Knowing Certain Trademark Ads Were Confusing, Google Sold Them Anyway — for $100+ Million

Disclosure: I serve as a consultant to various companies that compete with Google. But I write on my own — not at the suggestion or request of any client, without approval or payment from any client.

When a user enters a search term that matches a company’s trademark, Google often shows results for the company’s competitors. To take a specific example: Searches for language software seller "Rosetta Stone" often yield links to competing sites — sometimes, sites that sell counterfeit software. Rosetta Stone think that’s rotten, and, as I’ve previously written, I agree: It’s a pure power-play, effectively compelling advertisers to pay Google if they want to reach users already trying to reach their sites; otherwise, Google will link to competitors instead. Furthermore, Google is reaping where others have sown: After an advertiser builds a brand (often by advertising in other media), Google lets competitors skim off that traffic — reducing the advertiser’s incentive to invest in the first place. So Google’s approach to trademarks definitely harms advertisers and trademark-holders. But it’s also confusing to consumers. How do we know? Because Google’s own documents admit as much.

Today Public Citizen posted an unredacted version of Rosetta Stone’s appellate brief in its ongoing litigation with Google. Google had sought to keep confidential the documents that ground district court and appellate adjudication of the dispute, but now some of the documents are available — giving an inside look at Google’s policies and objectives for trademark-triggered ads. Some highlights:

  • Through early 2004, Google let trademark holders request that ads be disabled if they used a trademark in keyword or ad text. But in early 2004, Google determined that it could achieve a "significant potential revenue impact" from selling trademarks as keywords. (ref)
  • In connection with Google’s 2004 policy change letting advertisers buy trademarks as keywords, Google conducted experiments to assess user confusion from trademarks appearing in search advertisements. Google concluded that showing a trademark anywhere in the text of an advertisement resulted in a "high" degree of consumer confusion. Google’s study concluded: "Overall very high rate of trademark confusion (30-40% on average per user) … 94% of users were confused at least once during the study." (ref)
  • Notwithstanding Google’s 2004 study, Google in 2009 changed its trademark policy to permit the user of trademarks in advertisement text. Google estimated that this policy change would result in at least $100 million of additional annual revenue, and potentially more than a billion dollars of additional annual revenue. Google implemented this change without any further studies or experiments as to consumer confusion. (ref)
  • Google possesses more than 100,000 pages of complaints from trademark holders, including at least 9,862 complaints from at least 5,024 trademark owners from 2004 to 2009. (ref)

Kudos to Public Citizen for obtaining these documents. That said, I believe Google should never have sought to limit distribution of these documents in the first place. In other litigation, I’ve found that Google’s standard practice is to attempt to seal all documents, even where applicable court rules require that documents be provided to the general public. That’s troubling, and that needs to change.

Hard-Coding Bias in Google “Algorithmic” Search Results

I present categories of searches for which available evidence indicates Google has “hard-coded” its own links to appear at the top of algorithmic search results, and I offer a methodology for detecting certain kinds of tampering by comparing Google results for similar searches. I compare Google’s hard-coded results with Google’s public statements and promises, including a dozen denials but at least one admission. I conclude by analyzing the impact of Google’s tampering on users and competition, and by proposing principles to block Google’s bias.

Details, including screenshots, methodology, proposed regulatory response, and analogues in other industries:

Hard-Coding Bias in Google “Algorithmic” Search Results

A Closer Look at Google’s Advertisement Labels

Google's tiny 'Ads' labelGoogle’s tiny ‘Ads’ label

The FTC has called for “clear and conspicuous disclosures” in advertisement labels at search engines, and the FTC specifically emphasized the need for “terms and a format that are easy for consumers to understand.” Unfortunately, Google’s new advertisement labels fail this test: Google’s “Ads” label is the smallest text on the page, far too easily overlooked. (Indeed, as I show in the image at left, the “Ads” label substantially fits within an “o” in “Google.”) Meanwhile, Google now merges algorithmic and advertisement results merged within a single set of listings; Google’s “Help” explanations are inaccurate; and Google uses inconsistent labels mere inches apart within search results, as well as across services.

Details, including the shortfalls, screenshots, comparisons, and proposed alternatives:

A Closer Look at Google’s Advertisement Labels

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Labels and Disclosures in Search Advertising with Duncan Gilchrist

Disclosure: I serve as a consultant to various companies that compete with Google. But I write on my own — not at the suggestion or request of any client, without approval or payment from any client.

Search engines have long labeled their advertisements with labels like “Sponsored links”, “Sponsored results”, and “Sponsored sites.” Do users actually know that these labels are intended to convey that the listings are paid advertisements? In a draft paper we’re posting today, Duncan Gilchrist and I try to find out.

“Sponsored Links” or “Advertisements”?: Measuring Labeling Alternatives in Internet Search Engines

In an online experiment, we measure users’ interactions with search engines, both in standard configurations and in modified versions with improved labels identifying search engine advertisements. In particular, for a random subset of users, we change “sponsored link” labels to instead read “paid advertisement.” We find that users receiving the “paid advertisement” label click 25% to 33% fewer advertisements and correctly report that they click fewer advertisements, controlling for the number of advertisements they actually click. Results are most pronounced for commercial searches, and for users with low income, low education, and little online experience.

