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Are prices lower on the internet? Not always!
Are prices lower on the Internet? Not always!
James V. Koch Board of Visitors Professor of Economics, Old Dominion University, Norfolk, Virginia
I
t is almost an article of faith among some that people who use the Internet pay lower prices for the goods and services they buy. “Lower” here means prices less than they would pay for the same goods or services at a bricks-and-mortar store. The everyday experiences of many Net users provide informal support for this notion, and there is some rigorous empirical evidence in favor of it, although the Bureau of Labor Statistics has yet to publish significant comparisons between Net prices and non-Net prices. The most comprehensive, fresh evidence available (produced by Lehman Brothers) indicates that most Net prices are lower than those charged by bricks-and-mortar firms. Even after taking shipping costs into account, Net prices were 38 percent lower for apparel items; 28 percent lower for prescription drugs, alcohol, and cigarettes; 4 percent lower for home electronics and groceries; 2 percent higher for hardware; and 9 percent higher for toys. Overall, report Harris and Abate (2000), the Net prices of 93 different items were 13 percent lower than bricks-and-mortar prices, shipping included. Koch and Cebula (2002) provide an extensive summary of this and other evidence.
Web. But reality is different. The
Thus, many consumers often do pay less when they use the Net. Nevertheless, a surprisingly large set of circumstances exists in which Net users end up paying higher prices for goods and services. Here we examine seven of these circumstances.
Net is not always the shopper’s
A bit of theoretical background
paradise they may think. Data mining,
The more accurate the information users have about the goods and services they might buy, the less they are apt to pay. If consumer Net searches generate more and better information for consumers, then the Net is likely to reduce price. Such searches have the potential to cut transaction costs and make it cheaper to evaluate goods and services and ascertain prices. Consequently, the Net can increase the efficiency of markets and thus assist both buyers and sellers.
Most consumers believe they pay lower prices by using the
high levels of product branding, auctions, consumer use of shopping robots, persistent consumer use of portals, deceptively high delivery costs, and product bundling can all cause consumers to pay more than they would in a conventional bricks-and-mortar store. Net shoppers should beware these seven traps.
Even so, if goods and services are differentiated and consumers develop brand loyalties, then consumers might 47
well pay higher prices, even on the Net. Some individuals will pay a premium to purchase items sold by familiar, reliable sellers. Situations also arise in which buyers and sellers have different (asymmetric) information, which can result in higher prices. In particular, sellers may know something about their goods or services that buyers do not. The market for “lemons” in used cars is a well-known example, but the same asymmetric information phenomenon is often present in many Net auctions and where “experience” goods are involved that require consumers to actually use or sample items in order to know if they will be satisfying. It is not surprising in such situations when Net users end up paying more. The widely held notion that the Net results in lower prices actually applies to reasonably specific theoretical circumstances involving relatively homogeneous “shopping” goods when information is symmetric and transaction costs (including the value of the surfer’s time) are low. Reality may be different. Just what these qualifications are will be demonstrated here.
Situation 1: Data mining
D
ata mining—euphoniously described as “customer relationship management,” or CRM, by many firms—describes the ubiquitous practice of Net sellers collecting extensive information about consumers. Such information may range from customers’ addresses and incomes to a complete history of their surfing behavior. “For nearly a decade,” reports Gaither (2001), “corporations have been using increasingly powerful databases to collect and store huge amounts of information about their customers and their practices.” The digital cookies and pixel tags sent to the hard disks of computer users by Net sellers ranging from Amazon to Enterprise Rent-A-Car constitute especially fruitful and frequently unnoticed means for firms to collect such data, though demonstrably many consumers willingly supply such information. The truth is, Web surfing is seldom anonymous. It is as if users have an electronic bar code associated with their every Net action that is instantaneously scanned every time they make a move. Such snooping might inspire a police investigation in the real world, but it is both commonplace and legal on the Net. Very few laws constrain data mining on the Net and, according to Tedeschi (2002), sellers now have the ability “to analyze data about sales and customer browsing patterns and respond quickly with offers.” Hence, whatever means companies use to acquire data, they use that information to tailor the advertising Web users see and the prices quoted. The Net provides an inexpensive and almost instantaneous means for firms to test the price sensitivity of e-consumers. For example, a Net 48
seller might quote every tenth or twentieth visitor a higher price, then observe the results. The next day, or even the next hour, it could try a different experiment. Such experimentation would be difficult and very time-consuming for a bricks-and-mortar firm to pursue. As a consequence, data mining provides Net sellers with new ways to raise prices. According to Baker et al. (2001), Tickets.com earns up to 45 percent more sales revenue selling tickets on the Web than it does in person. Moreover, electronic components typically generate 33 percent more revenue on the Net, high-end automobiles 25 percent more, and video games 17 percent more. Memorabilia sold on the Net sell for an amazing 400 to 500 percent more. Meanwhile, reports Tedeschi (2001), Amazon has used Net data mining to offer prospective customers advertising and prices that have enabled it to produce a conversion rate (the percentage of its Web site visitors who actually become buyers) three times the industry average. Whereas the customer who walks into a bricks-and-mortar store is often a “statistical mystery” without a known history, state Baker et al., such is not the case on the Net. E-companies can almost instantaneously categorize and segment prospective customers based on data mining information and then tailor their prices and terms accordingly. Economically speaking, Tickets.com and other firms operating on the Net use data mining to gain a strong sense of the supply and demand situation in their markets, make rough-and-ready estimates of the price and income elasticities of demand of their customers, then base their
The truth is, Web surfing is seldom anonymous. It is as if users have an electronic bar code associated with their every Net action that is instantaneously scanned every time they make a move. prices on those estimates. Frequently the models underpinning these activities are highly sophisticated. Airlines have long practiced “yield management” to maximize the revenue they receive from specific air flights; now this practice has spread to mainline retailers such as Macy’s, J.C. Penney, L.L. Bean, and Shopko. Based on highly dynamic pricing, it typically results in different consumers paying different prices for the same items, especially when perishable items such as air tickets and hotel rooms are Business Horizons / January-February 2003
involved. On the Net, prices can be changed hundreds of times a day. Moreover, because most Web transactions are private, one consumer does not know what price the other has paid, which gives e-sellers the room to raise prices selectively.
