Comments for How do comparison shopping sites make a living?

It is well known that the Internet revolutionized lots of things. Among those things, we can say that it revolutionized the way of doing shopping. While in the past it was necessary to go to the “The Mall” to buy items in person, it is now possible to shop online. Technologies such as shopbots allows consummers to easely locate a bunch of products in the same place and clearly reduce the cost of searching for the best deal.

At first glance we may think that all those shopbots are the same and that, at the end, they do not have many ways to differenciate themselves from the competition. Nevertheless, we can easily find on the web several articles that provide a list with the “best” shopping sites. (1). This raises the question of the possibilities that exist for those websites to differenciate themselves from the competitiors. What are the sources of differenciations for the shopbots?

It is important to underline that with the growth of the shobots, those webistes are now competing among themselves either that only with the traditional shops. Thus, the shopping sites can no longer limit themselves to practical and attractive offers solely from the point of view of the price. It is now necessary for them to find other sources of differenciation. Bèzes et Belvaux showed that only the organisation of the offer and the way the firms establishe their worth seem to allow the shopping robots to differeniate themselves among them. (2)

Here, I focus my reasoning on the problem of differenciation for the shopbots. Yet, there is another point that in my opinion warrants attention : shopping sites allow consumers to compare offers and can therby facilitate their access to information. Well, it is one thing to say that consumers have a better acces to the information but we could also think the other way around : in which extent do those websites collect informations about the consumers in order to better target them.

(1) http://www.huffingtonpost.ca/2012/10/26/top-online-shopping-sites_n_2025607.html
(2) Bèzes, C., Belvaux, B., (2012), Quels éléments de différenciation pour les sites web marchands ? Une approche par l’image transmise, Management & Avenir, Vol. 8, N°58
(3) http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1210&context=ecis2008

Comparison shopping sites are knowing growing popularity nowadays but are they really increasing the competitiveness of product markets?

Following the Bertrand model with the hypothesis of total transparency, the competition between sellers is high. We could think that shopbots will lead to more transparency and to an increasing competition.
However, although price dispersion seems to become lower it is doing slowly. (1)
There are others factors we need to take in account to understand why the allocation with shopbots is not efficient.

Firstly, if the competition is too high with the introduction of comparison websites, the sellers’ interest to pay to be on the platform will decrease. Shopbots may find advantageous to raise their participation charges in order to obtain more rental from the firms. (2) It will indeed exclude some firms who cannot afford high fees. This leads to more price dispersion and less value for the consumers.
However, we must not forget that there is also competition between the shopbots themselves.
Is excluding cheap low quality products a really good deal for shopbots knowing that their principal users are price sensitive (2) and will easily turn to a competitor with better offers?

Secondly, we must keep in mind that all consumers are not fully informed despite the absence of charges to entry on the platforms. We could consider that loyal consumers are like uninformed in the spatial price dispersion. Brand and consumer loyalty can indeed lead to select higher-price vendors. (3)

In my opinion, knowing that in practice perfect competition is not reachable, a win-win situation would be to add recommendation datas to shopbots. In this way, a larger number of sellers could coexist on the platforms keeping price dispersion. Users would indeed looking at others aspects than prices like quality. Profit for firms will remain keeping a value for customers.

(1) http://www.cluteinstitute.com/ojs/index.php/JBER/article/view/8068/8122
(2) Moraga-González, J.L. & Wildenbeest, M.R., (2011)., Comparison Sites
(3) Simon, D. & Parsons, P., (2002)., Game Theory and Decision Theory in Agent-Based Systems

First of all, we could say that shopbots have increased competitiveness between retailers because they allow all the consumers to be fully informed since they can have access to these sites freely. This enables them to choose the cheapest product available. But this is true only if the price of the product is the only parameter taken into account. Indeed, according to Brynjolfsson and Smith (2000) (1), the brand and its reputation are also really important in the consumers’ mind, especially for customers that are more sensible about the non-contractible aspects of the product bundle, as delivery times for example. One of the problem of the comparison sites is the fact that they provide no information about service quality across retailers. Indeed, the spatial separation between consumers, retailers and products caused by the Internet increase the importance of service quality but decrease service quality cues available to customers (Smith, Bailey, and Brynjolfsson 2000) (2). Besides, in addition to the usual fees like the ‘cost per click’ or ‘cost per acquisition’ models, the comparison sites have implemented extra fees for additional services such as allowing the sellers to obtain priority positioning in the list (Moraga and Wildenbeest, 2011) (3). Those extra fees can result in misleading the consumer about the cheapest product available. This decreases the market efficiency. Then, it is not in the interest of the comparison sites to accept all firms’ participation, because they want to avoid the Bertrand paradox in the product market. Because if firms’ profit are decreasing, this means that they will be less able to pay those sites and thus the shopbots’ profits may be reduced too (4). Also, firms have found many ways to limit the competitiveness’ pressure. For example, a technique often used is to “offer a low quality product at a very low price to attract customers and then trying convincing them to pay extra fees to get the product they really wanted in the first place”. (5)
Cambini, Meccheri and Silvestri (6) have well summarized the effects of comparison sites on the competitiveness of product markets :“that online trading increases competition between firms, guarantees less expensive products, and so increases consumers’ surplus, but still higher than marginal price. The shopbots charge firms in a way that is above the socially optimal one, thereby inducing a price discrimination.”
“The market efficiency may depend on the firms’ ability to charge different prices whether they are on- or off-comparison sites. According to that, the behavior of the comparison sites will not be the same. When the products are homogenous and the retailers have to charge same prices on- and off-comparison sites, shopbots will charge fees so high that it will exclude some firms, which will lead to price discrimination and thus the market will not be efficient. But, if retailers can charge different prices on- and off-comparison sites, the shopbots will attract all the players to the platforms and the allocation is thereby efficient.” (7) Furthermore, it is also said that when there is a big vertical product differentiation, the comparison sites may be tempted to charge high fees to exclude low quality producers. In this case, the market efficiency is once again threatened. (8)
(1) Cambini, C., Meccheri, N., Silvestri, V. (2011). Competition, efficiency and market structure in online digital markets. An overview and policy implications. Online http://revel.unice.fr/eriep/?id=3212#tocto1n4 consulted on the 19/04/16.
(2) Clay, K., Krishnan, R., Smith, M. (2001). The Great Experiment: Pricing on the Internet. Online http://www.heinz.cmu.edu/~mds/papers/ebh/ebh.pdf consulted on the 19/04/16.
(3) Moraga, J., Wildenbeest, M. (2011). Comparison sites. Online http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1947292 consulted on the 19/04/16.
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Internet Shopbots are automated tools that gather information from online stores, allowing customers to easily and freely search for prices and product characteristics from different online retailers.

As a first step,I primarily wanted to highlight some relevant points from the text itself:

1) The existing business models for shopbots to charge online retailers:
• Flat fee giving the right to be listed on the platform
• Cost per click, paid every time a consumer is referred to the seller’s website from the comparison site
2) According to the “Bertrand Model”, when firms:
• Are competing setting the product price
• Produce it at exactly the same constant unit cost
• Face a large number of fully informed consumers who can freely acces the information
⇒ Firms will set their price equal to the unit cost of production.

Before getting into my research and analyze on the impact of such shopping robots on the competitiveness and efficiency of markets, I firstly wanted to give my personal opinion on the topic. In this way, as far as I am concerned am I convinced that, knowing the existence of those shopbots, if online retailers are present on them, it’s because they can benefit from such platforms, otherwise they wouldn’t be there. Nevertheless this benefit could arise from different sources. I identified 3 of them.
• Financial interest
• Notoriety and reputation interest
• It allows them to face competitors already present on the platform. In fact they may not have the choice if they want to survive on the market.

