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How Valuable are Shopbots?
Panos M. MarkopoulosComputer and Information Science Dept.
University of Pennsylvania
Jeffrey O. KephartTJ Watson Research Center
IBM Research
ABSTRACTThe price information that shopbots provide to buyers isclearly valuable, as it enables them to make a better in-formed choice of product and vendor. We quantify the valueof this price information to the buyer in terms of the pricedispersion and the buyer’s brand preferences, and considerscenarios in which the buyer pays a seller, a shopbot, or someother third party for price information. As an illustration,we compute the value of price information of well known re-tailers in online book markets, using data on price dispersionand brand preferences reported by Smith and Brynjolfsson,finding that information about a book’s price can be about6% to 10% as valuable as the book itself.
Keywordsshopbots, brand, price dispersion, information value
1. INTRODUCTIONShopbots—comparison-shopping web sites that collate in-
formation on products from multiple vendors—can be a veryvaluable tool for buyers [1, 8]. Typically, they permit buy-ers to sort product and vendor information along desireddimensions, such as price, delivery time, or vendor reputa-tion. The most sophisticated shopbots even provide person-alized rankings that take into account an individual buyer’sproduct and vendor preferences.
There are two components to the typical business modelemployed by shopbots. Like most Internet information ser-vices, shopbots typically sell advertising space on their web-site. In addition, many shopbots make a commission onsales that result from clickthrough purchases. The mainbeneficiaries of the service—the buyers—pay nothing at all.This is understandable. Today, it would be infeasible tocharge buyers for this service, given the lack of a suitablywidespread micropayment scheme, coupled with the incon-venience to buyers of being forced to deliberate over whetherto pay small amounts of money to get access to product in-formation.
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee.AAMAS’02,July 15-19, 2002, Bologna, Italy.Copyright 2002 ACM 1-58113-480-0/02/0007 ...$5.00.
However, in the future, we envision that buyers will em-ploy economically-motivated agents that will purchase phys-ical goods on behalf of human owners, and that in order tocarry out this task they may purchase information services,such as the product information service offered by shopbots.In such a future, it will be feasible for the shopbot or the sell-ers themselves to charge buyer agents for price information.In order for a buyer agent to know how much it can spendon product information, it must know the value of that in-formation. Shopbots could use such information to governhow long to wait for product information from sellers, or theorder in which sellers are contacted. Finally, sellers of priceinformation (presumably sellers of the physical product, orthe shopbot) could use knowledge of the value of product in-formation to buyers in order to establish a fair price. Thus itis interesting and important to quantify the value of productinformation.
In this paper, our objective is to quantify the value ofproduct information. We do so by means of a simple modelthat captures two of the most common and important di-mensions of concern to buyers: price and brand.
Price information is valuable to the extent that price dis-persion exists in a market. Despite claims that the Inter-net is “frictionless”, significant price dispersion has beenobserved in online markets [4, 7, 3]. Furthermore, brandappears to have a larger influence over purchase decisionsthan other variables, according to Smith and Brynjolfsson[5], who found that even buyers that use shopbots select thecheapest vendor just half the time.
A similar model that took into account only price infor-mation was presented in [10]. That paper mostly consideredgaming issues and showed that product sellers have incen-tives to sell their price information and that their informa-tion price will not be driven down to zero in competitionsettings. Our paper tries to quantify the value of prod-uct information regardless of which market entity (sellersor intermediary) takes advantage of it. By adding brandconsiderations in our model we hope to better model thehuman-shopbot interaction.
In section 2, we introduce a simple model of valuations,taking brand effects into account, and we derive an expres-sion for the value of price information in terms of price dis-persion and brand preferences. In section 3, we calibrateour model using data on online bookstore price levels andbuyer preferences, as reported by Smith and Brynjolfsson[4].Next, in section 4, we quantify the value of price informationto buyers under two different assumptions about how muchprice information is already known to the buyers. Then, in
section 5, we look at the situation from the perspective ofan agent that sells the price information to the buyer agent,asking how much that agent (presumably representing theshopbot or the seller of the physical good) could charge thebuyer for price information. We make some general com-ments about markets for product information in section 6,and summarize our findings in section 7.