Situation 2: Product differentiation and branding
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elatively few generic, essentially homogeneous goods and services exist. For every generic microcomputer sold over the Web, hundreds more highly identifiable Dell, Compaq, and Gateway computers are purchased. Net sellers may differentiate their products by means of advertising (“Dude, you’re gettin’ a Dell!”) and distinctive packaging and labeling (witness Gateway’s spotted cow), which lead to the conscious branding of the product in consumers’ eyes. But they can also brand their products in other ways: by manipulating the terms of sale, including guarantees, warranties, credit, service, giveaways, and bundling; by creating the ambience connected to the sale; by controlling the time of the sale; by offering multiple channels of distribution; and so on. The Net tends to accelerate such market practices. The goal of product differentiation undertaken by a profit-maximizing firm is to shift demand curves for the good or service to the right and change the slope of the curves, making them less price-elastic at relevant prices. Simply put, companies differentiate and brand their products in order to convince consumers to buy more units and be less sensitive to price increases. Baker et al. have found that companies selling highly branded consumer health and beauty products may have the ability to raise their prices by as much as 17 percent without evoking much of a consumer response. They refer to this as a “pricing indifference band,” indicative of successful product branding. The result is higher prices and profits and increased stock market value. Khermouch, Holmes, and Ihlwan (2001) estimated the value of the Microsoft brand at $65.1 billion, over and above the value of its physical assets and intellectual capital. As an approximation of the present value of Microsoft’s ability to earn higher than competitive profits, this reflects the tendency (or necessity) of Net users to identify and prefer Microsoft products over those of other vendors, even those whose products are technically similar (some might say superior). A person who may not necessarily be thrilled with Microsoft’s products or prices will nonetheless choose to buy from Microsoft because it is a less risky course of action than making a purchase from a lesserknown or less robust software company. Are prices lower on the Internet? Not always!
Net consumers may prefer the familiar, and frequently the familiar constitutes an already well-known bricks-andmortar company that also operates on the Web. Microsoft’s heavy advertising and branding send positive messages about its reliability, a message that appears to be particularly important on the Net. Hence, rather than making brands less important, the Net often makes them more important and exercises an upward influence on some prices.
Situation 3: Auctions
T
he most common type of Internet auction is an “English” auction, in which prospective customers submit electronic bids for a good or service and the item is sold to the individual making the highest bid during a specified time period. The largest Web-based auction firm is eBay, which received more than 9 million unique hits in a single week in March 2002 and claims to have put 5.6 million items on sale per day in 2001. English auctions are quite susceptible to fraud and scams. However, they also create circumstances in which successful bidders pay unrealistically high prices for goods and services. Bidders get caught up in the frenzy of competition and bid well above what they would offer in a bricks-andmortar environment. This is a version of the “winner’s curse” that Thaler (1992) and others have documented. The “winner’s curse” is accentuated when very large numbers of people bid for an item, which is the situation in most eBay auctions. The frenetic nature of the moment and their mounting emotional investment in an item carry them away. The result may still be a Pareto optimal transaction (both participants are better off in their own eyes); however, a host of other transactions satisfying this criterion would have made the consumer even better off. Most Net auctions exhibit asymmetric information. For example, eBay bidders generally don’t know and can’t find out who else is making bids, what those bids are, or how many there have been. Moreover, bidders usually don’t know the precise condition of the item being auctioned. A “like new” version of a baseball card may be worth $100, a slightly worn version only $25. But which is it? Quality and reliability are in the eye of the beholder (or at least those writing the auction descriptions), and hence auctions may generate higher final prices than one would see if buyer and seller could evaluate each other and the merchandise. In any case, neither buyers nor sellers necessarily opt for the “best” price when they use an auction. According to Baker et al., half of the companies using “reverse” auctions (seeking bids from prospective suppliers) do not choose the lowest price anyway. Of these, 87 percent choose to stick with their current supplier, even though 49
the price is higher. Net auctions, then, frequently do not result in the lowest price for consumers or the highest price for suppliers.