We consider our findings particularly timely in light of Google’s change, just last week, to label many of its advertisements as “Ads.” On one view, “Ads”” is an improvement – probably easier for unsophisticated consumers to understand. Yet it’s a strikingly tiny label – the smallest text anywhere in Google’s search results, and about a quarter as many pixels as the corresponding disclosure on other search engines. As our paper points out, FTC litigation has systematically sought the label “Paid Advertisement, and we still think that’s the better choice.

Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions

Edelman, Benjamin, and Michael Schwarz. “Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions.” American Economic Review 100, no. 2 (May 2010): 597-602. (First circulated in 2006 as Optimal Auction Design in a Multi-unit Environment: The Case of Sponsored Search Auctions. Reprinted in The Economics of E-Commerce, Michael Baye and John Morgan, editors, 2016.)

We characterize the optimal (revenue maximizing) auction for sponsored search advertising. We show that a search engine’s optimal reserve price is independent of the number of bidders and independent of the rate at which click-through rate declines over positions. We separate the effects of reserve price increases into direct effects (on the low bidder) and indirect effects (on others), and we show that most of the incremental revenue from setting reserve price optimally comes from indirect effects.

Google Inc. (teaching materials) with Thomas Eisenmann

Edelman, Benjamin, and Thomas R. Eisenmann. “Google Inc.” Harvard Business School Case 910-036, January 2010. (Revised April 2011.) (Winner of ECCH 2011 Award for Outstanding Contribution to the Case Method – Strategy and General Management.) (educator access at HBP.)

Describes Google’s history, business model, governance structure, corporate culture, and processes for managing innovation. Reviews Google’s recent strategic initiatives and the threats they pose to Yahoo, Microsoft, and others. Asks what Google should do next. One option is to stay focused on the company’s core competence, i.e., developing superior search solutions and monetizing them through targeted advertising. Another option is to branch into new arenas, for example, build Google into a portal like Yahoo or MSN; extend Google’s role in e-commerce beyond search, to encompass a more active role as an intermediary (like eBay) facilitating transactions; or challenge Microsoft’s position on the PC desktop by developing software to compete with Office and Windows.

Supplements:

Google Inc. (Abridged) – Case (HBP 910032)

Teaching Materials:

Google inc. and Google Inc. (Abridged) – Teaching Note (HBP 910050)

CPC/CPA Hybrid Bidding in a Second Price Auction

Edelman, Benjamin, and Hoan Lee. “CPC/CPA Hybrid Bidding in a Second Price Auction.” Harvard Business School Working Paper, No. 09-074, December 2008.

We develop a model of online advertising in which each advertiser chooses from multiple advertising measurement metrics–paying either for each click on its ads (CPC), or for each purchase that follows an ad-click (CPA). Our analysis extends classic auction results by allowing players to make bids using two different pricing schemes, while the driving information for bidders’ endogenous selection–the conversion rate–is hidden from the seller. We show that the advertisers with the most productive sites prefer to pay CPC, while advertisers with lower quality sites prefer to pay CPA–a result that may be viewed as counterintuitive since low quality sites cannot proudly tout their conversion rates. This result holds even if an ad platform’s assessment of site quality is correct in expectation. We also show that by offering both CPC and CPA, an ad platform can weakly increase its revenues compared to offering either alternative alone.

Microsoft adCenter (teaching materials) with Peter Coles

Coles, Peter, and Benjamin Edelman. “Microsoft adCenter.” Harvard Business School Case 908-049, January 2008. (Revised February 2010.) (educator access at HBP. request a courtesy copy.)

Microsoft considers alternatives to expand its presence in online advertising, especially text-based pay-per-click advertising. Google dominates, and it is unclear how Microsoft can grow, despite considerable technical and financial resources. Microsoft considers a set of alternatives, each with clear benefits but also serious challenges.

Teaching Materials:

Microsoft adCenter (Teaching Note) – HBP 908062

On Best-Response Bidding in GSP Auctions

Cary, Matthew, Aparna Das, Benjamin Edelman, Ioannis Giotis, Kurtis Heimerl, Anna R. Karlin, Claire Mathieu, and Michael Schwarz. “On Best-Response Bidding in GSP Auctions.” Harvard Business School Working Paper, No. 08-056, January 2008.

How should players bid in keyword auctions such as those used by Google, Yahoo! and MSN? We model ad auctions as a dynamic game of incomplete information, so we can study the convergence and robustness properties of various strategies. In particular, we consider best-response bidding strategies for a repeated auction on a single keyword, where in each round, each player chooses some optimal bid for the next round, assuming that the other players merely repeat their previous bids. We focus on a strategy we call Balanced Bidding (BB). If all players use the BB strategy, we show that bids converge to a bid vector that obtains in a complete information static model proposed by Edelman, Ostrovsky, and Schwarz. We prove that convergence occurs with probability 1, and we compute the expected time until convergence.