Price dispersion among commodity goods is typically attributed to consumer search costs. Nevertheless, such comparison shopping sites induce full transparency and annihilate customers’ search costs. Therefore, according to the “Bertrand Model”, firms facing the same constant unit cost should set their price equal to the unit cost of production. However, as explained in the text, whatever the type of product, there is price dispersion between online retailers and firms still earn positive profits at equilibrium. So the question remains “Does Shopbots increase the competitiveness of product markets and enhance market efficiency?”.

In the first instance, market observers predicted that online retailers would be victims of those automated tools, at the benefit of consumers. Effectively, shopbots would radically reduce consumers’ search cost while decreasing retailers opportunities to differentiate their products, driving this way their margins towards zero. However, even if shopbots could put some pressure on retailers’ margins, it appears that they still retain numerous opportunities to differentiate their products, leverage brand names, set strategic prices, and reduce the effectiveness of consumer search at shopbots. Moreover, in my opinion, even if it is the retailers who theoretically have to pay the fee, they could make the consumers bear the weight of this cost through increasing the products’ price.

In fact, a recent review of the academic literature (suggesting that shopbots would dramatically increase pricing pressure on retailers margins) highlights the fact that 3 important factors were ignored:

a. “The spatial and temporal separation between customers, retailers and product imposed by electronic markets by electronic markets means that service quality is an important product attribute, even for otherwise homogeneous physical goods”.
b. “Second, and similarly, while it is easy for shopbots to communicate some product characteristics (such as price), others (such as service quality and reliability) are more difficult to communicate. In effect, the Internet does not uniformly search costs for all aspects of the product bundle. This represents an opportunity both for retailers and shopbots in terms of how to display information to customers”.
c. “Third, because of their business models, shopbots have divided loyalties between the interest of customers and retailers. These divided loyalties may explain why recent changes in shopbots interfaces seem to make it more difficult for customers to find the lowest price.

For these reasons, unlike what many economists predicted, the resulting impact of shopbots on electronic market looks more like an “arms race”, giving additional powerful weapons to customers as well as to retailers, in order to reach their respective goals, which is “to find the best deal” and “to make profit” for the online retailers.

The early predictions regarding the impact of shopbots have been correct in at least one way: some shopbot customers appear to be very price sensitive. However, the point where they made a mistake concerns the online retlaiers and their profit margins. Indeed, as observed by Brown and Goolsbee, most of online retailers set their price with respect to a Mixed-strategy equilibria, in the presence of both informed and uninformed customers. In this model, contrarily to uninformed customers, the fully informed ones buy from the “best deal” retailer. This way, firms could respond by drawing prices from a U-shaped distribution. Therefore, firms could either decide to target uninformed customers and charge higher prices to maximize revenue, or they could decide to maximize their sales through focusing on informed customers. Moreover, subsequent researches showed that shopbots didn’t necessarily lead to lower prices in internet markets and could either imply higher prices as the number of retailers present on the platform increases.

Another way for retailers to decrease the pressure on their profit margin consists to reduce the effectiveness of consumer trough shopbots what can be achieved, for instance, by “encouraging shopbots to sort tables based on retailer advertising rather than price”.

In conclusion, even if we might think that such automated tools would drive price drastically down by setting pressure on online retailers, researches have shown that they retain a multitude of strategic options to minimize it, such as price discrimination, strategic pricing, bait and switch, and search obfuscation.

Sources:

http://ebusiness.mit.edu/research/papers/194A_Brynjolfsson_Internet_Shopbot.pdf
http://www.heinz.cmu.edu/~mds/isem.pdf
http://www.shopbottools.com/

The online retail has deeply changed our way of shopping. While our parents only had access to limited information when making a purchasing choice, we’re know (over)loaded with possibilities. However, the internet is as huge as it gets and relevant information may sometimes be hard to find. It is therefore very complicated for customers to be fully informed, allowing companies to sell at higher prices to uninformed customers. But this tendency may tend to decrease as the use of shopbots rises. Those seem to do all the information collection work for us. But is it really the case and if so, how does it impact the competitiveness of the market ?

First, let’s have a word about the information and its impact on prices. The existence of online retail allows new actors to find a place on the market, as some companies only sell online, sparing the cost of opening a physical store. When making a purchase, consumers have therefore the choice between a broader range of sellers. The true difficulty lies in finding all those sellers on the internet. You may find some of them easily but those won’t necessarily be the cheaper. In fact, with the web being as it is, consumers can’t be sure they’ve explored all possibilities. Companies can therefore price their product higher, as the search cost of consumers is pretty high. The introduction of shopbots into this landscape has two effects: an increase in the number of alternatives considered & a decrease in search time and cost. Consumers are therefore informed way better than before. Shopbots also act as a showcase where all retailers are aligned next to another, leading to an easier comparison. Once they’re aligned, studies show that they aren’t different from one another to the customer, the only differentiation aspect being the price. Lower search cost and better information would lead the market towards a Bertrand-type competition as explained in this paper. Tang, Smith & Montgomery have actually observed this shift when discussing the impact of shopbots on online book retailing. They saw that “an increase of 1% in shopbot use is correlated with a $0.41 decrease in price levels”. This conclusion is based on data collected between 1999 & 2001, so we can assume that the impact could be even higher nowadays.

This leads us to discuss a second aspect: the degree of use of shopbots. The impact of shopbots on competitiveness depends highly on how many consumers actually go through them when buying. The paper of Passyn, Diriker & Settle in the Journal of Business & Economics Research is particularly interesting on this matter. It would like the explosion of shopbots usage is rather relative. Although the amount of consumers using them is in substantial growth, its usage remains limited to a certain segment of the market, namely the “innovative and/or price sensitive and/or trusting of the online retailers”. This explains why the decrease in prices and price dispersion is relatively small as of now. However, they expect that shopbots will eventually reach a broader segment of population and that the uninformed customers (being the non-users) will remain so for a very long time. Therefore, the competitiveness should continue its growth at an even higher rate than now.

This last sentence leads us to our conclusion. Online retailers should already prepare themselves for this increase in price competition, leading to a decrease in their margin. The first solution would be to review their production process in order to maintain the margin and support the eventual price decrease but this isn’t an easy task as most retailers already have optimized their production. The second solution is, to me, the most interesting. As we’ve said earlier, when facing a range of alternatives on shopbots, consumers don’t differentiate them with anything else than the price. Retailers should therefore try to gain the attention of consumers with something else than their price. This means creating strategies to “distinguish themselves from others and to create and intensify trust and loyalty”.

References :
http://www.cluteinstitute.com/ojs/index.php/JBER/article/viewFile/8068/8122
http://repository.cmu.edu/cgi/viewcontent.cgi?article=1046&context=heinzworks

There is no longer any doubt that the impacts of the Internet throughout the economy and society are multiple and diversified. The Internet is clearly changing the traditional ways of doing business and I will specifically look at one of its impacts, which is the way that consumers make purchasing decisions. Nowadays, consumers are able to collate prices information and product offerings to find the best deal. Self-evidently, online retailers must adapt themselves and overcome the difficulties of operating in a highly competitive environment.

In this context, since the late 1990s, Internet shopping robots (shopbots) are important drivers of lower consumer search costs and act as infomediaries (information intermediaries). On one hand, they enable consumers to compare information on prices, availably as well as product and seller characteristics from many online retailers relatively quickly. One the other hand, they enable firms to transmit information on their products. However, these comparison shopping sites raise lots of questions about the competitiveness and efficiency of product markets.