Situation 4: Bots
“S
hop bot” is the label given to intelligent agent software that searches the Net for the lowest possible prices for a good or service and then arrays them in some fashion for consumers. The four most popular consumer bots in late 2001 were Bizrate, Dealtime, My Simon, and Pricescan. Several of these bots not only provide pricing information, they also rate the Net merchants whose prices they are reporting. Bots appear to create a “can’t lose” situation for consumers by providing them with the ability to seek out the lowest price for a good or service and determine the most reliable vendor. Unfortunately, the truth is more nuanced. The price list users receive from the bot may simply reflect which firms have paid the bot for the right to appear first or highest on that list. In other words, companies often pay bots for favorable placement, and sometimes must pay a fee even to be included on the list at all. Thus, econsumers can be misguided. Rather than helping them find the lowest price, the bot may be helping them find its biggest advertiser. Brynjolfsson and Smith’s (2001) study of shop bots found branded retailers charging 3.1 percent more than nonbranded ones, whereas retailers with which a consumer had dealt previously charged 6.8 percent more. Consumers may be nervous about giving their credit card number to an e-firm about which they know nothing. So they may reject the lowest price and opt to buy from a more familiar firm with which they have a history. Specialized bots pitting one seller against another now exist in many markets, including chemicals, certain financial services, transportation, and even bandwidth sales. An interesting example is NexTag, a bot enabling e-shoppers to bargain with multiple sellers at once without having previously committed to a purchase, as is the case with Priceline. However, not all consumers have access to bots; others prefer not to use them for a variety of reasons, including loyalty and service. Data mining sellers that operate their own increasingly sophisticated bots are most likely to pounce automatically on individual consumers who do not use shop bots. Goldman (2001) reports that fewer than 20 percent of MySimon’s consumers actually choose the lowest priced item when they use the bot to generate a price list. Other factors, including the conditions of sale and consumer loyalty, come to the fore. Arguably, many early consumer adopters of Net purchasing were highly price-sensitive,
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while more recent adopters are less price-sensitive and more attuned to convenience, for which some are willing to pay a premium. Shapiro and Varian (1999) point out that bots can actually result in higher prices than otherwise if they promote price-matching behavior because they give firms an excellent means to monitor each other’s pricing actions. In the bricks-and-mortar economy, competitors getting together, exchanging pricing information, and agreeing to match each other’s prices is easily recognized as illegal collusion. In the e-world, however, firms have no need to meet; they have the ability to monitor each other’s Web sites and price accordingly. All they need to do is program their software. Virtual collusion can occur with little effort, and there are at best only a few electronic tracks. Hence, firms sometimes have the ability to jack up the price because they know exactly what their competitors are or are not doing. Hof’s (2000) study of the behavior of Amazon and Barnes & Noble documents some of this behavior. The bottom line is that only some portion of e-consumers are likely to benefit greatly from bots. Those who do will be both knowledgeable and cautious. As Tedeschi (2000) puts it, comparison shopping by means of bots will pay off if consumers “will accept higher degrees of risk and lower levels of consumer service, and [commit] plenty of surfing time.”
Situation 5: Portal and community loyalty
P
ortals are Web sites that act as funnels for Net traffic. AOL, MSN, Netscape, and Yahoo! are examples of highly used portals that attract users (“communities”) who desire access to a bundled group of Web services. However, communities of interest also might develop around portals sponsored by universities, women’s groups, environmental advocates, and scores of other possibilities. In December 2001, Yahoo!, the largest portal site, attracted more than 70 million visitors. With Web users starting their surfing sessions at a portal, the goal of the portals is to become ingrained in users’ lives. The most popular portals not only provide convenient links to other key sites, but also such services as free e-mail, greeting cards, messaging, scheduling calendars, and Net search engines. In addition, they provide news coverage that is usually tailored to user interests, ranging from sports and technology to African-American issues and stamp collecting. To the extent that using portals becomes habitual for Net users, the portals provide the means for interested sellers to raise their prices because of targeted advertising. They can also guide potential consumers to preferred Web sites. Business Horizons / January-February 2003
There is a payoff, however. Agrawal, Arjona, and Lemmens (2001) report that community, issue-related portals have a 60 percent success rate in converting repeat visitors into members who make a transaction.
E-consumers can be misguided. Rather than helping them find the lowest price, the bot may be helping them find its biggest advertiser. If Yahoo! knows you are interested in politics, it might arrange for you to see an attractive advertisement for Joe Klein’s book about Bill Clinton. Convenience might then dictate that you purchase the book at the price offered rather than expending an effort to search. Inertia exists on the Net. Agrawal et al. report that 89 percent of all individuals who buy books on the Web make that purchase from the first site they visit, whereas 81 percent of those who buy music do so. Even travel, often considered an eminently “shoppable” item, sees Web users making 55 percent of all their purchases at the first site they visit. The bottom line is that portals control what Net users see first when they use the Net. They can keep track of where users go next and what they do when they get there. This is powerful information that permits reasonable inferences about users’ sensitivity to price changes and provides evidence on the productivity of specific advertising. It is information that any bricks-and-mortar firm would love to have. Portals, then, are a gold mine of information for e-companies that are contemplating the most profitable pricing policy.
Situation 6: Delivery costs
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hy should a company raise the price of a good or service it is attempting to sell you if it can earn the same revenue by charging you more for delivery? Fonti (1999) notes that shipping and delivery charges account for 10 to 20 percent of the revenue of a typical e-tailer. Consumers may be less responsive to increases in delivery costs than they are to the price of the good itself, but in the company’s view these revenue dollars are fungible. Net users often shop the price of the good, not the price plus delivery costs. By the time they
Are prices lower on the Internet? Not always!
find out about delivery costs, they have acquired some psychological commitment to purchasing the item, have listed their address and credit card information, and are reluctant to reverse their course. Consequently, many Net sellers do not disclose their delivery costs until the last possible moment. They deliberately “suck in” consumers with low advertised prices, then recoup any lost revenue by means of steep delivery costs. The era of free shipping for items purchased on the Net has largely ended and has been accompanied by the bankruptcy of the dotcoms that unwisely offered such terms. Nearly all e-companies view delivery costs as a manipulatable variable that offers them a stealthy way to raise their net revenue.