2. OUR MODELWe consider a market of N sellers, each offering a wide
range of products. A buyer queries a shopbot for a productchosen at random between all possible products. The shop-bot displays the price that each individual seller charges forthe product and the buyer determines the utility that shewould obtain from any individual offer. The utility is equalto U = v − pi + bi, i ∈ 1..N , where v is the value of theproduct to the consumer, pi is the price of seller i and bi isthe value the buyer receives from buying from seller i. bi canalso be interpreted as the degree of the buyer’s belief thatshe will receive satisfactory and timely service from seller i.
We assume that v is an information private to the buyer.All buyers are assumed to have identical bi’s. These areknown to the sellers and a common knowledge in the market.
In order to calculate the marginal value of the informationregarding what a particular seller charges for the product, weconsider what would happen if seller i did not provide priceinformation but only the fact that he carries the particularproduct that the buyer requested. In other words all sellersprovide the buyer with their price information while selleri does not. We assume that the buyer believes that pi isdistributed according to the distribution with PDF fi(x),particular to seller i. This distribution represents the buyer’sbeliefs about the overall price levels of seller i. This beliefcould be formed by previous information obtained on otherproducts of seller i, or if the information is not available, thebelief can be formed with the help of the shopbot that canrate sellers according to their overall price levels1. Again,fi(x) is common knowledge.
The buyer has compared across all sellers S that provideprice information for free and found seller m ∈ S that max-imizes her utility. Let um be the utility obtained from pur-chasing seller m’s product. Let ui be the expected utilityfrom purchasing the best product after having learned i’sprice as well. The marginal utility from knowing i’s price isthus ui − um. Notice that ui − um ≥ 0, because the buyerwould only purchase i’s product if her utility would increasefrom um by doing so.
If we denote the utility the buyer obtains from purchasingi’s product as ui, then ui > 0 for ui > um ⇔ pi < bi− bm +pm. So
ui − um =
Z bi−bm+pm
0
fi(pi)(ui − um)dpi =
1The StreetPrices.com shopbot displays graphically theprice range for a particular product, even before the userhas access to actual prices. A potential buyer can thus ob-tain a good idea of the price dispersion for the product.Other websites, such as BizRate.com will provide informa-tion on the overall price levels of a particular seller. It isthus feasible for a buyer to form reasonable expectations forthe price levels of a particular seller on a particular producteven without past experience.
Retailers at a ShopbotRetailer Proportion of Shopbot
Lowest Prices Market ShareKingBooks: 29.7% 18.6%A1Books: 24.6% 19.0%Borders: 19.3% 20.7%
1Bookstreet: 16.3% 11.2%BN: 6.1% 14.1%
Amazon 3.9% 16.3%
Table 1: Percentage of times each bookstore carriedthe cheapest item, including all costs, among the sixbookstores, as measured by Brynjolfsson and Smith.
=
Z bi−bm+pm
0
fi(pi)(bi − bm + pm − pi)dpi
which after additional algebraic manipulations becomesZ bi−bm+pm
0
Fi(pi)dpi,
where F (x) is the CDF of f(x). So,
ui − um =
Z bi−bm+pm
0
Fi(x)dx (1)
Given that the buyer can observe the price information ofa set of sellers S, we will be denoting the utility that thebuyer obtains from acquiring seller i’s price information asu(i|S).
3. INTERNET BOOKSTORESIn this section we calibrate our model with real price data
about six online bookstores.In order to estimate a reasonable approximation to the
fi distributions that measure consumers’ uncertainty aboutthe price of a certain book at bookstore i, we make two sim-plifying assumptions: fi’s are normal distributions and haveidentical standard deviations, fi = N(mi, σ). The identicalstandard deviation assumption translates to the belief thatconsumers have no reason to be more or less certain aboutany bookstore’s price, before they discover it. Consumersmight view bookstores as likely to be “cheap” or “expen-sive” and might have an expected “mean” for the price theyare about to discover. We assume however that they havethe same uncertainty, across all bookstores, about how farthe actual price is from that mean.
