Price Coherence: Impact and Incentives with Julian Wright

In modern markets, buyers can often buy the same good or service directly from a seller, and through one or more intermediaries, all at the same exact price. How should buyers behave in these markets? The natural strategy is to choose whichever intermediary offers the greatest benefit — perhaps a rebate, some loyalty points, or superior service. One intermediary might charge sellers far higher fees than another. But to buyers, these fees are irrelevant since they are paid entirely by sellers. It’s a classic I-choose-you-pay situation, and buyers predictably head for high-benefit intermediaries. The resulting outcomes can be both distortionary and welfare-reducing. For example, seeing an airline’s flights available both directly on the airline’s web site and via an online travel agent (like Expedia or Orbitz) (“OTA”), a buyer has every reason to choose the latter — avoiding retyping name, address, and payment details that the OTA already has on file. Convenient as an OTA may be, few users would willingly pay the ~$3 per segment (~$12 for a standard US domestic connecting round-trip) that OTAs charge to airlines. So too for credit cards: Their rebates and points are valuable, but most consumers would prefer a ~3% discount (the fee the seller pays to the card network).

Last week Julian Wright and I posted Price Coherence and Adverse Intermediation, analyzing incentives and outcomes in affected markets. We find that price coherence reduces consumer surplus and welfare due to inflated retail prices, over-investment in providing benefits to buyers, and excessive usage of intermediaries’ services. Notably, competition among intermediaries does not fix these problems: Indeed, competition among intermediaries intensifies the problems by increasing the magnitude of the effects and broadening the circumstances in which they arise.

Our analysis is grounded in eight diverse markets: insurance brokers and financial advisors, marketplaces, cashback/rebate services, search engine advertising, real estate buyers’ agents, restaurant ordering, and restaurant reservations, plus travel booking and credit cards as discussed above. In each instance, a law, norm, intermediary policy, or similar rigidity prevents sellers from passing an intermediary’s fees to the specific buyers who choose to use that intermediary. They’re complex markets, some quite large, and each worth a look. Their key similarity: In each instance, if a buyer foregoes the corresponding intermediary, the buyer still pays a share of intermediaries’ charges for others. If a buyer places a benefit on the intermediary’s service, perhaps still far less than what the intermediary charges the seller, the buyer might as well sign up.

It may seem counterintuitive that a series of voluntary transactions leaves all parties worse off. After all, no one would willingly enter a single transaction that makes him worse off. But the interlocking relationships truly can have that effect. Returning to the airline example: Consumers use OTAs because they anticipate, correctly, that substantially all airlines are in OTAs and because consumers know that prices are equal whether buying from an OTA versus directly from an airline. With many users shopping at OTA web sites, airlines then feel compelled to offer their flights via OTAs. In general, an individual airline would not want to withhold its flights from OTAs — it would lose too many sales. And an individual consumer has no reason to book directly — no cash savings from forgoing the OTA-provided benefits. On net, both buyer and seller end up using the intermediary even when they were perfectly capable of finding each other directly and even when the intermediary’s fees exceed the value the intermediary actually provides.

Our draft:

Price Coherence and Excessive Intermediation (last updated March 2015)

(update: published as “Price Coherence and Excessive Intermediation.” Quarterly Journal of Economics 130, no. 3 (August 2015): 1283-1328.)

The Market Power of Platform-Mediated Networks (teaching materials)

Edelman, Benjamin. “The Market Power of Platform-Mediated Networks.” Harvard Business School Technical Note 914-029, January 2014. (Revised March 2015.) (educator access at HBP. request a courtesy copy.)

This note provides criteria to evaluate the power of a platform-mediated network. For a company considering building such a network or an investor considering funding such an effort, this analysis reveals the scope and desirability of the opportunity. Meanwhile, for a company doing business with such a network, as a supplier or as a customer, this note provides strategies to shift the split of surplus to the company’s benefit.

Services for Advertisers – Avoiding Waste and Improving Accountability

In the course of my research on spyware/adware, typosquatting, popups, and other controversial online practices, I have developed the ability to identify practices that overcharge online advertisers. I report my observations to select advertisers and top networks in order to assist them in improving the cost-effectiveness of their advertising including by flagging improper ad placements, rejecting unjustified charges, and avoiding untrustworthy partners. This page summarizes the kinds of practices I uncover and presents representative examples drawn from my publications:

Services for Advertisers – Avoiding Waste and Improving Accountability

Measuring and Managing Online Affiliate Fraud with Wesley Brandi

Affiliate programs vary dramatically in their incidence of fraud. In some merchants’ affiliate programs, rogue affiliates fill the ranks of high-earners. Yet other similarly-sized merchants have little or no fraud. Why the difference?