Situation 7: Tying and bundling
T
ying refers to the practice of binding the sale of one good to the sale of another. (“I won’t sell you A unless you also buy B from me.”) Microsoft, for example, may require individuals to purchase its Excel spreadsheet program in addition to (and as part of) its basic Windows operating system. Contrast tying to the practice of bundling, in which several items are packaged together and sold for a single price, yet can be purchased separately. An illustration is cable TV. Ordinarily, one has the freedom to purchase the HBO channel as part of a package, or separately, or not at all. Whether tying or bundling is involved, the firm’s goal is to convince (or force) consumers to pay more for an item than they would have in the absence of these practices. Moreover, tying and bundling may cause consumers to buy items that otherwise they might not have bought at all. A firm usually must have considerable market power to be able to force consumers to purchase one good along with another. Bundling, however, is a different matter, and the Net seems ready-made for it. Information goods—items that can be digitized such as newspapers, music, TV programs, movies, and stock market data—are highly susceptible to bundling. For example, you can choose a great variety of songs to be placed on a CD, or personalize the mix and sources of news you receive when you click on a Web site. Alternatively, you could select (and pay for) a given combination of Wall Street market news at sites such as Bloomberg or SmartMoney. The mathematics describing the economic circumstances under which tying and bundling will raise a firm’s profits is complicated. Our interest is in the practical potential to raise Net prices. If a firm ties or bundles products together, it does so to extract more revenue from customers. Because the marginal cost of supplying most information goods is 51
very small, nearly all of this additional revenue is profit. And that translates into forcing/enticing e-shoppers to pay a higher price to obtain the item they really wanted. The Net is ideally designed to allow firms to engage in “mix and match” tying and bundling activities and to adjust their prices and behavior on the run as they accumulate buyer information and experience.
T
he Internet is a wonderful tool in the hands of knowledgeable consumers. It has the potential to cut search and transaction costs, minimize market frictions, and reduce prices. Even so, as we have seen, it can be the vehicle for consumers to pay higher prices. It seems many consumers do not understand how this is possible, not least because of the sometimes excessive hype and publicity that accompany articles about the Net and the individual Web sites of firms. While the media coverage of the demise of many dotcom firms has penetrated the public consciousness and generated a series of journalistic morality tales, there has been only sparse coverage of the circumstances under which consumer use of the Net can result in higher prices. ❍
References and selected bibliography Adams, William J., and Janet L. Yellen. 1976. Commodity bundling and the burden of monopoly. Quarterly Journal of Economics 90/3 (August): 475-498. Agrawal, Vikas, Luis D. Arjona, and Ron Lemmens. 2001. E-performance: The path to rational exuberance. McKinsey Quarterly No. 1: 1-8. Akerlof, George. 1970. The market for lemons: Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84/3 (August): 488-500. Baker, Walter L., Eric Lin, Michael V. Marn, and Craig C. Zawada. 2001. Getting prices right on the Web. McKinsey Quarterly No. 2: 1-3. Borzo, Jeanette. 2001. Out of order? Wall Street Journal (24 September): R13. Brynjolfsson, Eric, and Michael D. Smith. 1999. Frictionless commerce? A comparison of Internet and conventional retailers. Unpublished paper, Massachusetts Institute of Technology. ———. 2001. Consumer decision-making at an Internet shopbot: Brand still matters. Journal of Industrial Economics 49/4 (December): 541-558.
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Burstein, Meyer L. 1960. A theory of full-line forcing. Northwestern University Law Review 55 (March-April): 62-95. ———. 1960. The economics of tie-in sales. Review of Economics and Statistics 42 (February): 68-73. Colvin, Geoffrey. 2000. The seller’s instant Net advantage. Fortune (10 July): 74. Creswell, Julie. 2001. eBay remains standing, but for how long? Fortune (2 April): 34. Fonti, Nancy. 1999. Free shipping draws online shoppers. Wall Street Journal (2 December): np. Gaither, Chris. 2001. Software to track customers’ needs helped firms react. New York Times (1 October): C1. Goldman, Joshua. 2001. Face time. The Standard.com/grok. (December-January): 23. Graham, Jefferson. 2002. In 2001, the “big three” sites got bigger. USA Today (7 January): 3D. Harris, E.S., and J.T. Abate. 2000. United States: The Internet price index. Global Economic Monitor. New York: Lehman Brothers. Hof, Robert D. 2000. Don’t cheat, children. Business Week (11 December): EB116. Kelly, Kevin. 1998. New rules for the new economy. London: Fourth Estate. Khermouch, Gerry, Stanley Holmes, and Moon Ihlwan. 2001. The best global brands. Business Week (6 August): 50-57. Koch, James V., and Richard J. Cebula. 2002. Price, quality, and service on the Internet: Sense and nonsense. Contemporary Economic Policy 20/1 (January): 25-37. Merrick, Amy. 2001. Priced to move: Retailers try to get leg up on markdowns with new software. Wall Street Journal (7 August): A1. Nelson, Philip. 1974. Advertising as information. Journal of Political Economy 82 (July-August): 729-754. Neuborne, Ellen. 2001. Break it to them quickly. Business Week (29 October): EB10. Nielsen/Net Ratings. 2002. Top 25 Web properties: Week ending March 24, 2002, US. @ pm.netratings.com. Schlesinger, Jacob M. 1999. If e-commerce helps kill inflation, why did prices just spike? Wall Street Journal (18 October): A1. Shapiro, Carl, and Hal R. Varian. 1999. Information rules. Cambridge: Harvard University Press. Tedeschi, Bob. 2000. Web air deals: Caveat surfer. New York Times (16 April): 4 (Sec. 5). ______. 2001. Spy on your customers (they want you to). SmartBusinessMag.com (August): 58ff. ______. 2002. Fine-tuning customer behavior. New York Times (1 April): C6. Thaler, Richard M. 1992. The winner’s curse: Paradoxes and anomalies of economic life. Princeton, NJ: Princeton University Press.