As two-sided platforms, shopbots’ optimal pricing decision depends on countervailing effects. To begin with, shopbots deal with cross-side network effects since firms are better off when the number of consumers increases and the other way round. But far more importantly, shopbots also face within-side network effects. The higher the number of online retailers entering the site, the stronger is the competition in the retail market. The firms’ willingness to pay to join the platform will decrease since the competition reduces prices and consequently firms’ profits. Moreover, online vendors regularly need to adjust their prices to remain competitive because of the fast-moving environment.

While shopbots increase market competition, they can generate efficient product markets. Indeed, consumers gain access to fewer retailers offering competitive prices. The larger vendor monopolies are therefore reduced. In this context, Moraga-Gonzàlez and Wildenbeest have analysed two different cases of comparison sites: homogeneous product sellers and differentiated product sellers. The former leads to inefficient market allocation, and the later can lead to market efficiency.

Regarding the first case, firms are not able to charge different on- and off-the-comparison-site prices. Consequently, the fees charged by the shopbots will be so high that it will exclude some online retailers. That implies products are sold at prices exceeding marginal cost. In the second case, firms can charge different on- and off-the-comparison-site prices, and the shopbots will attract all the vendors to the platform.

To cut a long story short, although competitive pressure has increased with the rise of shopbots, it also improves overall market efficiency.

SOURCES :

Cambini, C., Meccheri, N & Silvestri, V. (2011). Competition, efficiency and market structure in online digital markets. An overview and policy implications. Online http://revel.unice.fr/eriep/?id=3212#tocto1n4, consulted on 19/04/16.

Clarke, S. (2009). Shopbots: a syntactic present, a semantic future. Online http://www.icsd.aegean.gr/kotis/OE%26SW'09/papers/S-shopbots.pdf, consulted on 19/04/16.

Moraga-González, J. L. & Wildenbeest, M.R. (2011). Comparison Sites. IESE Business School Working Paper No. 933. Online http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1947292, consulted on 19/04/16.

Smith, MD. (2002). The impact of shopbots on electronic markets. Online http://www.heinz.cmu.edu/~mds/isem.pdf, consulted on 19/04/16.

MacKenzie, I., Meyer, C. & Noble, S. (2013). How retailers can keep up with consumers. Online http://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers, consulted on 19/04/16.

We saw that comparison shopping sites manage to make a living. It is indeed interesting as an intermediary providing information for shoppers and sellers. Comparison shopping sites are able to charge both of these stakeholders as it creates value in providing advertising for sellers and comparison information for buyers.

From my point of view, Shopbots affect the behaviour of customers and firms cannot deny this. I think that being listed on the intermediary websites is important for the firms because a large part of the consumers likely use those websites. Moreover, most of the products sold by the firms aren’t really different. Hence, I think that gather all the prices will tighten the competition. However, I cannot state on the market efficiency.
What can make the difference is that comparison shopping sites provides information not only on prices but also on quality and feedbacks.

I found an interesting study from 2015 (http://www2.warwick.ac.uk/fac/soc/economics/research/workingpapers/2015/twerp_1056b_ronayne.pdf ) that explains the impact of price comparison website on a homogeneous market. The study says that “While a consumer may not know all the firms in a market, a PCW can expose the full list of market offerings, maximizing inter-firm pressure.”
The author made a model where there are two types of shoppers: the ones who use the price comparison websites in equilibrium and inactive consumers who buy directly from a particular firm.
The study arrives as a conclusion that only one price comparison website increases competition on price between firms. Moreover, with the charge of fees, the prices go up. “The net effect is that prices increase for all consumers, who would be better off without the site.”
The author also checked with more than one price comparison website. If shoppers only check one price comparison website, this is the same situation as the monopoly.
According to the author, what is important is to know how many shopbots shoppers check.

Finally, the opinion of the author is that the consumers would be better off without the price comparison websites. “Regulatory bodies” should limit fees and the number of shopbots in the market and encourage consumers to compare products themselves.
http://www2.warwick.ac.uk/fac/soc/economics/research/workingpapers/2015/twerp_1056b_ronayne.pdf

In order to go further, we have to distingue 2 kinds of users : the “expert shoppers” and the “ novice shoppers”. Most of the shopbots will focus on the “expert shoppers” and on direct search i.e. when people will search a specific product and not only “something useful”. (1)

While a price comparison site should make the market smoother and operate a conversion to a single price for one identical product, we can observe that is not always the case. In the article, the reason of a possible positive search cost if they want to observe and to compare prices. But there are other reasons like product category, number of sellers and market imperfections. (2)

Furthermore, for certain cases, simulations lead one to believe that the presence of shopbots could lead to phenomena such as cyclical price wars and widespread monopoly pricing. The presence of shopbots might lead to retailers engaging in a veritable “arms race” who can lower their prices faster (3).

Finally, the article seeks about the research of Baye and Morgan I think we could add 2 things that emerge from their analysis : the willingness to pay for the information of the consumers is positively linked to the benefits they expect to get from it and more you have retailers on your platform, stronger the competition on the retail market will be. (with an implication that the profits will decrease and the consumers will not agree to pay to be on the platform). (4)

In conclusion, most of the case we can say that shopbots decrease the competition and increase the efficiency of the market but we have to be vigilant to certain particular occasions that I spoke earlier and that are present in the article.

(1) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4182042/
(2) http://faculty.som.yale.edu/ksudhir/search-2.pdf
(3)https://books.google.be/books?id=8ZfK9ejLp2kC&pg=PA42&lpg=PA42&dq=shopbots+implication&source=bl&ots=2IV88tXid_&sig=E7ak-BdX2N2pSeLjJhzTRffIdrs&hl=fr&sa=X&ved=0ahUKEwjYocy_jZvMAhUE7xQKHefxA0cQ6AEINjAD#v=onepage&q=shopbots%20implication&f=false
(4) http://revel.unice.fr/eriep/?id=3212#tocto1n4

1. Hypothesis

Before entering in the heart of the topic, we have first to make a hypothesis concerning the goods we are going to talk about. Some previous studies analyzed the purchase of relatively inexpensive goods, such as books (Chevalier & Goolsbee, 2003; Clay, Krishnan, & Wolff, 2001; Degeratu, Rangaswamy, & Wu, 2000). For such goods, there are of course price considerations, but the majority of consumers don’t feel like requiring online product search and price comparisons: shoppers would have perceived such small price disparities as negligible. Secondly, it is important to mention that unique designs and custom-made goods may be available from only one vendor, even if it is rarely the case [2]. Therefore, in the following sections we will only talk about durable goods of substantial value provided by several retailers.

2. How are Shopbots working?

All consumer purchase decisions are based on the possession or acquisition of information.
The emergence of shopbots has facilitated consumer’s ability to gather price and product information. Indeed, online shopping increase the number of alternatives considered, while reducing search time and costs. Furthermore, an online seller is able to update prices quickly. This implies that once a buyer has decided what to purchase (in terms of brand and model) there are only a few distinctions among online vendors of the goods. Therefore, the main distinguishing factor is price. In this case, this would lead to an increased price competition, ultimately leading to price convergence (Bertrand pricing model). Indeed, Tang et al. (2007) found that every 1% increase in shopbot use leads to $0.41 decrease in price levels in the online book retailing industry. Nevertheless, the literature explains that there is still a price dispersion (e.g., Brynjolfsson & Smith, 2000) as the result of the existence of a market segment of consumers who do not use shopbots (e.g., Waldeck, 2005) or of consumers who consider only a subsets of vendors.

3. Shopbots retailing strategy:

A simple strategy for shopbots developers to fit all search preferences is to let the shopbot covering all vendors of the market. In this case, all users can meet their search requirements by sub-selection. Moreover, a shopbot which has a full-coverage strategy would remove any question of market bias, increasing perceived fairness and building consumer trust (Datta & Chatterjee, 2008).