In Information and Incentives in Online Affiliate Marketing, Wesley Brandi and I examine the impact of varying merchant management decisions. Some merchants hire specialist outside advisors (“outsourced program managers” or OPM’s) to set and enforce program rules. Others ask affiliate network staff to make these decisions. Still others handle these tasks internally.

A merchant’s choice of management structure has significant implications for both the information available to decision-makers and the incentives that motivate those decision-makers. Outside advisors tend to have better information: An OPM sees problems and trends across its many clients. A network is even better positioned — enjoying direct access to log files, custom reports, and problems reported by all merchants in the network. That said, outside advisors usually suffer clear incentive problems. Most notably, networks are usually paid in proportion to a merchant’s affiliate channel spending, so networks have a significant incentive to encourage merchants to accept even undesirable affiliates. In contrast, incentives for merchants’ staff are typically more closely aligned with the merchant’s objectives. For example, many in-house affiliate managers have stock, options, or bonus that depend on company profitability. And working in a company builds intrinsic motivation and loyalty. In short, there are some reasons to think outsourced specialists will yield superior results, but other reasons to favor in-house staff.

To separate these effects, we used crawlers to examine affiliate fraud at what we believe to be unprecedented scope. Our crawlers ran more than 2 million page-loads on a variety of computers and virtual computers, examining the relative susceptibility of all CJ, LinkShare, and Google Affiliate Network merchants (as of spring 2012) to adware, cookie-stuffing, typosquatting, and loyalty apps.

We found outside advisors best able to find “clear fraud” plainly prohibited by network rules, specifically adware and cookie-stuffing. But in-house staff did better at avoiding “grey area” practices such as typosquatting — schemes less plainly prohibited by network rules, yet still contrary to merchants’ interests. On balance, there are good reasons to favor each management approach. Our advice: A merchant choosing outsourced management should be sure to insist on borderline decisions always taken with the merchant’s interests at heart. A merchant managing its programs in-house should be careful to avoid known cheaters that a savvy specialist would more often exclude.

Our results clearly reveal that networks take actions that are less than optimal for merchants. It’s tempting to attribute this shortfall to malicious intent by networks, but the same outcome could result from networks simply putting their own interests first. Consider a network that receives undisputed proof that a given affiliate is cheating a given merchant. Should the network eject that affiliate from the entire network (and all affiliated merchants), or only from that single merchant’s program? The former helps dozens or hundreds of merchants, but with corresponding reduction to network revenues. No wonder many networks chose the latter. Similarly, when networks decide how much to invest in network quality — engineers, analysts, crawlers, and the like — their incentive to improve quality is tempered by both direct cost and foregone revenue.

Incidental to our analysis of management structure, we gathered significant data about the scope of affiliate fraud more generally. Some differences are stark: For example, Table 4 reports Google Affiliate Network merchants suffering, on average, less than half as much adware and cookie-stuffing as LinkShare merchants. I’ve been critical of Google on numerous issues. But when it comes to affiliate quality, GAN was impressive, and GAN’s high standards show clearly in our large-sample data. Note that our analysis precedes Google’s April 2013 announcement of GAN’s shutdown.

Our full analysis is under review by an academic journal.

(update: published as Edelman, Benjamin, and Wesley Brandi. “Risk, Information, and Incentives in Online Affiliate Marketing.” Journal of Marketing Research (JMR) 52, no. 1 (February 2015): 1-12. (Lead Article.)

Competing Ad Auctions

Ashlagi, Itai, Benjamin Edelman, and Hoan Soo Lee. “Competing Ad Auctions.” Harvard Business School Working Paper, No. 10-055, January 2010. (Revised May 2010, February 2011, September 2013.)

We present a two-stage model of competing ad auctions. Search engines attract users via Cournot-style competition. Meanwhile, each advertiser must pay a participation cost to use each ad platform, and advertiser entry strategies are derived using symmetric Bayes-Nash equilibrium that lead to the VCG outcome of the ad auctions. Consistent with our model of participation costs, we find empirical evidence that multi-homing advertisers are larger than single-homing advertisers. We then link our model to search engine market conditions: We derive comparative statics on consumer choice parameters, presenting relationships between market share, quality, and user welfare. We also analyze the prospect of joining auctions to mitigate participation costs, and we characterize when such joins do and do not increase welfare.

The Ad Networks and Advertisers that Fund Ad Injectors with Wesley Brandi

Webcake adware inserts an AT&T ad into the YouTube site without permission from Google.Webcake adware inserts an AT&T ad into the YouTube site without permission from Google.

Ad injectors insert ads into others’ sites, without permission from those sites and without payment to those sites. In this article, we review the basic operation of ad injectors, then examine the ad networks, exchanges, and other intermediaries that broker the placement of advertising through injectors.

We also report which advertisers most often advertise through injectors. Whether through complexity, inattention, or indifference, these advertisers’ expenditures are ultimately the sole revenue source for injectors.

The Ad Networks and Advertisers that Fund Ad Injectors