Business Horizons / January-February 2003
James V. Koch Board of Visitors Professor of Economics, Old Dominion University, Norfolk, Virginia
I
t is almost an article of faith among some that people who use the Internet pay lower prices for the goods and services they buy. “Lower” here means prices less than they would pay for the same goods or services at a bricks-and-mortar store. The everyday experiences of many Net users provide informal support for this notion, and there is some rigorous empirical evidence in favor of it, although the Bureau of Labor Statistics has yet to publish significant comparisons between Net prices and non-Net prices. The most comprehensive, fresh evidence available (produced by Lehman Brothers) indicates that most Net prices are lower than those charged by bricks-and-mortar firms. Even after taking shipping costs into account, Net prices were 38 percent lower for apparel items; 28 percent lower for prescription drugs, alcohol, and cigarettes; 4 percent lower for home electronics and groceries; 2 percent higher for hardware; and 9 percent higher for toys. Overall, report Harris and Abate (2000), the Net prices of 93 different items were 13 percent lower than bricks-and-mortar prices, shipping included. Koch and Cebula (2002) provide an extensive summary of this and other evidence.
Web. But reality is different. The
Thus, many consumers often do pay less when they use the Net. Nevertheless, a surprisingly large set of circumstances exists in which Net users end up paying higher prices for goods and services. Here we examine seven of these circumstances.
Net is not always the shopper’s
A bit of theoretical background
paradise they may think. Data mining,
The more accurate the information users have about the goods and services they might buy, the less they are apt to pay. If consumer Net searches generate more and better information for consumers, then the Net is likely to reduce price. Such searches have the potential to cut transaction costs and make it cheaper to evaluate goods and services and ascertain prices. Consequently, the Net can increase the efficiency of markets and thus assist both buyers and sellers.
Most consumers believe they pay lower prices by using the
high levels of product branding, auctions, consumer use of shopping robots, persistent consumer use of portals, deceptively high delivery costs, and product bundling can all cause consumers to pay more than they would in a conventional bricks-and-mortar store. Net shoppers should beware these seven traps.
Even so, if goods and services are differentiated and consumers develop brand loyalties, then consumers might 47
well pay higher prices, even on the Net. Some individuals will pay a premium to purchase items sold by familiar, reliable sellers. Situations also arise in which buyers and sellers have different (asymmetric) information, which can result in higher prices. In particular, sellers may know something about their goods or services that buyers do not. The market for “lemons” in used cars is a well-known example, but the same asymmetric information phenomenon is often present in many Net auctions and where “experience” goods are involved that require consumers to actually use or sample items in order to know if they will be satisfying. It is not surprising in such situations when Net users end up paying more. The widely held notion that the Net results in lower prices actually applies to reasonably specific theoretical circumstances involving relatively homogeneous “shopping” goods when information is symmetric and transaction costs (including the value of the surfer’s time) are low. Reality may be different. Just what these qualifications are will be demonstrated here.
Situation 1: Data mining
D
ata mining—euphoniously described as “customer relationship management,” or CRM, by many firms—describes the ubiquitous practice of Net sellers collecting extensive information about consumers. Such information may range from customers’ addresses and incomes to a complete history of their surfing behavior. “For nearly a decade,” reports Gaither (2001), “corporations have been using increasingly powerful databases to collect and store huge amounts of information about their customers and their practices.” The digital cookies and pixel tags sent to the hard disks of computer users by Net sellers ranging from Amazon to Enterprise Rent-A-Car constitute especially fruitful and frequently unnoticed means for firms to collect such data, though demonstrably many consumers willingly supply such information. The truth is, Web surfing is seldom anonymous. It is as if users have an electronic bar code associated with their every Net action that is instantaneously scanned every time they make a move. Such snooping might inspire a police investigation in the real world, but it is both commonplace and legal on the Net. Very few laws constrain data mining on the Net and, according to Tedeschi (2002), sellers now have the ability “to analyze data about sales and customer browsing patterns and respond quickly with offers.” Hence, whatever means companies use to acquire data, they use that information to tailor the advertising Web users see and the prices quoted. The Net provides an inexpensive and almost instantaneous means for firms to test the price sensitivity of e-consumers. For example, a Net 48
seller might quote every tenth or twentieth visitor a higher price, then observe the results. The next day, or even the next hour, it could try a different experiment. Such experimentation would be difficult and very time-consuming for a bricks-and-mortar firm to pursue. As a consequence, data mining provides Net sellers with new ways to raise prices. According to Baker et al. (2001), Tickets.com earns up to 45 percent more sales revenue selling tickets on the Web than it does in person. Moreover, electronic components typically generate 33 percent more revenue on the Net, high-end automobiles 25 percent more, and video games 17 percent more. Memorabilia sold on the Net sell for an amazing 400 to 500 percent more. Meanwhile, reports Tedeschi (2001), Amazon has used Net data mining to offer prospective customers advertising and prices that have enabled it to produce a conversion rate (the percentage of its Web site visitors who actually become buyers) three times the industry average. Whereas the customer who walks into a bricks-and-mortar store is often a “statistical mystery” without a known history, state Baker et al., such is not the case on the Net. E-companies can almost instantaneously categorize and segment prospective customers based on data mining information and then tailor their prices and terms accordingly. Economically speaking, Tickets.com and other firms operating on the Net use data mining to gain a strong sense of the supply and demand situation in their markets, make rough-and-ready estimates of the price and income elasticities of demand of their customers, then base their
The truth is, Web surfing is seldom anonymous. It is as if users have an electronic bar code associated with their every Net action that is instantaneously scanned every time they make a move. prices on those estimates. Frequently the models underpinning these activities are highly sophisticated. Airlines have long practiced “yield management” to maximize the revenue they receive from specific air flights; now this practice has spread to mainline retailers such as Macy’s, J.C. Penney, L.L. Bean, and Shopko. Based on highly dynamic pricing, it typically results in different consumers paying different prices for the same items, especially when perishable items such as air tickets and hotel rooms are Business Horizons / January-February 2003
involved. On the Net, prices can be changed hundreds of times a day. Moreover, because most Web transactions are private, one consumer does not know what price the other has paid, which gives e-sellers the room to raise prices selectively.