We compute the exact form of the fi’s from data providedin [5] on the percentage of times a bookstore carried thecheapest book, as shown in table 1. We seek to computesix means and one standard deviation and thus we need asystem of seven equations.
The data from table 1 give rise to five equations (the sixthis a linear combination of the other five). For example, theequation for Amazon is:Z ∞
0
f1(x)Π6i=2(1− Fi(x))dx = 0.039 (2)
We obtain a sixth equation by requiring that the averagebook price (including all costs) is $13.69, as measured in[4]. The seventh equation is obtained by requiring that sixprice quotes display a standard deviation equal to 2.0, a fitto the standard deviation of the price dispersion measuredby Brynjolfsson and Smith in [4].
$7.5 $10 $12.5 $15 $17.5 $20
0.05
0.1
0.15
0.2
AmazonBN1BookstreetBordersA1BooksKingbooks
Figure 1: Our model of consumers’ uncertaintyabout book prices in six major Internet bookstores.We assume that the consumers search for a bookwhose average price is equal to the average bookprice on the Internet. The parameters for the as-sumed normal distributions are mAmazon = 15.01,mBN = 14.58, mBorders = 13.30, mA1Books = 12.99,mKingBooks = 12.74, m1Bookstreet = 13.51, σ = 1.82
Amazon
BN1Bookstreet
Borders
A1BooksKingbooks
$7.5 $10 $12.5 $15 $17.5 $20
0.05
0.1
0.15
0.2
Figure 2: Consumers’ uncertainty including brandeffects for the six major Internet bookstores. Theparameters for the assumed normal distributionsare mAmazon = 13.17, mBN = 13.86, mBorders = 12.58,mA1Books = 12.99, mKingBooks = 12.74, m1Bookstreet =13.51, σ = 1.82
The solution of the system is displayed in figure 1.Our next task is to account for the brand effect. We would
like to know how much cheaper a book should be, to accountfor the brand effect in a similar book offered by a brandedretailer. According to Brynjolfsson and Smith, Amazon,BN and Borders can charge on average $1.85, $0.72 and$0.72 respectively more than an unbranded retailer to makethe buyer indifferent between their book offers. We will bereferring to Amazon, BN and Borders as “branded” retailersfor the rest of the paper and the rest of the bookstores as“unbranded”.
From equation 1, we can see that this can be modeled asa left “shift” of a branded bookstore’s price distribution (ac-cording to buyer’s uncertainty), by this amount, as shownin figure 2. The average buyer that has the rational ex-pectations as shown in figure 1 and values brand accordingto the Brynjolfsson and Smith findings, can be modeled asa price sensitive buyer that has price expectations accord-ing to figure 2. For the remaining of this paper we accountfor brand effects in this fashion: We have shifted the pricedistributions of the branded sellers to the left and considerprice sensitive buyers over the updated price distributionsthat we continue to denote by fi and Fi for their PDF andCDF respectively.
The predictive power of our model is somewhat limited,
Rank Naive Predicted by Actualour model
1 KingBooks: 29.7% Borders: 24.8% Borders: 20.7%2 A1Books: 24.6% Kingbooks: 22.0% A1Books: 19.0%3 Borders: 19.3% A1Books: 17.9% Kingbooks: 18.6%4 1Bookstreet: 16.3% Amazon: 15.5% Amazon: 16.3%5 BN: 6.1% 1Bookstreet: 11.5% BN: 14.1%6 Amazon 3.9% BN: 8.3% 1Bookstreet: 11.2%
Table 2: Shopbot market share as a percentage ofbook sales among the six bookstores
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
Borders KingBooks A1Books Amazon 1Bookstreet BN
predicted by ourmodelnaive
Figure 3: Reality check: comparing the absolute er-rors of our model and the naive approach, that saysthat buyers buy at shopbots only based on price.
as shown in table 2 and figure 3, but still a significant im-provement over the “naive” assumption that shopbot usersprefer the cheapest product, which, according to [5] is trueonly half the times. Differences from the actual data ex-ist due to effects that are not captured by our model, suchas different buyer sensitivity to different price components2,repeated sales effects, special promotions etc.
