since the 1960s (Howard 1963 , 1977 ; Howard and Sheth 1969 ). Howard ( 1963 ) and Howard and Sheth ( 1969 ) referred to the set of brands considered when contemplating a purchase within a particular product class, the number of brands a buyer considers when contemplating a purchase and the notion of acceptable brands considered in a purchasing decision. Howard ( 1977 ) formally introduced the term ‘evoked set’, and defined it as:

the subset of brands that a consumer would consider buying out of the set of brands in the product class of which he or she is aware (Howard 1977 , p. 32).

Roberts ( 1989 ) refers to Howard’s ( 1963 ) definition. Hauser et al. ( 1983 ) used the term ‘consideration set’ in place of evoked set, though the definitions are equivalent. Hauser and Wernerfelt ( 1990 , p. 393) gave a precise definition of the consideration set:

The theoretical construct of a consideration set is those brands that the consumer considers seriously when making a purchase and/or consumption decision.

Although there are differences in emphasis, the concept of the consideration set and the earlier equivalent term of ‘evoked set’, has remained broadly the same over the past 50 years. It is the group of brands that a consumer actively considers when making a purchasing decision. The pre-requisites of being included in the consideration set are that a consumer must be aware of the brand and also judge it to be acceptable, at least for consideration and evaluation.

The formation and evaluation of the consideration set are clearly important features in the customer journey. At the start of the customer journey there is the universal set of brands within a particular market. For an individual consumer, this is immediately reduced to the awareness set, from which the consumer selects a set of brands, which she regards as worthy of active consideration (Shocker et al. 1991 ). The shape of the journey measured by the number of brands has been conceptualized as a funnel that starts with the widest possible set of options and ends in an individual making a purchase. The choice of a particular brand from the consideration set involves consumer search and evaluation, including the acquisition and evaluation of information from multiple sources. The customer journey metaphor in which the universal set is reduced to a single purchased brand is an apt metaphor, and the concept of a sales funnel captures to an extent, the shape of the journey measured by the inclusion and exclusion of competing brands. We consider competing brands to be provider brands, i.e. airline brands in the airtravel market and not meta-search or OTA brands. In this way, the intermediaries are not used for the calculation of the consideration set.

There are very few empirical studies that apply the consideration set in an online context and measure the nature and extent of the online search process to evaluate competing brands. Johnson et al. ( 2004 ) measured the consideration set size for a range of consumer markets in the US and reported very narrow search results of 1.2 (books), 1.3 (CDs) and 1.8 (air travel sites), meaning that consumer visited on average 1.2, 1.3 and 1.8 websites respectively. Their sample of air travel websites consisted of a combination of both OTAs (Expedia, Travelocity) and also airline websites. Zhang et al. ( 2006 ) repeated Johnson´s work a couple of years later and found similar results: 2.1 (CDs), 3.3 (airline tickets), 3.3 (computer hardware). A McKinsey study on the financial services sector in Germany reported a higher consideration set of 3.8 (Meyer and Stobbe 2010 ). Holland and Mandry ( 2013 ) conducted an international, cross-sector analysis of banking, grocery, airlines, telecommunications, insurance and automotive. They reported consideration set sizes ranging from 2.40 to 2.77 in the UK and 2.13–2.60 in the United States. The method used in all of these studies is to measure the consideration set based on the number of brands included in the search process, indicated by the range of different websites visited. In the airline market this is the number of airline websites visited.

In summary, the few empirical results show that the average online consideration set size is relatively small. This is contrary to economic and marketing theories that predict extensive search patterns based on very low search costs using the Internet (Stigler 1961 ; Jepsen 2007 ; Bucklin and Sismeiro 2009 ). This raises an important question. Why is the average online consideration set relatively small, i.e. why do consumers not engage in more extensive search-patterns? One possible explanation is that consumers use OTAs and meta-search engines to assess the market. It is therefore necessary to consider search behaviour that includes comparison websites (whether this is on an OTA or a meta-search engine) in addition to direct search with airline websites.

2.2 Comparison websites

The Internet and online intermediaries improve access to information and dramatically lower search costs (Laffey and Gandy 2009 ; Dickinger and Stangl 2012 ; Lee et al. 2007 ). However, there remains significant price and product dispersion in the travel market, which indicates that an intensive search process is worthwhile (Baye et al. 2003 ). Table  1 shows an overview of the key literature on comparison web sites.