Situation 2: Product differentiation and branding
R
elatively few generic, essentially homogeneous goods and services exist. For every generic microcomputer sold over the Web, hundreds more highly identifiable Dell, Compaq, and Gateway computers are purchased. Net sellers may differentiate their products by means of advertising (“Dude, you’re gettin’ a Dell!”) and distinctive packaging and labeling (witness Gateway’s spotted cow), which lead to the conscious branding of the product in consumers’ eyes. But they can also brand their products in other ways: by manipulating the terms of sale, including guarantees, warranties, credit, service, giveaways, and bundling; by creating the ambience connected to the sale; by controlling the time of the sale; by offering multiple channels of distribution; and so on. The Net tends to accelerate such market practices. The goal of product differentiation undertaken by a profit-maximizing firm is to shift demand curves for the good or service to the right and change the slope of the curves, making them less price-elastic at relevant prices. Simply put, companies differentiate and brand their products in order to convince consumers to buy more units and be less sensitive to price increases. Baker et al. have found that companies selling highly branded consumer health and beauty products may have the ability to raise their prices by as much as 17 percent without evoking much of a consumer response. They refer to this as a “pricing indifference band,” indicative of successful product branding. The result is higher prices and profits and increased stock market value. Khermouch, Holmes, and Ihlwan (2001) estimated the value of the Microsoft brand at $65.1 billion, over and above the value of its physical assets and intellectual capital. As an approximation of the present value of Microsoft’s ability to earn higher than competitive profits, this reflects the tendency (or necessity) of Net users to identify and prefer Microsoft products over those of other vendors, even those whose products are technically similar (some might say superior). A person who may not necessarily be thrilled with Microsoft’s products or prices will nonetheless choose to buy from Microsoft because it is a less risky course of action than making a purchase from a lesserknown or less robust software company. Are prices lower on the Internet? Not always!
Net consumers may prefer the familiar, and frequently the familiar constitutes an already well-known bricks-andmortar company that also operates on the Web. Microsoft’s heavy advertising and branding send positive messages about its reliability, a message that appears to be particularly important on the Net. Hence, rather than making brands less important, the Net often makes them more important and exercises an upward influence on some prices.
Situation 3: Auctions
T
he most common type of Internet auction is an “English” auction, in which prospective customers submit electronic bids for a good or service and the item is sold to the individual making the highest bid during a specified time period. The largest Web-based auction firm is eBay, which received more than 9 million unique hits in a single week in March 2002 and claims to have put 5.6 million items on sale per day in 2001. English auctions are quite susceptible to fraud and scams. However, they also create circumstances in which successful bidders pay unrealistically high prices for goods and services. Bidders get caught up in the frenzy of competition and bid well above what they would offer in a bricks-andmortar environment. This is a version of the “winner’s curse” that Thaler (1992) and others have documented. The “winner’s curse” is accentuated when very large numbers of people bid for an item, which is the situation in most eBay auctions. The frenetic nature of the moment and their mounting emotional investment in an item carry them away. The result may still be a Pareto optimal transaction (both participants are better off in their own eyes); however, a host of other transactions satisfying this criterion would have made the consumer even better off. Most Net auctions exhibit asymmetric information. For example, eBay bidders generally don’t know and can’t find out who else is making bids, what those bids are, or how many there have been. Moreover, bidders usually don’t know the precise condition of the item being auctioned. A “like new” version of a baseball card may be worth $100, a slightly worn version only $25. But which is it? Quality and reliability are in the eye of the beholder (or at least those writing the auction descriptions), and hence auctions may generate higher final prices than one would see if buyer and seller could evaluate each other and the merchandise. In any case, neither buyers nor sellers necessarily opt for the “best” price when they use an auction. According to Baker et al., half of the companies using “reverse” auctions (seeking bids from prospective suppliers) do not choose the lowest price anyway. Of these, 87 percent choose to stick with their current supplier, even though 49
the price is higher. Net auctions, then, frequently do not result in the lowest price for consumers or the highest price for suppliers.