4. INFORMATION VALUE TO BUYERSThe price information of each of the six bookstores in our
study has definite value to the shopbot user. Each additionalprice quote increases the buyer’s chances of finding a moredesirable product. It is obvious that the value of informationto the buyer depends on what the buyer already knows.Again, S is the set of sellers whose price information thebuyer can observe for free and the marginal value to thebuyer of the price information of seller i is denoted by u(i|S).If i ∈ S then it is obvious that u(i|S) = 0, since the buyeralready knows seller i’s price.
We proceed to calculate the marginal value at two limits:
• S consists of one seller j other than i.
• S consists of all sellers other than i.
In the case where S consists of only one seller j, the av-erage value of seller i’s price information is given by:
u(i|j) =
Z ∞
0
fj(x)
�Z x
0
Fi(z)dz
�dx (3)
where, again, fj(x) is the PDF of seller j’s product priceand Fi(x) is the CDF of seller i’s product price. The innerintegral is the expected increase of the buyer’s utility if he
2According to Brynjolfsson and Smith buyers express thepeculiar behavior of being much more sensitive to mail coststhan the actual book price. The buyer’s decision is thusinfluenced not only by the total book cost, but also by theway that this cost is constructed. We have not attemptedto model this behavior in our paper.
Bord
ersKing
Book
sA1B
ooks
Amazon
1Boo
kstre
et
BN
BordersKingBooks
A1BooksAmazon
1BookstreetBN
$0.00
$0.20
$0.40
$0.60
$0.80
$1.00
$1.20
$1.40
$1.60
$1.80
unknown i
known j
Figure 4: Average value of the price information ofbookstore i, given that we know only of bookstorej’s price
can observe seller i’s price, given that he already knows thatseller j charges x for his product (see also equation 1).
We have plotted u(i|j) in figure 4. The figure depicts theaverage value of the price information of seller i (unknown),given that we have already observed only seller j’s (known)price. The buyer will experience the minimum average in-crease in utility (approximately 40 cents) if she discoversBN.com book price, after she has observed Border’s price.The opposite case, where the buyer wishes to learn Border’sprice, after she has observed BN.com price yields a max-imum expected increase in utility, equal to approximately$1.80.
In the case where S consists of all sellers other than i, weproceed to calculate the marginal value for each of the sixbookstores, by considering what happens if the price infor-mation of a particular seller is not included in the shopbot.For each of the six bookstores we ask the following ques-tion: “Given that the buyer can observe the price data ofthe other S bookstores, how much does the buyer value theability to know that bookstore’s price information as well?”or in other words: “How much would a shopbot user bewilling to pay to have a bookstore’s price information in-cluded in the shopbot, given that she can observe the priceinformation of the others.”
First, we note that given that the price of seller i cannotbe observed, the minimum price among the remaining Ssellers follows a distribution with CDF:
F minS (x) = 1−Πj∈S(1− Fj(x)) (4)
with the corresponding PDF fminS (x) =
dF minS (x)
dxdenot-
ing the probability that a certain x will be the minimumprice observed among the remaining five sellers.