In the airline industry, Collins et al. ( 2010 ) demonstrate significant heterogeneity of search preferences, which implies that the effects of the Internet and comparison websites on search behaviour will be uneven (Dickinger and Stangl 2012 ). In the hotel market, Anderson ( 2011 ) used panel data to explore the interaction between a hotel price comparison website and direct research with individual hotels, and found that 75 % of travellers used the comparison engine in combination with direct search. As there appears to be no research that models the interaction between direct search and the use of comparison websites using the online consideration set concept, we are suggesting a research design for exploring this problem, which is elaborated in the next section.

3 Research framework and hypotheses

Based on empirical search patterns, three generic search patterns that are shown diagrammatically in Fig.  1 , define the online customer search process and which we are using hereafter. Model 1 and 2 have been constructed based on a simple logical search process of consumers. The interconnection of the market players can be seen in model 3 and has been similarly shown by other authors (Werthner and Klein 1999 ; Xiang and Gretzel 2010 ).

Generic online search models for airline flights

In model 1, consumers search airline websites only. In model 2, consumers only investigate comparison websites. In model 3, consumers combine search of comparison websites and airline websites. The consideration set concept is only applied to the primary search with airline websites, whether this is done on its own as in model 1, or in conjunction with comparison websites as shown in model 3. This is based on the definition of the consideration set (Hauser and Wernerfelt 1990 ), which means the number of airline brands considered. This definition excludes comparison websites because an OTA or meta-search engine is a source of information regarding airline travel and is not an airline brand.

Hypothesis 1:

The online consideration set based on primary search with airline websites only will be in the region of 2.5–3.0 based on the earlier results from Johnson et al. ( 2004 ), Zhang et al. ( 2006 ), Holland and Mandry ( 2013 ), and Holland and Jacobs ( 2015 ).

This range is relatively small compared to the pre-Internet results from Hauser and Wernerfelt ( 1990 ), which reported an average of 3.98 from the assessor database research project and 4.05 based on nine separate studies. The extent of the search process is an indication of the level of competitive intensity within a market. The implicit theoretical assumption of the electronic markets hypothesis is that the Internet increases the breadth of search (Bakos 1998 ) whereas the few empirical studies that have attempted to measure the online consideration set in a systematic manner have reported significantly lower results than might reasonably be expected. The range of 2.5–3.0 is given based on the bounds of previous online studies, and is substantially lower than the results reported by Hauser and Wernerfelt ( 1990 ).

Hypothesis 2:

The majority of consumers will use comparison websites, i.e. an OTA or a meta-search engine, as part of their search process. This includes both Model 2 and Model 3 search behaviour.

The US and Germany are both highly advanced online markets and therefore one would expect sophisticated search behaviour. The logic is that comparison websites help users achieve an extensive search process in an efficient manner. The other evidence to support this hypothesis is the economic size of the leading OTAs such as Expedia and Priceline, and their very high numbers of online users (Xiang et al. 2015 ).

Hypothesis 3:

The use of comparison websites is a substitute for extensive direct research with airline websites. It is therefore expected that these users will have a lower propensity to conduct further search with a second airline website compared with users that do not use conduct comparisons through an OTA or meta-search engine.

The theoretical logic is that consumers will continue to search until the cost of additional search outweighs the expected benefits (Stigler 1961 ). It is therefore surprising that most consumers do not conduct an extensive search process of the airline websites. A plausible explanation for this behaviour is that consumers are using OTAs and meta-search engines to give them coverage of the market instead of conducting extensive direct search with different individual airline websites. There are no previous empirical research results that specifically address the interaction between comparison websites and direct search and this is therefore an important question to address.

4 Methodology

4.1 clickstream data.

Online panel data uses clickstream data from a large panel of online users that is generated as they surf across different websites (Bucklin and Sismeiro 2009 ). ComScore is a world leader in online digital analytics and their research data has been used in previous research (Lohse et al. 2000 ; Johnson et al. 2004 ; Zhang et al. 2006 ). It has also been used to analyse online consideration sets (Holland and Mandry 2013 ; Holland and Jacobs 2015 ). ComScore is a powerful source of information because it provides massive scale, international scope, very detailed granularity and the ability to measure surfing patterns of very large groups of individuals across multiple websites. Its worldwide online panel is approximately two million users. See comScore ( 2014 ) for a commercial overview of their business model.