However, the full-coverage strategy leads to market inefficiency: search cost increase to consumers as a result of information overload (e.g., Öörni, 2003) and development costs increase for shopbots because of added complexity (programming, designing and so on). Because shopbots goal is profit maximization, they must adapt their strategy in order to balance their two competing goals: provide comprehensive and unbiased listing, and have a profitable revenue model (Michael Smith, quoted in Betts, 2001, p. 9).

4. Shopbots and consumers’ behavior:

First of all, consumers’ behavior is influenced by their expectations regarding sellers or product attributes. Sellers may differentiate themselves with better shopping functionality and more products information. This reduces the elasticity of demand of some consumers by increasing their switching cost. Confidence in transaction and loyalty are also taking into account and gives a crucial competitive advantage to the branded seller especially in e-commerce.

Secondly, consumers’ behavior is influenced on their awareness of search technology and of its ease in Web navigation [3]. Thus, some consumers will be more or less informed about prices. On the other hand, some consumers are price insensitive and attach more value to a seller’s additional or specific service offering.

5. Conclusion

As a conclusion, recent research has shown that while some shopbots users are very price sensitive, shopbots consumers are highly influenced by online retailers with a strong brand image and retailers they have already been dealt with. Despite the large amount of information provided by pricebots, shopbots consumers remain asymmetrically informed about product attributes such as service quality and perceived brand and prior experience as alternatives atributes for this missing information. Therefore, in my opinion, shopbots, despite the fact that they provide more transparency, doesn’t lead to a strengthen competition between retailers. Indeed, as some consumers don’t use shopbots services, the only segment where competition is strengthened is the segment of regular shopbots users.

References:
[1] Passyn, K. A., Diriker, M., & Settle, R. B. (2013). Price comparison, price competition, and the effects of ShopBots. Journal of Business & Economics Research (Online),11(9), 401-n/a.

[2 ]Allen, G., & Wu, J. (2010). How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy. European Journal of Information Systems,19(3), 257-272. doi:http://dx.doi.org/10.1057/ejis.2010.6

[3 ]Smith, M. D. (2002). The impact of shopbots on electronic markets. Academy of Marketing Science.Journal, 30(4), 446. Retrieved from http://search.proquest.com/docview/224872167?accountid=12156

The shopbots are two-sides platforms which play the role of intermediaries between the sellers and the buyers by allowing sellers to communicate each information of their products to the buyers. (1) Besides, shopbots allow consumers to compare easily similar products in order to choose one that corresponds to them. Then the shopbots reduce the cost of buyer’s researches because just one click is needed to obtain the information’s. (2) We will examine if the shopbots increase the competitiveness of the market products and if they improve the market efficiency.

We can notice some external effects are presents in the shopbots platforms. Indeed, there are two positive cross-side effects. On one hand, sellers are happier when the number of buyers increases because they can sell their goods to more people. On the other hand, buyers appreciate when the number of sellers increases because they have more product choices. But there is also one negative within-side effect. Firms will not enjoy a new entry of a firm on the shopbots. In fact, at most firms join the platform at most the competition will be strong and it will decrease the prices and then the profits. (1)

Concerning the competitiveness between the products, we can suggest that it is impacted by the shopbots existence. We notice that the probability that the buyer choose an offer between each offers is a downward function of price. (3) Indeed, most of the time, we imagine that buyers base their purchase decision on the price for identical goods (2). With the help of the comparison website, they can easily see all prices for their researched product. In consequences, that forces sellers to compete on prices. In this case, we observe that price dispersion follow a non-linear decrease with the website comparison. Besides, at most there are sellers on the shopbots, at most it will impact negatively the price of a product and the price dispersion because the competition will be stronger. (4)
But prices are not always the purchase’s determinant factor. In fact, buyers can also be less price sensitive and be interested by non-price attributes as vendor reputation, the reliability, the transaction, payment factors, the delivery options, the like (2), the shipping cost and the availability. (1) All these elements allow sellers to differentiate their homogenous goods and then differentiate their prices that leads to price dispersion. In addition, brand, trust and awareness are high significant in online markets and can definitely influence the consumers decisions. (1) That explains that well known brands and trustful brands can put higher prices which also lead to price dispersion. In addition, developing loyal consumers is then very important to the brand. Indeed, the creation of loyalty is the best protection against price competition. (2) Then, some buyers can choose high priced goods even if they are aware of the price disparities, because they prefer a specific vendor or several non-related price attributes.
Besides, studies shown that current online shopper do not often use price comparison website. Indeed, just a limited segment of the population use these kinds of tools. That means the other segment of the population ignore the price dispersion and then may not absolutely buy the cheaper product. (2) Then, firms which don’t want to offer low prices can try to attract these people.
In addition, consumers usually base their purchase decisions on information and especially when the purchase has an important value for them. That means that they generally don’t take the time to search information for impulse purchase, routing buying and ordinary goods. (2) That involved that people are not conscious of the price dispersion for this kind of purchases and then the sellers don’t have to compete on prices.

We can then conclude that the shopbots increase the competition between the products. Indeed, it rises price competition for the products which don’t have other advantages perceived by buyers than the price. For the firms which enjoy others advantages than price (time delivery, notoriety, trust,…) , they can compete between them on these advantages. In each case, the shopbots give the information about the price and the other advantages to the buyer. That means that the market products are more competitive because each firm has to provide best advantages than its competitors. Besides, the buyers are not the only ones to be well informed. Indeed, shopbots allow sellers to observe the buyers choice behaviour on these kind of website, which can allow them to review their online competitive strategy.

In general, the online markets are more efficient because price levels, menu costs and price elasticity are respected. (5) The market efficiency is defined by the fact that all prices reflect all available information. (6) By giving all product information’s expected by the consumer’s, the shopbots seem to lead to more transparent markets and to a perfect information situation. Indeed, the buyers can be totally informed when they take their purchase decision. (3) But, we have also to take into account that shopbots allow sellers to pay an additional fee in order to be better positioned on their website which influence the customers’ choices. Then, it reduces the market transparency. Besides, we can think that brands could put different prices for the same product on the online market. Brands are also used to frequently change their online prices in order to avoid competitors to predict it. (7) All these elements lead to conclude that shopbots don’t really improve the market efficiency.

(1) http://revel.unice.fr/eriep/?id=3212#tocto2n1
(2) Passyn, K. et al. (2013). Price Comparison, Price Competition, And The Effects Of ShopBots. Journal of business & economics research. 11(9), 401-415.
(3) Larribeau,S., Penard,T. (2003). Que peut-on dire des stratégies tarifaires sur Internet ? une étude économétrique sur la vente en ligne de CD en France. Système d’information et management. 3, 1-33.
(4) Tang., Z. et al. (2007). The impact on shopbots use on prices and prices dispersion: evidences from online book retailing. Heinz College research.
(5) https://books.google.be/books?id=2w7HH2dvVHgC&pg=PA4&lpg=PA4&dq=market+efficiency+shopbots&source=bl&ots=vjSnMy7FBl&sig=pavUjDQxsRvnkFVD164eogMiL6E&hl=fr&sa=X&ved=0ahUKEwja35S3vILMAhWGmw4KHaSLCfoQ6AEIXTAI#v=onepage&q=market%20efficiency%20shopbots&f=false
(6) http://www.investopedia.com/articles/02/101502.asp
(7) Baye, M. et al. (2004). Temporal price dispersion: Evidence from an online consumer electronics market. Journal of Interactive Marketing, 18 (4), 101-115.

First of all, the comparison shopping sites are become more than a simple tools to compare prices for the customers. It is a real strategic tool for the firms who use it. More and more shopbots play the role of intermediaries and this because they can choose which sellers represented on their sites, which fees they must to pay, who has access to the information and the products, etc (1).