So the expected marginal utility to the buyer of havingseller i’s price information included in the shopbot data isthus:
u(i|S) =
Z ∞
0
fminS (x)
�Z x
0
Fi(z)dz
�dx (5)
We plot u(i|S) in figure 5. In the figure, we consider
2 4 6 8 10
$0.05
$0.1
$0.15
$0.2
$0.25
$0.3
Price Information Value
Additional Sellers
BordersKingBooks & othersA1BooksAmazon1BookstreetBN
Figure 5: Buyer’s marginal utility for having eachof the bookstores’ price included in the shopbot’sdata (assuming that the shopbot already includesall other bookstores’ information) as a function ofthe number of additional bookstores in the market,in addition to the original six. Additional book-stores are assumed to be “unbranded” and identicalto KingBooks in overall price levels.
the effect of varying the number of sellers included in S,by adding cheap, “unbranded” sellers in the hypotheticalmarket comprised of the six bookstores of our study. Inparticular we add bookstores with overall price levels iden-tical to Kingbooks.com, the bookstore with the lowest pricelevels, according to the Brynjolfsson and Smith report.
Intuitively, the greater the number of sellers participat-ing in a market, the smaller impact each one of them canhave on the consumer’s purchasing decisions. In figure 5,we see that indeed competition reduces the marginal valuethe buyer receives from each additional price quote. For ex-ample, the marginal value the buyer receives from knowingBorder’s information drops from more than 30 cents for theoriginal market of the six bookstores to less than 10 centsfor a market of sixteen bookstores, or more than a threefolddecrease.
5. THE INFORMATION SELLERS’PERSPECTIVE
This section explores the information sellers’ perspective(infoseller) on the value that their price information car-ries. We will see that the infoseller’s perspective leads toconsiderable higher values for the price information. Theinfoseller could be the actual product seller, the shopbot orsome other entity. It is perhaps more realistic to expect thatinformation would be sold by the shopbot, on behalf of theseller, with the revenues divided between the two.
The infoseller has a different viewpoint, because of the“multiplicative effect” of price information: the value of prod-uct price information is utilized more often than the value ofthe product itself. If we view the buyer’s marginal utility forthe seller’s price information as the amount of money a ra-tional buyer would be willing to pay to have the seller’s infor-mation included in the shopbot list, then an infoseller wouldsell his price information more often than the actual productis sold, as buyers would require multiple price quotes for asingle purchase.
While price information is always sold if it is priced be-low its marginal value, an actual product is only sold whenthe seller carries the most attractive choice for the shopbot
2 4 6 8 10
$0.2
$0.4
$0.6
$0.8
$1
$1.2Value perProduct Sold
Additional Sellers
BordersKingBooks & othersA1BooksAmazon1BookstreetBN
Figure 6: Price information value per book sold, asa function of additional bookstores in the market.
2 4 6 8 10
2 %
4 %
6 %
8 %
10 %Information Valueas a Percentage ofProduct Revenue
Additional Sellers
Borders, KingBooks & others
A1Books1BookstreetAmazonBN
Figure 7: Price information value as a percentage ofthe bookstore’s revenues from product sales.
users which will only happen a fraction of the times buyersenter the market, depending on the particular item a buyeris looking for and the current market prices for that item.So, the value of seller i’s information per product that theseller sells is uproduct(i|S) = u(i|S)/plow(i), where plow(i)is the probability that seller i carries the most attractiveprice/brand combination for the item a buyer is interestedin. For plow(i) we use the seller’s predicted market shareamong shopbot users, from table 2, and plot uproduct(i|S)in figure 6.
It is noteworthy that, even if the value of price informa-tion for each seller diminishes, from the buyer’s perspective,as we add more and more bookstores in the market (seefigure 5), the relative importance of the same value for theinfosellers is impressively stable, due mainly to the “multi-plicative effect”. In figure 6 we can see that the utilized valueof price information per product sold reduces by 20%-30%for ten additional “cheap” competitors and almost stabilizesbeyond that number.
We can also have a first estimate of the relative sizes ofthe shopbots book market and a hypothetical “product in-formation market”: a market trading book price informa-tion. In figure 7 we depict the information value that eachbookstore generates, as a percentage of the bookstore’s rev-enues from selling books. To calculate this figure we use theoverall bookstore’s price levels, as calculated in figure 1 andthe probability of a buyer choosing the specific bookstore.Another way to view figure 7 is as the ratio of book infor-mation value to book price for each bookstore. We see thatthe price information value as a percentage of the book’s fullprice ranges between 6% and 10%.