Situation 4: Bots
“S
hop bot” is the label given to intelligent agent software that searches the Net for the lowest possible prices for a good or service and then arrays them in some fashion for consumers. The four most popular consumer bots in late 2001 were Bizrate, Dealtime, My Simon, and Pricescan. Several of these bots not only provide pricing information, they also rate the Net merchants whose prices they are reporting. Bots appear to create a “can’t lose” situation for consumers by providing them with the ability to seek out the lowest price for a good or service and determine the most reliable vendor. Unfortunately, the truth is more nuanced. The price list users receive from the bot may simply reflect which firms have paid the bot for the right to appear first or highest on that list. In other words, companies often pay bots for favorable placement, and sometimes must pay a fee even to be included on the list at all. Thus, econsumers can be misguided. Rather than helping them find the lowest price, the bot may be helping them find its biggest advertiser. Brynjolfsson and Smith’s (2001) study of shop bots found branded retailers charging 3.1 percent more than nonbranded ones, whereas retailers with which a consumer had dealt previously charged 6.8 percent more. Consumers may be nervous about giving their credit card number to an e-firm about which they know nothing. So they may reject the lowest price and opt to buy from a more familiar firm with which they have a history. Specialized bots pitting one seller against another now exist in many markets, including chemicals, certain financial services, transportation, and even bandwidth sales. An interesting example is NexTag, a bot enabling e-shoppers to bargain with multiple sellers at once without having previously committed to a purchase, as is the case with Priceline. However, not all consumers have access to bots; others prefer not to use them for a variety of reasons, including loyalty and service. Data mining sellers that operate their own increasingly sophisticated bots are most likely to pounce automatically on individual consumers who do not use shop bots. Goldman (2001) reports that fewer than 20 percent of MySimon’s consumers actually choose the lowest priced item when they use the bot to generate a price list. Other factors, including the conditions of sale and consumer loyalty, come to the fore. Arguably, many early consumer adopters of Net purchasing were highly price-sensitive,
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while more recent adopters are less price-sensitive and more attuned to convenience, for which some are willing to pay a premium. Shapiro and Varian (1999) point out that bots can actually result in higher prices than otherwise if they promote price-matching behavior because they give firms an excellent means to monitor each other’s pricing actions. In the bricks-and-mortar economy, competitors getting together, exchanging pricing information, and agreeing to match each other’s prices is easily recognized as illegal collusion. In the e-world, however, firms have no need to meet; they have the ability to monitor each other’s Web sites and price accordingly. All they need to do is program their software. Virtual collusion can occur with little effort, and there are at best only a few electronic tracks. Hence, firms sometimes have the ability to jack up the price because they know exactly what their competitors are or are not doing. Hof’s (2000) study of the behavior of Amazon and Barnes & Noble documents some of this behavior. The bottom line is that only some portion of e-consumers are likely to benefit greatly from bots. Those who do will be both knowledgeable and cautious. As Tedeschi (2000) puts it, comparison shopping by means of bots will pay off if consumers “will accept higher degrees of risk and lower levels of consumer service, and [commit] plenty of surfing time.”
Situation 5: Portal and community loyalty
P
ortals are Web sites that act as funnels for Net traffic. AOL, MSN, Netscape, and Yahoo! are examples of highly used portals that attract users (“communities”) who desire access to a bundled group of Web services. However, communities of interest also might develop around portals sponsored by universities, women’s groups, environmental advocates, and scores of other possibilities. In December 2001, Yahoo!, the largest portal site, attracted more than 70 million visitors. With Web users starting their surfing sessions at a portal, the goal of the portals is to become ingrained in users’ lives. The most popular portals not only provide convenient links to other key sites, but also such services as free e-mail, greeting cards, messaging, scheduling calendars, and Net search engines. In addition, they provide news coverage that is usually tailored to user interests, ranging from sports and technology to African-American issues and stamp collecting. To the extent that using portals becomes habitual for Net users, the portals provide the means for interested sellers to raise their prices because of targeted advertising. They can also guide potential consumers to preferred Web sites. Business Horizons / January-February 2003
There is a payoff, however. Agrawal, Arjona, and Lemmens (2001) report that community, issue-related portals have a 60 percent success rate in converting repeat visitors into members who make a transaction.
E-consumers can be misguided. Rather than helping them find the lowest price, the bot may be helping them find its biggest advertiser. If Yahoo! knows you are interested in politics, it might arrange for you to see an attractive advertisement for Joe Klein’s book about Bill Clinton. Convenience might then dictate that you purchase the book at the price offered rather than expending an effort to search. Inertia exists on the Net. Agrawal et al. report that 89 percent of all individuals who buy books on the Web make that purchase from the first site they visit, whereas 81 percent of those who buy music do so. Even travel, often considered an eminently “shoppable” item, sees Web users making 55 percent of all their purchases at the first site they visit. The bottom line is that portals control what Net users see first when they use the Net. They can keep track of where users go next and what they do when they get there. This is powerful information that permits reasonable inferences about users’ sensitivity to price changes and provides evidence on the productivity of specific advertising. It is information that any bricks-and-mortar firm would love to have. Portals, then, are a gold mine of information for e-companies that are contemplating the most profitable pricing policy.
Situation 6: Delivery costs
W
hy should a company raise the price of a good or service it is attempting to sell you if it can earn the same revenue by charging you more for delivery? Fonti (1999) notes that shipping and delivery charges account for 10 to 20 percent of the revenue of a typical e-tailer. Consumers may be less responsive to increases in delivery costs than they are to the price of the good itself, but in the company’s view these revenue dollars are fungible. Net users often shop the price of the good, not the price plus delivery costs. By the time they
Are prices lower on the Internet? Not always!
find out about delivery costs, they have acquired some psychological commitment to purchasing the item, have listed their address and credit card information, and are reluctant to reverse their course. Consequently, many Net sellers do not disclose their delivery costs until the last possible moment. They deliberately “suck in” consumers with low advertised prices, then recoup any lost revenue by means of steep delivery costs. The era of free shipping for items purchased on the Net has largely ended and has been accompanied by the bankruptcy of the dotcoms that unwisely offered such terms. Nearly all e-companies view delivery costs as a manipulatable variable that offers them a stealthy way to raise their net revenue.