For the customers the advantage is simple, the comparison shopping sites allow mainly to reduce the costs of search and find the products that they want at the best price.

For the firms, there are several reasons to embark on these sites. The shopbots represent a help to develop it sales and attract new consumers who will not come in their official websites (1). In this way, the shopbots create value for the firms but these sites increase also the price competition. Indeed, the firms must to pay fees to be present in the shopbots and if they want to emphasize some products (to sells them faster or make more sales), they must pay more fees. Finally, the firms pay for the customers who visit their products and maybe buy them. In this system, the firms have to adapt their prices to stay competitive and profitable. But the competition resides also between the products. The firms want to propose the best products at the best prices and the large range of product possible, so they don’t propose their entire product but well a special selection of products, chose to maximize their prices and their sales.

The consequence is that the equilibrium of price is not respect with the comparison sites. The firms adapts their prices to the competition, the fees and for maximize their profit compared to others firms. So I think that the market efficiency is not optimal in presence of shopbots.

Source :
(1) Moraga-Gonzalez et Wildebeest (2011). Comparison Sites, online : http://www.tinbergen.nl/~moraga/CompSites.pdf
(2) http://www.ecommercemag.fr/Thematique/marche-prospective-1010/etudes-cibles-10044/Breves/Exclusif-les-comparateurs-de-prix-passes-au-crible-45486.htm
(3) Simth M. (2002). The impact of Shopbots on electronic marketss, online : http://www.heinz.cmu.edu/~mds/isem.pdf
(4) Passyn K, Diriker M., Settle R. (2013). Price comparison, price competition and the effects of shopbots, online : http://www.cluteinstitute.com/ojs/index.php/JBER/article/viewFile/8068/8122

Today, shopbots provide to users to compare different products easier than before. On one hand, they can see what differ the products in term of quality, price, popularity etc. On the other hand, the decision of taking a product rather than other is still difficult. But what about the sellers of the different products? I will argue my comment by giving a different example than in the article, maybe more concrete: the comparison site Bravofly.

First, when we talk about comparison, we can see there is a huge impact on the users’ decision. Indeed, when you are offered a two different prices for the same product, you will choose the one with the lower price and so, it will increase your welfare. On the opposite, it will be quite difficult for the two sellers to distinguish the same product except in term of price. We reach here what the article call the Bertrand paradox. When you take the two same flights services to go to Portugal for example, you will be more incentive to take the one with the lower price. As a matter of fact, with the increasing of the life-costs for example, more and more people try to do some savings. So the competition become to be really though if people are not so well informed, only on prices for example.

On the opposite, we can see that popularity is quite important too. Actually, when you have to take the plane, you can also take into consideration the quality, the reputation the company which you want to travel with but also the departures. We can easily see that huge companies like Brussels Airlines, even if they are more expensive, a majority of people will choose them due to those information shared. So, in a certain way, the competition could be less though if the consumer is well informed about the product or the service.

In term of efficiency, it could go on both way. In my opinion, efficiency could decrease or increase. Actually, it depends on the switching costs. It depends on the time, the money that people are ready to spend to choose, to compare, and to decide which company they will take. If they already have a preferred company like Ryanair and they have chosen them because of their capacity to suggest cheap travel in regard with other companies like Brussels Airlines, the switching costs won’t be huge so the efficiency could increase. But if someone who is used to take Brussels Airlines switch to Ryanair because of external reasons (an increase in life-costs so less money, no more place in the Brussels Airlines’ flight …), his welfare will decrease because he doesn’t find back the features of his regular company (comfort, flight hours …) so the switching costs are quite big even if the money spent is less; it will lead to a decrease in efficiency.

To conclude, the case is quite ambiguous. The competition could increase if the users are not well informed, if the companies try only to focus on the features they are good on (price in most of the case like Ryanair for example). But it could well decrease if the users are well informed, once all the companies have to provide to the users all the information they need like the price, the departures, the time it will be needed to reach the destination …. But it also depends of the rationality of the consumers.

References
http://www.Bravofly.com

I believe it is almost certain that the advent of “Shop Bots” or Ecommerce comparison tools have provided consumers with lower search costs which has resulted in far better pricing and overall market efficiency for commodity type products.

Creating an effective and widely used pricing discovery tool has resulted in greater market efficiency and increased competition for both goods and services. The key to this, however, is in developing the right tool and increasing adoption. In 2002, Economists Brown and Goolsbee found that for every 10% increase in internet usage, online life insurance quotes decreased by 5%. This demonstrates the effect that greater consumer awareness and the elimination of search costs can have on a firm’s ability to price discriminate for commodity prices/services.[1]

In addition, the purveyors of these online shop bots are well aware of the need to continually increase the quality of their platform or risk being displaced by a competitor. This competition can be easily observed in the online travel booking agency where overall agency fees charged to the airlines/hotels have been decreasing due to the low barriers to entry and attractive economics of the industry. This competition among effect is largely ignored by the academic models that suggest Price Comparison Websites (PCWs) lower overall benefits to consumers, although David Ronayne of Warwick University suggests that the presence of intense competition among these price aggregates can eliminate the negative effects associated with charging the producers a referral fee [2]

1)http://revel.unice.fr/eriep/?id=3212#tocto2n1
2)http://www2.warwick.ac.uk/fac/soc/economics/research/workingpapers/2015/twerp_1056b_ronayne.pdf

I totally agree about the perspective of price comparison, because nowadays there are no difference between the products, the only point for customer is the price. the customers will buy the product in the store which has the lowest price. But the problem is there are so many real stores and online stores, it’s hard for the customer to find all the prices of all the stores, especially for the people who didn’t really familiar with internet. The service like shopbot is really important for those customers, and I think this service is good to the customer and retailers. Because the customer will get their ideal price, and the retailer would receive very good feedback from their promotion campaign. Because even there are promotions for the products, but not every customer will notice that. but through the shopbot, the promotion will be noticed by the consumer easily, so i think it’s good for both side.

I think there are some other issues about what make the customer buy, some products seldom decrease prices in all the shops, for example: Apple Macbook, so some customer start to decide which shop to buy on the service after purchase, but not only on prices

On one side you have the “Bertrand paradox” based on full transparency of prices and one the other side; you have the “Diamond paradox” where “all firms will price at the monopoly level and no consumer will search”. A new player comes in the middle of these two models called shopbots; charging sellers to advertise on online price comparison businesses and offering free access to customers. To understand the negative aspects of this model, I have based my research on a study and technology

A study from Jie Zhang & Bing Jing, “The Impacts of Shopbots on Online Consumer Search”, demonstrate an interesting result in which consumers tend to do deeper research on online retailer websites after using shopbots. Therefore, in that case, shopbots reduce customers’ search cost. Based on this study, shopbots don’t increase the competitiveness of product markets and enhance market efficiency but, indirectly, impacts the online retailer websites.

The Baye and Morgan model is maybe losing interest from a customer perspective since the emergence of certain technologies such as access to high speed internet, smartphones and social media. Companies, such as Google, are collecting vast amounts of data, trying to work out how to analyse them into relevant and useful information about customers. Real time marketing could be replacing price comparison website by proposing an individualise and specific product to a customer, cutting down the cost search and killing the shopbot businesses that are useless if product advertising including price information arrives straight on to the customers. Companies have to adapt constantly to gain a competitive advantage and affect the market. For example, Wishabi is a company that has re-invented the circular shopping experience transforming print circular into a dynamic digital experience and use intelligent native advertising to distribute it through a premium media network. Wishabi’s technologies, helps companies deliver targeted content to each and every consumer. This example shows that innovation doesn’t have any boundaries and new models will appear based on old one with a technology twist into it.

references:
http://www.utdallas.edu/~murthi/Papersubs/Zhang_Jing.pdf
http://www.wishabi.com/

While the online shopping bring a lot of convenient to customers, most busy consumers do not have the time or the willingness to visit several websites and compare prices. Shopbots provide the comparing method for the lowest price for same types of products comparatively cost less.