The infosellers would like to know how much they couldtheoretically charge a shopbot user for their price informa-tion to be included in the shopbot’s results. Ideally, they
2 4 6 8 10
2 %
4 %
6 %
8 %
10% Information Valueas a Percentage ofProduct Revenue
Additional Sellers
KingBooks & others
1BookstreetA1BooksBorders
BNAmazon
Figure 8: Book information value per one dollar ofbook price for an alternative search cost of 40 cents.
would be able to charge the marginal value of their infor-mation to the potential buyer. However, this is not alwayspossible. Figures 6 and 7 refer to the maximum revenue thatan infoseller could possibly extract from the buyer. This isof course an upper bound that probably cannot be realized.
The most important reason is that the buyer will usuallybe able to access the seller’s price information through othermeans. We introduce an alternative search cost c that thebuyer can incur and learn seller i’s product price. This costrepresents the time and effort of visiting the seller’s web sitedirectly and the added inconvenience of not having this in-formation as part of the shopbot’s comparison shopping en-vironment that facilitates the buyer’s decision making pro-cess. This cost can range from a few cents to infinity, in thecase that a seller refuses to provide any price information,even at his website. The infoseller cannot charge the buyermore than c, as in that case the buyer would choose to incurthe cost c and learn i’s price. From equation 5, the averagemaximum price that the infoseller can hope to charge thebuyer for price information is:
infopricei(c) =
Z ∞
0
fminS (x)min
�c,
Z x
0
Fi(z)dz
�dx (6)
For example, for an alternative search cost c equal to 40cents, figure 7 would change to figure 8. In figure 8 wesee that the price information value as a percentage of thebook’s full price ranges between 5.5% and 8%, down from6%-10% in figure 7. It is also worth noting that this value isnot necessarily monotonically decreasing in the number ofadditional product sellers and for some bookstores peaks ina market of nine competitors (the original six plus three).
It is natural that the value that an infoseller can demandfrom a buyer for his price information decreases as the al-ternative cost that the buyer can incur and access this in-formation through other means becomes lower. We haveplotted equation 6 in figure 9, where S represents all otherbookstores in the set of six bookstores that we study.
We observe that the maximum value an infoseller can ex-tract is in accordance with the seller’s popularity among theshopbot users. The buyers would value Border’s price infor-mation more than any other bookstore. We further observethat the value of information is very sensitive to the alter-native search cost, c, for small values of c, while it almoststabilizes for c > $1. For three of the bookstores in our studythe value of information stabilizes for even lower values ofc, at around 40 cents.
The multiplicative effect can be seen in figures 10 and 11,where we plot the average maximum value that an infoseller
$0.2 $0.4 $0.6 $0.8 $1 $1.2
AlternativeSearch Cost
$0.05
$0.1
$0.15
$0.2
$0.25
$0.3Price Information
Value Borders
KingBooks
A1Books
Amazon
1Bookstreet
BN
Figure 9: Average maximum value of the price in-formation of the six bookstores, assuming that theshopbot displays for free the information of theother five, as a function of the buyer’s alternativesearch cost c
$0.2 $0.4 $0.6 $0.8 $1 $1.2
$0.2
$0.4
$0.6
$0.8
$1
$1.2BordersKingBooksA1BooksAmazon1BookstreetBN
AlternativeSearch Cost
Value perProductSold
Figure 10: Value generated by the price informationof a bookstore per book sold, as a function of thealternative search cost c. Alternatively, the valuethat the price information of a book generates toshopbot users, before the actual book is sold.
can extract from a buyer, per product that a seller sells andas a percentage of a seller’s product revenue, respectively.