4.2 Measurement of the online consideration set

The consideration set is defined as the number of airline website brands visited. Note that it is not possible to track search behaviour within an individual website using standard panel data reports. There is therefore a trade-off between more extensive information about search behaviour across multiple websites by using online panel data and more detail about specific search paths within websites based on web server data. In the context of research into the search process during a customer journey it is clearly much more useful to use data that covers the journey rather than have very detailed information about a single stage of the journey. Two limitations follow from this approach: (1) it is not possible to analyse the detailed search within an individual airline website to see which and how many flights (including code-sharing offers for airline alliances) are evaluated; (2) we do not know what happens within a comparison website, in particular the number of brands considered. The comparison websites are therefore modelled to focus on the overall search process, the interaction between airline websites and comparison websites, and the consideration set of airline brands.

The audience duplication report gives the total number of visitors to a set of airlines, the number that visit one airline only and the number that visit two or more airlines within a month, i.e. the report gives us insights into search for multiple airline brands. An assumption is made that customers visiting one website only are more likely to be conducting some form of e-service rather than actively searching for a flight and are excluded from the online consideration set calculation. The authors are aware that there may be a number of false negatives: e.g. travellers searching for flights just on the Lufthansa website, which includes Lufthansa’s code share partners, but do not have a way of identifying them at this time. Those customers that visit two or more websites are regarded as active searchers. This approach to measuring the online consideration set is consistent with the marketing definition and use of the term consideration set (Brown and Wildt 1992 ). The online consideration set is calculated by: (a) summing the number of different airline websites visited by all searchers; and (b) dividing by the number of searchers (Zhang et al. 2006 ).

4.3 Distribution of users across the generic online search models 1, 2 and 3

Audience duplication reports of the airlines and the comparison websites were used to calculate the distribution of users across the search models as shown in Fig.  2 . α refers to the set of all major airlines in the market, {airline 1, airline 2, airline 3…airline n}. β refers to the set of all of the major comparison websites, {agent 1, agent 2, agent 3…agent n}. α, β and (α ∪ β) are calculated based on the comScore audience duplication reports for (i) all airlines, (ii) all comparison websites, and (iii) all airlines and comparison websites.

The calculation of visitors to each of the generic search models

4.4 Calculating the probability of additional search

Three websites are shown in Fig.  3 : Airlines 1 and 2 and an online travel agent. Sets A to G can be calculated directly from the intersections of the two airlines and the travel agent website. X ∩ Y ∩ Z is given empirically in the audience duplication report for {X, Y, Z}. Similarly, (X ∩ Y), (X ∩ Z) and (Y ∩ Z) are given empirically in the three separate audience duplication reports for {X, Y}, {X, Z} and {Y, Z}.

The visiting patterns to two airlines and an online travel agent

The customers of Airline 1 (X) are divided into two groups: those that don’t use the online travel agent website (Group 1), and those that use the online travel agent website (Group 2). Based on the panel data, the probability of the customers in each group of visiting the second airline can then be calculated as follows. The probability of additional search for a sample of airline pairs and a comparison website are calculated for a sample within each market in the form of a natural experiment (Campbell and Stanley 1963 ; Neslin and Shoemaker 1983 ).

4.4.1 Airline 1 Customers

Group 1 Customers of Airline 1 that don’t use the online travel agent website is given by (B ∪ F). The probability of Group 1 visiting Airline 2 =  \(\frac{B}{B \cup F}\) .

Group 2 Customers of Airline 1 that use the online travel agent website is given by (A ∪ C). The probability of Group 2 visiting Airline 2 =  \(\frac{A}{A \cup C}\) .

4.4.2 Airline 2 Customers

Group 1 Customers of Airline 2 that don’t use the online travel agent website is given by (B ∪ G). The probability of Group 1 visiting Airline 1 =  \(\frac{B}{B \cup G}\) .

Group 2 Customers of Airline 2 that use the online travel agent website is given by (A ∪ D). The probability of Group 2 visiting Airline 1 =  \(\frac{A}{A \cup D}\) .

5 Analysis and results

All figures for the German and US market are retrieved from comScore reports for the time period of 1 month, in this case May 2014. Total airline visitors is the number of consumers that look at any airline website. If an individual visits two or more airline websites, they are only counted once. This is the number of unduplicated, unique visitors in the sample. The total number is divided into two categories, those that look at one website only (e-service), and those that look at two or more websites (searchers).

5.1 Hypothesis 1

The unique visitor results for Germany and the US are shown in Tables  2 , 3 , 4 and 5 .

It can be seen in Tables  2 and 4 that the individual airlines attract large numbers of online users. The first stage of the analysis is to measure the breadth (extent) of the primary research. That is, the online consideration set is based on direct visits to individual airline websites only, regardless of whether or not users visit a comparison website. The results for both markets are shown in Table  6 .