The way for shopbots website to get profits is from “one click” calculation and charging the name showing on the price list. Consumers might think that results are sorted by store rating or best pricing which the results are primarily sorted by retailers who paid for appearing at the top of the list in actual. For online retailers, in order to entry and break exit barriers to show on shopbots website, they have to earn enough gross margins to offset the “one click” cost and advertising expenditures. They add these marketing and advertising cost into products which in some cases online prices are higher than offline price which illustrate the online prices are not uniformly decreasing. Instead of uniformly low prices, online firms are using the Internet to create a market where uniform prices are increasingly rare.
http://neumann.hec.ca/pages/jacques.nantel/notes/nantel/pricing.pdf

Sometimes when the consumers face to a perfect research result, retailers can balance information in their favor and change characters like logistic time and delivery cost. It provides a chance for retailers to get profits from this irrational consumer behaviors.

Price wars would certainly decrease the number of retailers in the long term and thus decrease the profitability of Shopbots. Hence, Shopbots are somehow stuck in the middle: On the one hand, the search process has to deliver some kind of utility to consumers and thereby generate significant web traffic. On the other hand, the utility of the search must not be too high to fuel ruining price wars among the participating retailers.
http://is2.lse.ac.uk/asp/aspecis/20080160.pdf

If we think about the shopbot as a device which endeavours to fulfil a need in the society, then we can draw out some useful insights. One of them is precisely the social value of a shopbot, which is closing the gap between the neoclassical world and our real one through information to dispersed and uninformed consumers rather than omniscient and savvy agents. Nonetheless, this net effect of welfare relies principally upon 1) how well do shopbots attain their function and 2) how much do they distort the agents’ behaviour by changing the rules of game.

The main function of shopbots is to provide information for people so as to make better decisions. Though, a lot of works have just struck sanguine hopes (Waldeck (2005), Lindsey-Mullikin and Grewal (2006)) since there exist obstacles stemming from consumers’ limited access to shopbots, counterattacking strategies of e-tailers and so forth. Passyn et al (2013) points up the best strategies by online retailers are the related ones with brand loyalty but others are also available and unexploited yet. Smith (2002) argues the shopbots are still in an early phase and they can be more valuable whether other features are employed like customisation. Within this tendency, Garfinkel et al ( 2008) call the new complete shopbot as shopbot 2.0 which integrates a recommendation system whereby overcomes with the muddy transparency of e-tailers to show only biased opinions within own sites.

We can conclude the price dispersion will exist in spite of a more and more adoption of shopbots. Therefore the shopbot fails to close completely this gap between Bertrand perspective and ours. Although more elements ought to be added into the analysis like e-tailers’ strategies and better shopbots for attaining an objective opinion. These are providing better features to consumers and thus an enriched variety. The best outcome in economist’s jargon has not been achieved so far, and maybe never will, but we can reach the second best which is the realistic one.

References:
“Imperfect Information: The Persistence of Price Dispersion on the Web” Lindsey-Mullikin and Grewal
“The impact of shopbots on electronic markets” Smith
“Price Comparison, Price Competition, And The Effects Of ShopBots” Passyn, Diriker and Settle.
“Shopbot 2.0: Integrating recommendations and promotions with comparison shopping” Garfinkel, Gopan, Pathak and Yin.

In my view, shopsbots do have an effect on the market.

In fact, I totally agree with the article Price Comparison, Price Competition, And The Effects Of ShopBots (Kirsten A. Passyn, Memo Diriker, Robert B. Settle, Journal of Business & Economics Research – September 2013 Volume 11, Number 9). The authors say that there are no real substantial price discrepancies among online sellers and the differences are more obvious each day because of the increasing number of shopbots. This change in access to information has two major effects.

First, the difference between sellers will become more and more apparent. Thus, the behavior of consumers will be more sensitive to the price. That point leads to more price competition and the discrepancies will decrease. The article says that this decreasing is certain thanks to the increasing number of sources and the gain in experience for the consumers but it will be slow.

Second, even if the number of shopbots and retailers increase, the online shopping suffers from security distrust. That’s why, we observe that the customers continue to buy product on the same website even if a shopbot shows that it’s not the cheapest price. Furthermore, every shop like Fnac has to sell online in order to survive. As the customers can compare the price on the website, they need to lower their price. And, in order to keep the logic, the price in “real shops” has to decrease too.

In conclusion, shopbots increase price competition but it won’t really change the landscape because people keep choosing the dealers in which they trust.

To answer this question we must try to understand how shopbots affect search costs and price dispersion.

Shopbots are intermediaries, which help connect the demand and the supply side of the market. Although Internet and search engines such as Google have greatly lowered the search costs incurred by consumers, we still live in a world of imperfect information. Some users are therefore uninformed while others, thanks to their low search costs, are informed and can beneficiate from that by buying a product at a lower price than the former group. In this environment, how do shopbots influence market efficiency? Since shopbots usually ask for a fee to the retailers while offering access to their website for free to the consumers, they should favor the demand side of the market by letting users pick the best deals. The reality is a bit different, as shopbots need to make a profit. In some cases they will be better off charging a high fee to the retailers, preventing some smaller firms the possibility to join their website. This will in turn create price dispersion, which is beneficial for consumers. This will happen if the online price for a particular homogeneous product is identical across all websites. In this case the market allocation is not efficient because prices exceed marginal costs. On the other hand, when online retailers can charge different prices across various websites, the shopbot will be pushed to accommodate all the players, which would create an efficient market allocation.

Now, because the system of fees that shopbots ask from retailers is a lot more complicated than a simple flat fee, it is difficult to access whether they have increased or lowered the competitiveness of product markets.
One could argue that by letting online retailers buy their way to the top of the list in the shopbots results and exclude some retailers, they have ironically narrowed the buyer’s options rather than widen them. The main users of shopbots are previously uninformed consumers but they are also the most easily swayed. This means that these consumers will only see the products/prices for the retailers that are listed by the shopbots, which has potentially denied access to other firms in order to maximize its own profit as we have seen earlier. In the end those users are only partially more informed and the question is therefore, how big is the percentage of non-listed retailers. Because we assume that uninformed customer will randomly buy from any retailer. The bigger the number of non-listed firms, the less the users of those shopbots are actually being properly informed.

The problem of shopbots is the trust that consumers put in them thinking that they actually do register all retailers without promoting any of them in particular.

But it would be unfair to disregard them as they do inform previously uninformed consumers, even if not giving them all the information available.

In the end, I believe that shopbots increase the competitiveness between big companies while reducing the competitiveness between big and small firms. This might result in a market with a smaller number of online retailers, those which will have been investing in shopbots.
As for “How can such a business model be economically viable?”, it seems that Cost-per-Click is the best way to go as it has proved itself in the more general domain of search engines. As for digital advertising, it is a double edge-sword, as consumers might be reluctant to search for the best prices on a website advertising particular vendors while it is supposed to be objective (or at least that’s what users want to believe).

If shopbots would list all online retailers, we would see an increase in competitiveness. The result would be a market efficiency. As it can be interpreted, I am however doubtful that it is or will be the case.