We observe in figure 11 that “unbranded”, cheap retail-ers gain a comparative advantage in a market for productinformation: their information is relatively valuable, whiletheir product revenues are relatively low, as a consequenceof their preference towards price competition. The valueratios vary starting from 6% for BN.com to 10% for King-Books3, showing that sellers that compete mainly based onprice would have greater incentives to participate in such amarket for information. We also see that the value ratios arehigh for Borders which even though it enjoys brand power,maintains low overall price levels.
Finally, it should be noted that the values calculated inthis section assume that the infoseller is allowed to chargedifferent information prices for different books that a book-store carries. Pricing is done so that it is always marginallyoptimal for the buyer to purchase the price quote. If thisis not possible and a fixed price for the price quotes of abookstore is necessary, the expected revenues from price in-formation deteriorate as can be seen in figure 12 for the caseof Amazon. This happens because, now, it is not certain
3for sufficiently high alternative search costs.
$0.2 $0.4 $0.6 $0.8 $1 $1.2
2 %
4 %
6 %
8 %
InformationValue as aPercentage of Product Revenue
AlternativeSearch Cost
AmazonBN
BordersA1Books
KingBooks
1BookStreet
Figure 11: Value that the price information of abookstore generates for shopbot users, as a percent-age of the bookstore’s revenues from product sales.
that the price quote of the seller will always be requested.The result is a slightly higher price quote price and a lowerexpected information revenue per buyer. In addition to re-duced information revenues in the fixed price case, the sellernow faces the possibility of losing a product sale he couldotherwise have made, since it is possible that his price quotewill not be requested and the buyer will not get informed ofthe bookstore’s offer.
To calculate the optimal fixed price used in figure 12,we proceed as follows: We denote gi|S(q) the function thatmaps a minimum discovered price q, among S sellers, to themarginal utility a buyer gains from obtaining seller i’s priceinformation. From equation 1:
gi|S(q) =
Z q
0
Fi(x)dx
Let g−1i|S be the inverse of gi|S , in other words, g−1
i|S maps a
price quote price to a product price. If q is the minimumproduct price among the S sellers, an infoseller that chargesx for seller i’s price information would have an expectedrevenue:
Ri(x) = x · Pr(g−1i|S(x) < q),
where Pr(g−1i|S(x) < q) is the probability that g−1
i|S(x) is less
than q. This is simply:
Ri(x) = x ·�1− F min
S
�g−1
i|S(x)��
(7)
and the first order conditions give the optimal fixed pricequote price:
R′i(x) = 0 (8)
It is evident that if the infoseller is the actual productseller, this pricing scheme should be avoided as it can lead toa potential loss of revenue from actual product sales. How-ever if the infoseller is a shopbot, the fixed price scheme canbe used to simplify the buyer’s selection process.
6. MARKETS FOR PRODUCT INFORMA-TION
Product information has definite value for the rationalconsumer. More information always increases the chancesthat the buyer will find a more suitable product. Consumersseem to value products in a variety of ways, using differentmeasures, personal tastes and exhibiting loyalty and lock-in
$0
$0.05
$0.10
$0.15
$0.20
$0.25
0 2 4 6 8 10
Average priceand expectedprofits per buyerwith variable price
Fixed price
Expected profitsper buyer withfixed price
Additional Sellers
Figure 12: Comparison of price quote prices forAmazon for the case where a different price quoteprice is charged per book, versus a fixed price quoteprice for all books. The values are given as functionof additional sellers, in addition to the original six,entering the market. An infinite alternative searchcost is assumed, in other words the buyer agent can-not obtain price information unless it pays for it.
behavior for the sellers they patronize. This complex spaceof consumer behavior has given rise to considerable pricedispersion even in market for products that most researcherswould categorize as commodities, such as books and CDs.
Searching in that product space is no easy task for therational consumer that seeks to maximize a “personalized”concept of utility. Shopbots and price comparison web sitesfacilitate this search but seem to fall short of offering thewide variety of information different prospective buyers mightbe interested in. For example, special seller offers or guar-anties are usually not included in price comparison web sitesthat tend to provide more structured information, capturingmore “popular” important product attributes, such as priceor delivery time.