The total airline website visitors is the number of consumers looking at any of the airline websites. Note that this is smaller than the sum of the unique visitors to each airline shown in Table  2 (6048) because some visitors go to more than one airline website. The figure of 6048 is the total number of visits made to different websites by all online users. The total airline visitors of 4240 are divided into two groups: e-service and search.

The number of airline websites visited by searchers is calculated by subtracting the e-service consumers from the total number of visits made by all online users, i.e. 6048 − 3092 = 2956 because by definition e-service customers only visit one airline brand. In Germany, the total number of websites visited by the searchers (2956) is divided by the number of searchers (1148), which equals a consideration set of 2.58.

The online consideration sets for Germany and the US are similar and fall within the range of 2.5–3.0. Hypothesis 1 is therefore accepted . This means that consumers in both markets look at just 2 or 3 airline websites on average, with very few conducting a more extensive search process. This is a striking result given that there are 18 major airlines operating in Germany and also 18 in the United States.

5.2 Hypothesis 2

The comparison websites are significantly larger than the airline companies in both markets, measured by unique visitors. It is therefore important to understand the generic online search models as shown in Figs.  1 and 2 in order to gain an overview of online consumer search behaviour. The results are shown in Table  7 .

The unduplicated visitors to all airlines is the total number of individuals that visited one or more of the airline websites within the time period of 1 month, in this case May 2014. The definition of unduplicated visitors to all of the comparison websites is the same. Based on the empirical results from the three unduplicated visitor reports, the distribution of searchers across the three search models is calculated, as shown in Fig.  2 .

The importance of comparison websites is demonstrated by the sum of Model 2 and Model 3 users, which gives the percentage of all users that visit a comparison website, either in conjunction with primary search (Model 3), or visiting comparison websites only (Model 2). In Germany 60 % (35 and 25 %) of the total user group in this sample visit comparison websites and in the US the figure is higher at 73 % (43 and 30 %). Hypothesis 2 is therefore accepted.

These results also mean that in Germany, 40 % of users only visit airline websites, and in the US, this number is only 27 %. This means that there are two distinctive groups of online users that visit airline websites: those that don’t use comparison websites, and those that do use comparison websites. This presents an opportunity to analyse the generic search models to test Hypothesis 3 by comparing the search behaviour of these two groups in more detail.

The specific research objective is to test whether comparison websites act as a substitute for primary search with airline websites, stimulate primary search, or have no discernible effect. This is a crucial question because a plausible explanation for small online consideration sets is that consumers use OTAs or meta-search engines, which have comparison functionality, rather than conduct their own search directly with individual airline websites. On the face of it, this seems a rational search strategy. However the actual effect of comparison websites on primary search has not been tested in previous research and online panel data provides an ideal opportunity to conduct what is a natural experiment on a very large sample of online users (Meyer 1995 ; Chen et al. 2011 ; McLeod 2012 ).

5.3 Hypothesis 3

The purpose of this hypothesis is to test the effect of the use of comparison websites on the propensity to conduct additional primary search. A sample of the largest airline pairs in Germany and the United States was taken in order to investigate the propensity to search for a further airline within this group. In order to test the interaction of searchers with airlines and comparison websites, the following OTAs with the largest number of visitors were selected for each country: Fluege.de (Germany) and Expedia.com (US), see Tables  3 and 5 for further details. The set analysis used to calculate the results is shown in Fig.  3 . The empirical results for Germany are shown in Table  8 and those for the United States are shown in Table  9 .

Note that Group 1 members only conduct primary search and are Model 1 type users. Group 2 conduct primary search and also visit comparison websites, and are Model 3 type users (see Figs.  1 , 2 ). This analysis therefore applies to 65 % of the German market and 57 % of the US market. The remainder in both markets only visit comparison websites and the question of the effect of the comparison website on primary search is not applicable.

The probabilities shown for Groups 1 and 2 represent the probability for a user of the airline in column 1 also visiting the airline shown in column 2, within the sampling period of 1 month. For each airline pair in both Germany and the United States, Group 2 users are significantly more likely to conduct search in both airline websites. The third column shows the ratio of the probabilities to conduct further search for Group 2/Group 1. N.B. Similar analyses were also conducted with the OTA Opodo in Germany and the results were consistent with those shown below. The analysis was also repeated in both markets using Kayak.com, a meta-search engine, and similar results were observed.