Shopbots can increase competition in product markets as consumers have access to clear comparison of prices between retailers. Increased information creates “informed consumers” which, in equilibrium, means they will search for the most competitive, usually lowest, price. Retailers in turn, must satisfy consumer preferences and compete with each other to supply their homogeneous goods at the lowest price. According to Baye & Morgan:
‘The level of activity in the market for information directly impacts the competitiveness of the product market, and this in turn affects the willingness of consumers and firms to participate’.
Consumers look for the lowest price, suggesting that demand is infinitely price-elastic. Also, if retailers supply homogeneous goods e.g. in a two-firm economy, Bertrand’s paradox prevails in that consumers will solely base their choice on price comparison, but in fact due to the Nash equilibrium, both firms will set their price equal to marginal cost. For larger economies, temporal price dispersion exists i.e. firms setting randomised prices over time, which induces partial, not full firm participation.
Shopbots can maximise profits when the fees charged for consumer access are set low enough to induce all consumers to subscribe i.e. zero-price provision. It pays off for shopbots to have increased consumer participation as this increases firms’ incentives to attempt to acquire users by offering the lowest price. This in turn implies that, despite the fact that total surplus is maximized by inducing competitive pricing in the product market, the free riding by consumers ensures that this is never profit maximizing for the gatekeeper.
It can be said that intermediary price comparison sites, or shopbots, enhance market efficiency. Efficiency in a market requires prices to reflect all available information. Shopbots provide information to consumers, mostly on a zero-cost basis, thus reducing asymmetries of information and market failure. Consumers benefit as they become “informed consumers”, and can find the lowest price through comparison. Shopbots apparently provide consumers with full transparency of prices, which would enhance market efficiency. If firms know they will be compared to their competitors when signing up for these sites, they may be more willing to let the prices be set by the market and its forces i.e. in perfect competition. However, informational biases can occur which would make the market less efficient e.g. many shopbots can only provide information on the prices of the firms which have paid them to advertise. In that case, even the informed consumers may be less informed than they think they are.
For these websites to be sustainable, they should encourage more users on supply side to reduce the informational bias, inducing competition and efficiency in the market.

Sources: Baye & Morgan, 2001 http://www.rchss.sinica.edu.tw/cibs/pdf/BayeMorgan.pdf
Bertrand’s Paradox, http://en.wikipedia.org/wiki/Bertrand_paradox_(economics)

According to the literature, the advent of the internet should increase customers’ market power on electronic markets. The shopbots development gave many reasons to believe in those tools enabling customers to be better informed of market prices. However, they might not be as fully efficient as we would like to think.

As already explained, shopbots aims at providing to customers as much information as possible in order to compare products sold by different vendors. Thus, it seems logical to think that shopbots will ensure a maximal representation of markets. However, if information bias would be avoided through a full-coverage strategy (1), the latter would also create inefficiency (2). Indeed, information overload would lead to increase search costs to customers and development costs to shopbots. Therefore, in order to better balance their profits with regard to their services, a sample-vendor strategy could be more efficient. This means that asymmetry of information is not solved as customers are not fully informed.
Furthermore, customers will have to ability to quickly compare many product offerings and prices. Nevertheless, information provided by shopbots are mainly quantitative. Thus, the lack of specific product characteristics (e.g. service quality, branding, etc.) might be crucial in the customers’ decision-making process even though they are very price sensitive (3). Indeed, this may lead consumers to offset missing information by making purchases based on previous experience or trust in brands. Therefore, the increase competitiveness in those markets is limited as customers do not only value price.

However, the impact of shopbots may depend on the type of products it is comparing. Homogeneous goods present characteristics that are easily comparable and their purchased are mainly dependent on their price. As Tang et al. (2007) have demonstrated for the online book industry, a 1 percent increase of shopbots usage reduces of $0.41 price levels of books. In this context, it proves the increasing competitiveness on the market due to shopbots. Therefore, it might have a different impact depending on the complexity of products.

(1) Datta, P., & Chatterjee, S. (2008). The economics and psychology of consumer trust in intermediaries in electronic markets: the EM-trust framework. European Journal of Information Systems. Vol 17(1). Pp. 12-28.
(2) Öörni, A. (2003). Consumer search in electronic markets: an experimental analysis of travel services. European Journal of Information Systems. Vol 12(1). Pp. 30-40.
(3) Smith, M.D. (2002). The impact of shopbots on electronic markets. Journal of the Academy of Marketing Science. Vol 30 (4). Pp. 442-450.
Gove, A., & Jianan, W. (2010). How well do shopbots represent online markets? A study of shopbots’ vendor coverage strategy. European Journal of Information Systems. vol 19(1). pp. 257-272.
Tang, Z., Smith, M., & Montgomery, A. (2007). The impact of shopbot use on prices and price dispersion: evidence from online book retailing, Heinz research. vol 47.

It is my belief that shopbots do actually increase the market competition: since the model followed by most of them relies on charging the sellers, and making the web page free for the users, any other uninformed user can become an informed one, free of charge, and within this new surge of information, not only prices play a part on the final and ultimate decision.
The concept of price dispertion will continue to be central to this line of business, since there are many key factors at play: not only is there a demand for the cheapest offer out there, but the ultimate decission on behalf of the user also relies on the confidence it has on the seller, on wether the overall price includes taxes, shipping costs (if there were any) and other factors that will result in choosing an option that might not be the cheapest.
On this line of thought, if a big company were to put a price above average, they would still get great demand b their loyal clients or by users that only rely on this big company. But this also allows for smaller companies to compete for lower prices, and better deals. It might also be the case that, provided every user is informed, some companies really have no competitive advantage on the market and thus will be forced to shut down in light of all the people having the option to choose a better deal.
It is interesting to see as well that, as shown on this paper http://www.utdallas.edu/~murthi/Papersubs/Zhang_Jing.pdf , contrary to common sense, instead of diminishing the amount of searches due to the fact that shopbots show all the main information, the actual result is that “consumers are actually visiting more online retailer web sites after using shopbots”. Why? Because after having selected the best price, or best seller, or best option on the shopbot, most people dig deeper onto the actual seller’s webpage where they will find further details about their product or service needed, and most of them end up buying the latter on the seller’s webpage. This, in turn, means the shopbot will not be able to earn a profit out of the transaction itself. And the other big implication of this result is the fact that the first impression made on the shopbot’s page, is the most important one for the seller, since that will open the door for the user to go into the seller’s webpage and actually be more informed about all their offers. This again will result in an upraisal of market competitiveness.

I believe the most effective way to monetize this business is indeed through the pay per click system,probably adding a plus for every effective sale carried on, but as we’ve stated before,not all sales are actually carried on through the shopbot’s page, and thus the PPC (pay-per-click) system is the solution to this issue. Promoted companies, and ads on these web pages are also additional sources of income that should be set as default for every shopbot.

I personally think that the informations that you can find on a Shopbot is very useful for consumers but can reduce the profit of some sellers.
Indeed, this websites offer the possibility to compare, for a same item, between all the sellers, especially about prices differences.

In my opinion, the Shopbots help the market to be more efficient because the companies have to adapt their prices in a competion market with less “uninformed” consumers than before.
Many consumers are now “informed” because there are no cost to search this informations (thanks to the Shopbots).

I also think that some sellers can place a higher price than an another seller, because of his reputation.
For example, if you are comparing the price for a product and you see a difference of price between two sellers : The first is a well-known seller and the other is a new one.
Even if the price is higher, you’ll probably order on the well-known website because you are afraid to be swindle on Internet.

That’s why, I think that Shopbots make the market more efficient but it will still prices differences. The price dispersion will persist for different reasons (reputation, quality of after sales department, transaction security,…).

About “How can such a business model be economically viable?”, I think that digital advertising is a good source of revenues for a Shopbot, but also the CPC (Cost-per-click). So, it stills free for the consumers and the search costs stay null.