Arguably the cost of maintaining extra product informa-tion dimensions exceeds the shopbot’s utility from providingthem; it is indeed expensive for a shopbot to keep track of alldifferent ways a seller chooses to present product informa-tion both online and offline and reprogram the informationgathering software every time a change takes place. It isthus hardly surprising that only a bare minimum of productdimensions is reported in shopbots’ websites.
Furthermore, most shopbot websites are in reality inter-mediaries, whose incentives do not always match with theconsumer’s desire of finding a best product at the best pos-sible price. Special shopbot-seller deals are quite common asshopbots seek ways to increase revenue, often at the expenseof information transparency [6].
Markets for product information can provide a solution tothese problems, as sellers and shopbots would have incen-tives to provide a wider variety of product information in atimelier manner. In such a market a buyer would be buyingproduct information bundles, based on the expected utilitythat the information would bring. Buyers will benefit frombeing able to use their own “personalized” concept of utilityto make a better decision based on more information, butthat will come at a cost, since information will no longer befree. Sellers and/or shopbots would want to sell this infor-mation as close to its marginal utility (as calculated in this
paper) as possible. Their revenues would then be directlytied to the quality and quantity of information they provideleading to higher information transparency. This is a majoradvantage product information markets would have againstother shopbot revenue models, such as the sales commissionor the subscription based methods.
In a sense a market for product information would be op-erating in parallel with the product market that it supports,an idea used in [9] and [2]. Prospective buyers interested inparticipating in the product market would have the optionof using the information market first, as a guide, if they be-lieve that the market information costs would be lower thanthe potential savings.
It is, however, unreasonable to expect that humans wouldbother to micro-manage their information purchases andprocess large amounts of product data themselves. Thisis a job better suited for economically-motivated softwareagents that represent their human owners. Software agentswould be the key players in product information markets.
This paper gave an estimate of the size of a hypotheticalbook information market compared to the size of the bookmarket itself. We computed that an information market canbe 6%-10% of the size of the product market, or, rather theportion of the product market that is serviced by shopbots.However, since it is arguably far less costly for the seller tosell information rather than products, we would expect thesellers’ profits in each of the markets to be of comparablesize.
In a hypothetical product information market, productinformation would be sold as a bundle that includes multi-ple product dimensions, such as price, quality, delivery timeand special offers. This paper is the first one to account formore than one product dimension in the valuation of prod-uct information: we have considered the effect of price andbrand on product information value.
We do not claim that the Internet book industry is moreor less likely than other industries to give rise to a mar-ket for product information. Our purpose was to providean estimate of the size of a prospective information marketbased on industry data and the online book industry pro-vided an excellent opportunity due to the Brynjolfsson andSmith reports.
7. CONCLUSIONSWe envision a future where economically-motivated soft-
ware agents will be employed by humans to simplify theirpurchases of physical goods. In such a future it will be fea-sible for the product seller or an intermediary (shopbot) tocharge buyer agents for price information. In this paperwe were able to quantify the value of product informationby means of a simple model that captures two importantproduct dimensions, price and brand, using data from theBrynjolfsson and Smith reports.
We have separated the buyer’s and the information seller’sview of the problem and demonstrated that due to the “mul-tiplicative effect” of information, product information valuecan be as high as 10% of the product cost itself, in a marketcomprised of real online bookstores.
Furthermore, we have discussed the possibility of the emer-gence of product information markets which would providea new business model for the shopbots’ operation. Productinformation markets would contribute to higher informationtransparency as sellers would have the incentives to provide
better, more accurate information in a timelier manner.In future research we intend to extend the ideas presented
in this paper and construct models for product informa-tion markets in order to better understand their interactionwith the product markets they support. Our goal is to pro-pose new business models for the operation of shopbots thatwould improve the efficiency and information transparencyof the markets they support.
8. ACKNOWLEDGEMENTSThe authors would like to thank Professor Lyle H. Ungar
for his valuable comments and suggestions.
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