The results in Tables  8 and 9 show a clear difference between the search behaviour of groups 1 and 2 for both markets and for every single natural experiment. It is therefore reasonable to reject the hypothesis that the use of comparison websites acts as a substitute for direct search because each experiment disconfirms this idea. Hypothesis 3 is therefore rejected. Instead the results suggest that the OTAs (Fluege and Expedia) are a catalyst for the consumer to conduct further search, which is evidenced by a substantially higher probability of visiting a further airline, which will lead overall to a more extensive search process. The evidence to support this catalyst hypothesis is very strong and based on 42 separate individual experiments that use the set theory shown in Fig.  3 . The US and German airline markets are both very large and highly sophisticated, and the analysis of the largest airlines and OTAs in these markets means that the results are based on very high volumes of search activity in both markets.

6 Discussion and limitations

These results have several important implications for search theory and management practice. Taking the airline websites separately, the consideration set is relatively small and this result is consistent with earlier studies that used online panel data to accurately measure real behaviour of very large samples of users (Holland and Mandry 2013 ; Zhang et al. 2006 ; Johnson et al. 2004 ). The most obvious possible explanation for a relatively narrow search pattern is that comparison websites are used in place of primary search with the airline websites but our evidence does not support this idea, and instead we conclude that comparison websites, whether this is an OTA or a meta-search engine, increase the level of direct search with airline websites.

The managerial implications of our results for airlines is that it is vital for airline companies to continue to build awareness of their brands and their services through offline and online advertising so that customers include them in their consideration sets. The OTAs and meta-search engines are powerful partners because they promote the airlines and airlines must therefore work with these online marketing partners whilst also attempting to maintain their direct relationships with customers. Airlines should exploit their historical advantages from their loyalty schemes and knowledge of frequent flyers to encourage direct search. Comparison websites should continue to build incentives for customers to search with them, e.g. better prices and different services.

The results for the generic search patterns reported in Table  7 demonstrate the high level of usage of comparison websites, which have advanced, multi-criteria search functionality across airlines. However, this doesn’t give any information about the effects of the comparison website on primary search patterns. The generic search pattern results mean that the population of users that look at airline websites can be divided into two groups, those that don’t use online travel agents, and those that do use online travel agents. The more detailed analysis of these two groups shown in Table  8 demonstrates conclusively that the online travel agent acts as a catalyst to increase the level of primary search. That is, the use of comparison websites stimulates primary search with airline websites rather than acting as a substitute for primary search. Relating this result to the online consideration set results shown in Table  6 , an important corollary of these results is that if users of comparison websites are more likely to conduct additional research with a second airline, then the average online consideration set of model 1 searchers (those that only use airline websites) must be lower than that of model 3 searchers (those that use both comparison websites and conduct primary research). The average online consideration sets reported in Table  6 is based on the union of model 1 and model 3 searchers. Model 1 searchers must therefore have an average online consideration set lower than 2.58. The logic is that if comparison websites increase the likelihood of additional search, then model 3 users will visit more airline websites than model 1 (non-comparison website) users. The figure of 2.58 for Germany is based on all users that visit airline websites, i.e. model 1 and model 3 users. This means that model 1 users must have a lower online consideration set than model 3 users. The research issue regarding small online consideration sets remains an important question that cannot be explained by the use of comparison websites.

The sample in our research is of the order of magnitude of one million in the United States, and 100,000 online users in Germany. These samples are two to three orders of magnitude larger than traditional research samples in academic surveys, where a very large survey would be around one thousand. In total 42 natural experiments were conducted and reported in Tables  8 and 9 . In addition, a further 24 natural experiments were conducted with Opodo in Germany (8), and Kayak in both Germany (8) and the US (8).

The differences between OTA users and non-OTA users are measured in multiples of between 1.4 and 10, i.e. these are not small differences in probability between two samples of Group 1 and Group 2. In Germany, online users are on average four times as likely to visit a further airline website if they use online travel agents compared to those that do not use online travel agents (i.e. the average of column 5, Table  8 ), and in the US the figure is 2.38. Some possible explanations for the difference in the effect of the comparison website on direct search between the US and Germany are prior knowledge of the market and decision making style (Karimi et al. 2015 ) and industry concentration (Holland and Jacobs 2015 ). The results are consistent for every single airline pair tested, including the additional research with Opodo and Kayak. Statistical tests are therefore not applicable because at this level of sampling differences of this magnitude are real differences and cannot be attributed to large variances or sampling error. Nevertheless, a t test was calculated for both countries. The results were consistent in the US and Germany ( p  < .001) and confirm the statistical significance of the differences between the probabilities.