If the sellers want to be seen in the results, he has also to agree to pay for a good ranking in the search results.
I think that this business model is economically viable thanks to this fees.

References :

Kephart J. O., & Greenwald A. R. (2002), Shopbot Economics, Game Theory and Decision Theory in Agent-Based Systems, 119-158.

Markopoulos P., & Kephart J. O., How valuable are Shopbots ?, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.6.5719&rep=rep1&type=pdf.

Brynjolfsson E., & Smith M. D., (2001), The great equalizer? Consumer choice at internet shopbots, Working paper.

Many a clever man has greeted the penetration of e-commerce for its alleged virtues to discipline firms. E-commerce has drastically reduced the perceived distance to suppliers thus lowering the geographic limits of the market. In addition the platform on which e-business relies, i.e. the internet, has rendered relevant market data (product characteristics, prices, guaranties, terms of use, etc.) easily accessible to market actors thus reducing search costs. In a static framework it seems realistic to assume that, other things being equal, increased transparency would help consumers make an informed choice by increasing their awareness of alternative suppliers, reducing lock in to particular firms and thus market power, and facilitating comparison of products, thereby reducing mismatch. These are also the grounds on which consumer protection groups usually advocate for more transparency. In light of the above, price-comparison sites seem like a welcome phenomenon, all the more since they are market-based solutions that didn’t require any sort of consumer rights intervention in order to emerge.

But should they be celebrated, really? In my opinion, not necessarily. This is so for at least two reasons: (1) some aspects of the e-marketplace compromise the disciplining effect of transparency (especially the existence of switching costs and potential security & delivery risks so that consumers are willing to pay a premium for popular brands) or facilitate obfuscation rather than clarification (e.g. price discrimination practices and frequent price-variations are more commonly encountered in e-commerce); (2) increases in price transparency facilitate collusion.

With regards to (1): E-commerce introduces a number of possibilities for switching costs (see Belleflamme and Peitz 2010: 167-168 for a switching costs typology). For instance one will usually need to learn how to use the website-interface (so-called learning costs), and placing an order might require the creation of a client account and the provision of personal information while the whole process might only require “one click” with one’s current supplier (so-called transaction costs). As expected from the above, Johnson, et al. (2003) show that the time required by consumers to complete a transaction falls with repeat purchases. Switching costs are higher still because of uncertainty costs. In e-commerce trust is more important than ever. Actors meet on a virtual marketplace; they cannot see the party with which they are potentially going to engage with and they can’t take a look at the desired product before delivery. A series of scandals revolving around leakages of customer information that resulted in cybertheft have further damaged the image of e-commerce, affecting relatively less known companies more than the big players.

High switching costs of the sort favour settled companies more than entrants because of the asymmetry in customer bases. Incumbents can put a premium on entrants’ prices without losing their customers to entrants for as long as the premium reflects the switching costs (i.e. by adopting a limit-pricing-like strategy). As a result, customers are disposed to continue to patronise their current supplier and at least some degree of market power is retained. Smith, M. and E. Brynjolfsson (2001) find that consumers tend not to buy from the cheapest company listed on price-comparison sites, thus providing empirical evidence for the above argument.

Secondly, firms might want to counter an increase in transparency by artificially reintroducing obscurity (for a formal analysis see Ellison and Wolitzky, 2012; see Ellison and Ellison, 2009 for an empirical study). There are a number of ways by which a firm could make that happen. To name but a few: rendering prices more complex (e.g. through random pricing, frequent revisions of prices, price discrimination, product differentiation, etc.), quoting prices in a way as to sabotage the algorithm’s construction thereby inducing it to produce misleading price-comparison results, etc. As a consequence price-comparison websites might have a hard time producing meaningful comparisons: e.g. are we still comparing the same products or are they only similar and what are the differences? Are the quoted prices including taxes or not?

With regards to (2): price-comparison websites facilitate collusion because they increase price transparency. The validity of the argument is supported by the natural experiment that was accidentally conducted by the Danish Competition Authority in the early 1990s. The authority found that there was a lack of competition in the ready-mixed concrete market. In an attempt to promote competition, the authority launched a price-monitoring programme and prices prompted up by 15-20% (see Albaek et al., 1996). By giving the affected companies access to realtime price monitoring such price-comparison services effectively decrease the period of defection that would go unnoticed and prompt “punishment” (in fact one could design automatic price adjustments that would punish instantly). In addition, firms are no longer required to engage in inter-firm price-information sharing that would otherwise produce factual evidence and thus increase the risk of being caught by antitrust authorities. In effect, transparency stabilises collusion.

Note however that it is not clear to what extent such price comparison sites have increased transparency from a firm’s perspective. In some e-markets it seems perfectly possible to set up a private monitoring algorithm that monitors competitor’s prices. This is especially so with companies which are mostly internet-based, like Amazon, because prices must then by nature be quoted online.

A somewhat related service that has recently come under scrutiny by antitrust authorities (for instance by the OFT in the UK and the Bundeskartellamt in Germany) is the one of online hotel booking portals (like booking.com, expedia, etc.). In both the British and the German investigation, the central problem was that freedom of pricing (on both the hotel- and intermediaries-level) was severely hampered through vertical agreements between hotel chains and booking intermediaries to ensure rate parity thereby stifling competition on the intermediaries’ commissions and between the different booking channels (intermediaries, the hotel’s online, telephone and on-site pricing schemes, all-inclusive packages offered by travel agencies, etc.).

References:
Albaek, Svend, Peter Mollgaard and Per Baltzer Overgaard (1996): ‘Law-assisted collusion? The Transparency Principle in the Danish Competition Act,’ European Competition Law Review, 17, 339-343.
Belleflamme, Paul and Martin Peitz (2010): Industrial Organization: Markets and Strategies, Cambridge University Press.
Ellison, Glenn and Alexander Wolitzky (2012): ‘A search cost model of obfuscation,’ RAND Journal of Economics, 43(3), 417–441.
Ellison, Glenn and Sara Fisher Ellison (2009): ‘Search, Obfuscation, and Price Elasticities on the Internet,’ Econometrica, 77(2), 427-452.
Johnson, E., S. Bellman, and G. Lohse (2003): ‘Cognitive Lock-In and the Power Law of Practice,’ Journal of Marketing, 67(2), 62–75.
Smith, M. and E. Brynjolfsson (2001): ‘Consumer Decisions-Making at an Internet Shopbot: Brand Still Matters,’ Journal of Industrial Economics, 49(4), 541–565.

Shopbot: an empirical evidence: (http://revel.unice.fr/eriep/?id=3212#tocto1n4)

They use data collected from DealTime.com. And they want to analyze the choices made by the consumers when they use on a shopbot. They analyze the consumers’ response from the price, the brand, the shipping etc.

One result is the following: “most customers greatly prefer well-known brand even if the total price is higher”. They trust more the well-known brand, because these brands give more credibility.

Moreover, “customers appear to be more sensitive to changes in sales tax and shipping cost than they are to changes in the item price, even when the total price they must pay remain unaffected” and “consumers who search more intensively are less-price sensitive than other consumers”. So we can expect that the shopbots do not increase the competitiveness of product market due to the fact that consumers are not only interested by the price. So, price dispersion will persist in such kind of market.

We can conclude that the competition in such kind of market is not à la Betrand, because the consumers value other characteristics than the price, so there is no evidence that the shopbots will increase the competitiveness of the market.

Their results lead also to a kind of freemium model: firms tend to attract consumers with a low quality product at a very low price (can be below the marginal cost), and after, the firm try to convince customers to “pay extra to get the product they really wanted in the first place”. With such strategy, it seems that the shopbots may be a barrier to the efficiency.