Situation 7: Tying and bundling
T
ying refers to the practice of binding the sale of one good to the sale of another. (“I won’t sell you A unless you also buy B from me.”) Microsoft, for example, may require individuals to purchase its Excel spreadsheet program in addition to (and as part of) its basic Windows operating system. Contrast tying to the practice of bundling, in which several items are packaged together and sold for a single price, yet can be purchased separately. An illustration is cable TV. Ordinarily, one has the freedom to purchase the HBO channel as part of a package, or separately, or not at all. Whether tying or bundling is involved, the firm’s goal is to convince (or force) consumers to pay more for an item than they would have in the absence of these practices. Moreover, tying and bundling may cause consumers to buy items that otherwise they might not have bought at all. A firm usually must have considerable market power to be able to force consumers to purchase one good along with another. Bundling, however, is a different matter, and the Net seems ready-made for it. Information goods—items that can be digitized such as newspapers, music, TV programs, movies, and stock market data—are highly susceptible to bundling. For example, you can choose a great variety of songs to be placed on a CD, or personalize the mix and sources of news you receive when you click on a Web site. Alternatively, you could select (and pay for) a given combination of Wall Street market news at sites such as Bloomberg or SmartMoney. The mathematics describing the economic circumstances under which tying and bundling will raise a firm’s profits is complicated. Our interest is in the practical potential to raise Net prices. If a firm ties or bundles products together, it does so to extract more revenue from customers. Because the marginal cost of supplying most information goods is 51
very small, nearly all of this additional revenue is profit. And that translates into forcing/enticing e-shoppers to pay a higher price to obtain the item they really wanted. The Net is ideally designed to allow firms to engage in “mix and match” tying and bundling activities and to adjust their prices and behavior on the run as they accumulate buyer information and experience.
T
he Internet is a wonderful tool in the hands of knowledgeable consumers. It has the potential to cut search and transaction costs, minimize market frictions, and reduce prices. Even so, as we have seen, it can be the vehicle for consumers to pay higher prices. It seems many consumers do not understand how this is possible, not least because of the sometimes excessive hype and publicity that accompany articles about the Net and the individual Web sites of firms. While the media coverage of the demise of many dotcom firms has penetrated the public consciousness and generated a series of journalistic morality tales, there has been only sparse coverage of the circumstances under which consumer use of the Net can result in higher prices. ❍
References and selected bibliography Adams, William J., and Janet L. Yellen. 1976. Commodity bundling and the burden of monopoly. Quarterly Journal of Economics 90/3 (August): 475-498. Agrawal, Vikas, Luis D. Arjona, and Ron Lemmens. 2001. E-performance: The path to rational exuberance. McKinsey Quarterly No. 1: 1-8. Akerlof, George. 1970. The market for lemons: Quality uncertainty and the market mechanism. Quarterly Journal of Economics 84/3 (August): 488-500. Baker, Walter L., Eric Lin, Michael V. Marn, and Craig C. Zawada. 2001. Getting prices right on the Web. McKinsey Quarterly No. 2: 1-3. Borzo, Jeanette. 2001. Out of order? Wall Street Journal (24 September): R13. Brynjolfsson, Eric, and Michael D. Smith. 1999. Frictionless commerce? A comparison of Internet and conventional retailers. Unpublished paper, Massachusetts Institute of Technology. ———. 2001. Consumer decision-making at an Internet shopbot: Brand still matters. Journal of Industrial Economics 49/4 (December): 541-558.
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Burstein, Meyer L. 1960. A theory of full-line forcing. Northwestern University Law Review 55 (March-April): 62-95. ———. 1960. The economics of tie-in sales. Review of Economics and Statistics 42 (February): 68-73. Colvin, Geoffrey. 2000. The seller’s instant Net advantage. Fortune (10 July): 74. Creswell, Julie. 2001. eBay remains standing, but for how long? Fortune (2 April): 34. Fonti, Nancy. 1999. Free shipping draws online shoppers. Wall Street Journal (2 December): np. Gaither, Chris. 2001. Software to track customers’ needs helped firms react. New York Times (1 October): C1. Goldman, Joshua. 2001. Face time. The Standard.com/grok. (December-January): 23. Graham, Jefferson. 2002. In 2001, the “big three” sites got bigger. USA Today (7 January): 3D. Harris, E.S., and J.T. Abate. 2000. United States: The Internet price index. Global Economic Monitor. New York: Lehman Brothers. Hof, Robert D. 2000. Don’t cheat, children. Business Week (11 December): EB116. Kelly, Kevin. 1998. New rules for the new economy. London: Fourth Estate. Khermouch, Gerry, Stanley Holmes, and Moon Ihlwan. 2001. The best global brands. Business Week (6 August): 50-57. Koch, James V., and Richard J. Cebula. 2002. Price, quality, and service on the Internet: Sense and nonsense. Contemporary Economic Policy 20/1 (January): 25-37. Merrick, Amy. 2001. Priced to move: Retailers try to get leg up on markdowns with new software. Wall Street Journal (7 August): A1. Nelson, Philip. 1974. Advertising as information. Journal of Political Economy 82 (July-August): 729-754. Neuborne, Ellen. 2001. Break it to them quickly. Business Week (29 October): EB10. Nielsen/Net Ratings. 2002. Top 25 Web properties: Week ending March 24, 2002, US. @ pm.netratings.com. Schlesinger, Jacob M. 1999. If e-commerce helps kill inflation, why did prices just spike? Wall Street Journal (18 October): A1. Shapiro, Carl, and Hal R. Varian. 1999. Information rules. Cambridge: Harvard University Press. Tedeschi, Bob. 2000. Web air deals: Caveat surfer. New York Times (16 April): 4 (Sec. 5). ______. 2001. Spy on your customers (they want you to). SmartBusinessMag.com (August): 58ff. ______. 2002. Fine-tuning customer behavior. New York Times (1 April): C6. Thaler, Richard M. 1992. The winner’s curse: Paradoxes and anomalies of economic life. Princeton, NJ: Princeton University Press.
Business Horizons / January-February 2003