Notwithstanding the scale of the data sample, there are some limitations to the study. In order to be consistent with prior research into the concept of the consideration set, we excluded comparison website visits from the calculation of the consideration set. However, the high use of comparison websites suggests that search in the airline market is more extensive than the consideration set suggests, at least for Model 2 and Model 3 searchers.

Secondly, we make the distinction between individuals visiting only one airline website, which we define as conducting e-service, and those who are visiting two or more airline websites, which are defined as searchers. The assumption is that customers who are actively searching for flight information visit more than one airline website, and that those who visit just one airline are most likely to be conducting some form of e-service. There are two possible errors here: (1) e-service users are actually searching but only visit one airline; (2) someone may be conducting e-service on two or more airline websites. The scale of these errors though is likely to be small because the assumptions are plausible and consistent with prior literature on the consideration set. Note that if we include all of the online visits to just one website as searchers, then this would reduce the size of the online consideration set considerably, which is stronger support for hypothesis 1.

Furthermore, we are aware that search for scheduled flight services has distinct characteristics when compared to other product categories. For many travellers, the availability of flights has a significant influence on the purchasing decision. Gaining transparency over available flights is therefore often the first step, which then can be followed with more detailed search on the airline websites. The mapping out of the actual customer journey is a subject for future research. The small online consideration sets may be partly explained by the familiarity of customers with specific routes and possibly also limited options, and the propensity to fly with one airline to take advantage of loyalty programs. A further explanation might be the existence of airline alliances on which single brand websites such as American Airline contain other partner brands and flight options, which enable the customer to consider other flight options without needing to visit other airline websites.

7 Conclusions

The methodology developed in this research illustrates a novel use of online panel data to explore more detailed aspects of search behaviour, in particular the interaction effects between different types of websites, in this case comparison websites and airline websites. The use of set theory to analyse audience duplication reports is a novel methodology to create and analyse Venn diagrams of overlapping search behaviour between groups of websites. This approach made it possible to measure the high level generic search patterns that are shown in Fig.  1 , and also provided a mechanism to analyse model 1 and model 3 searchers in more detail.

The marketing concept of the consideration set was applied in an online context and operationalized using airline websites only, i.e. primary research. The results of 2.58 in Germany and 2.74 are consistent with earlier studies. The average online consideration set can be expanded to estimate the distribution of searchers which shows that very few consumers, only 14 % of the total, look at four or more websites. This raises the question about why 86 % of consumers only look at 2 or 3 airline websites rather than follow a rational, extensive search strategy.

The most obvious explanation for the small online consideration set is that comparison engines have extensive search and comparison functionality and that this is used as a substitute for extensive primary search. To an extent this is true because a high proportion of online users in both markets use an OTA or meta-search engine. However, the propensity to conduct more direct research is significantly higher for those users that include an OTA or meta-search engine in their search process than those users that do not and only look at the airline websites. The conclusion therefore is that comparison websites are a catalyst for further direct research, rather than a substitute for direct search with individual airline websites.

In a more general sense, the empirical evidence reported here does not support the rational consumer model, where one would expect consumers to either conduct extensive primary search with the airline websites, or use a combination of airline and comparison websites. An explanation for the apparently irrational behaviour of consumers in their search for airline tickets must therefore be found elsewhere. Bounded rationality (Simon 1955 ), brand loyalty (Jacoby and Kyner 1973 ), lack of perceived competition in pricing and flight choice and repeat buying behaviour are all rich areas for future research.

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Acknowledgments

This research is supported by the Fonds National de la Recherche, Luxembourg (7842603). The authors would like to acknowledge comScore for providing the research data for this paper. Please see http://www.comscore.com/About-comScore for further information about comScore. The authors have conducted the analysis and interpretation of the data, and any errors in the paper are the sole responsibility of the authors.

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Holland, C.P., Jacobs, J.A. & Klein, S. The role and impact of comparison websites on the consumer search process in the US and German airline markets. Inf Technol Tourism 16 , 127–148 (2016). https://doi.org/10.1007/s40558-015-0037-9

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Received : 26 January 2015

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DOI : https://doi.org/10.1007/s40558-015-0037-9

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The 15 best price comparison sites for your online products.

If you buy something through our links, we may earn money from our affiliate partners. Learn more .

Projections say nearly 2 billion people are expected to purchase goods and services online this year. And price comparison options remain important. If your business offers online shopping options, you probably know how much shoppers value finding a great deal.

If you want to really reach price conscious consumers online, make it easy for them to compare your products with similar items by listing them on price comparison sites. Check out a variety of these sites and apps currently available. Some connect to search engines or other platforms, and others just crawl the web for the best deals. Learn what you need to know about price comparison sites and reaching customers on these platforms.

Criteria for Choosing the Ideal Price Comparison Site: Our Methodology

Selecting the right price comparison site for your online products is crucial to boost your business’s visibility and sales. To help you make an informed choice, we’ve developed a comprehensive set of criteria, ranked on a scale of importance from 1 to 5, with 5 being the highest importance and 1 being the lowest:

• Accurate product data and pricing information is paramount. Choose a site known for its up-to-date and precise data.
• A site that aggregates products from a vast network of retailers offers more options for your customers, increasing the likelihood of sales.
• An intuitive and easy-to-navigate interface is essential for a positive user experience. Customers should find it effortless to search and compare products.
• In today’s mobile-driven world, a price comparison site should have a mobile-responsive design or dedicated mobile apps for on-the-go shopping.
• Customer feedback can influence purchasing decisions. Consider a site that displays product reviews and ratings prominently.
• Ensure the site integrates smoothly with your e-commerce platform, allowing seamless updates of product listings and pricing.
• Evaluate the pricing structure for listing your products on the comparison site. It should align with your budget and offer a good return on investment.
• Higher traffic and a larger user base can translate into more potential customers viewing your products. Opt for sites with a significant online presence.
• Access to analytics and insights about your product’s performance on the site can help refine your marketing and pricing strategies.
• Responsive customer support is vital if you encounter issues or have questions about your product listings. Ensure support channels are available.

Best Price Comparison Sites

Google shopping.

Online shoppers already turn to Google to look for specific types of products. So get listed on Google Shopping and your product automatically comes up alongside options from other stores right on the search page. To get listed, you need to register your online shop in Google’s Merchant Center and then a feed of your products will be listed. They also offer options for advertising and promoting certain products.

Amazon Product Ads

Even if you sell your products on your own ecommerce site, listing on Amazon can help you gain visibility for your products when people search for similar items. You can easily set up an Amazon store and use it as your primary ecommerce destination or just a supplementary one. Then you can invest in product ads to get your products featured prominently.

PriceGrabber

PriceGrabber is a price comparison site that includes product listings across a ton of different categories. Customers can search for a specific item or browse more general listings. To get your products featured, you need to sign up for a Connexity cost-per-click listings network account.

CamelCamelCamel

CamelCamelCamel is a price comparison engine that mainly focuses on Amazon listings. It even lets customers get alerts when prices drop on certain items and see the history of price changes. If you have products listed on Amazon, they’ll automatically be available for shoppers to use CamelCamelCamel’s functionality.

BuyVia is a deals site that allows shoppers to compare prices on products, access coupons and find deals on the items they’re shopping for. The company also offers mobile apps in the App Store and Google Play. The team scours for deals across Google Shopping and other platforms. But you can also request a deal for specific products on the website or app.

Shopping.com

Shopping.com is a price comparison site that lets consumers view products in a particular category or search for specific items from top brands. To join, you can sign up for an eBay Commerce Network merchant account and then connect your store.

Pronto offers a search engine that is specifically for products. Consumers search for a particular item and find product listings so they can compare prices and other features. The site crawls through ecommerce shops to find these items. So focus on SEO for your shop get included in these searches.

ShopSavvy is a mobile app that lets shoppers scan bar codes of certain items and then compare those items to other products in local stores and online. The products are largely added by users. But ShopSavvy also uses a back-end system called PriceNark. You can connect your store to that engine to give your products more visibility.

PricePirates

PricePirates allows customers to search for products, compare prices and find deals on a variety of items. The site crawls through various ecommerce platforms to find these listings. Specifically, it includes many options from top marketplace platforms like Amazon and eBay.

Shopzilla offers deals and price comparison tools to help shoppers find value on a variety of products. They also provide a merchant services option for businesses. Get your products included in the site by signing up with Connexity.

Bizrate provides price comparison options along with shopping guides and weekly promotions. The site also uses Connexity for its merchant services option. Sign up with them to learn more about being featured.

Bing Shopping Campaigns

Though Bing lags behind Google in popularity as search engine, it still offers a fair amount of visibility for products. When people search for an item on Bing, your item appears next to others like it. And you can purchase ads from Microsoft to increase visibility for your products in searches.

YahooShopping

Yahoo is another search engine that offers a shopping option that makes it easy for consumers to compare prices from different vendors. Yahoo Shopping connects to PriceGrabber. So once you sign up for an account on that site, you can easily get your products listed on Yahoo as well.

Check out Become another search based price comparison tool. Become offers a fairly bare bones service, and simply crawls various platforms to find products related to a particular search. Just focus on SEO and improve your chances of getting your products in front of customers.

ShopMania offers a wide array of online tools for shoppers. They browse by category, search for specific items, and view special deals. Get your products featured. Just sign up for an account on the website and update information about your shop. The site also provides merchants with seller tools and the option to set up a storefront on their Facebook pages.

Building a Strong Product Listing: Tips for Price Comparison Success

Creating an effective product listing on price comparison sites is crucial for attracting potential customers. Here are some tips to help you build compelling product listings:

• High-Quality Images : Include clear, high-resolution images of your products from multiple angles. High-quality visuals can significantly impact purchase decisions.
• Detailed Product Descriptions : Write informative and concise product descriptions that highlight key features, benefits, and specifications. Help customers understand what sets your product apart.
• Accurate Pricing : Ensure that the prices listed on the comparison site are accurate and up-to-date. Inaccurate pricing can lead to customer dissatisfaction.
• Competitive Pricing Strategy : Research your competitors’ prices and consider offering competitive pricing to attract budget-conscious shoppers.
• Customer Reviews and Ratings : Encourage satisfied customers to leave reviews and ratings on the comparison site. Positive feedback builds trust and credibility.
• Clear Shipping Information : Provide transparent shipping details, including estimated delivery times and any shipping costs. Clarity can prevent cart abandonment.
• Product Variations : If your product comes in different variations (e.g., sizes, colors), clearly list and differentiate them to help customers find their preferred options.
• Promotions and Discounts : Highlight any ongoing promotions, discounts, or special offers. Deals can entice customers to make a purchase.
• Availability Status : Keep your product availability status accurate. Inform customers if a product is out of stock or available for pre-order.
• SEO Optimization : Optimize your product titles and descriptions with relevant keywords to improve visibility on search engines and within the comparison site itself.
• User-Friendly URL : Customize your product’s URL on the comparison site to make it more user-friendly and memorable.
• Mobile Optimization : Ensure that your product listing is mobile-responsive, as many users shop on smartphones and tablets.
• Contact Information : Provide clear contact information, such as a customer support email or phone number, in case shoppers have questions.
• Return and Refund Policy : Communicate your return and refund policy to build trust. Transparency in handling returns can reassure potential buyers.
• Customer Assistance : Be responsive to customer inquiries and assist them promptly. Positive interactions can lead to higher conversion rates.

In a rapidly evolving digital marketplace where nearly 2 billion people are expected to shop online this year, price-conscious consumers seek exceptional deals. Listing your products on price comparison sites is an invaluable strategy to cater to this growing audience.

To ensure your business maximizes its visibility and sales potential on these platforms, we’ve provided a comprehensive set of criteria to choose the ideal price comparison site. These criteria, ranging from data accuracy to customer support, form the foundation of your decision-making process. By carefully evaluating these factors and understanding their importance, you can make informed choices that drive your business forward.

Additionally, we’ve outlined essential tips for creating compelling product listings on these platforms. From high-quality images to responsive customer assistance, each element plays a vital role in attracting potential customers and boosting your competitive edge.

Incorporating these strategies and criteria into your approach will empower your business to thrive in the dynamic world of online shopping. By embracing the power of price comparison sites and optimizing your product listings, you’re poised for success in meeting the demands of the modern, price-savvy consumer.

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Great article… I’ve noticed the same player referenced by a number of authors. This makes it challenging for younger upstarts to get recognized.

Please take a look at https://www.pricecheckhq.com/ the next time you are considering writing another article on price comparison websites.

It’s true, price comparison websites are changing the way we shop online. I believe it’s the future of online shopping as it’s helping a lot of people saving money online.

Very nice article!

Also you can take a look at https://pricesandshops.com (price comparison for the USA)

Too many of these doing the same thing. I was searching for guide on Laptops and ran into this site http://www.slankit.com . They are doing amazing thing, not just comparing prices, they match needs to technical spec which I think is pretty cool. I wonder why these authors dont see site like SlankIT and cover or are they getting incentives from the big box sites? lol

Great article,

The internet is now getting crowded full of too many compare sites, most of the top search results point to products from sellers on Amazon or Ebay followed smaller merchants

Nice and interesting information and informative too.

Never heard of camelcamelcamel but I’ve used it now! Thanks for that tip, I will add price comparison is, although annoying sometimes, a great move forward – for those savvy with money it’s the way to go, others are doing the research and providing an easier choice for us.

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