Honey’s Contractual Breaches and Value (or Lack of It) to Merchants

On December 21, YouTuber MegaLag dropped a 23 minute video eviscerating Honey.  Calling Honey a “scam”, he made two core allegations.

  1. Honey announcing
    Honey claims affiliate commission if a user presses “Got it” to acknowledge no deal found

    Honey takes payments that would otherwise go to influencers who recommended products users buy. (video at 2:50) MegaLag shows Honey claiming payments in four scenarios: i) if a user activates a function to search for coupons (even if none are found), ii) if a user activates a function to claim Honey Gold (no matter how meager the rebate), iii) if the user gets the message “We searched for you but didn’t find any deals” and merely presses the button “Got it”, and iv) If Honey shows the message “Get Rewarded with PayPal” “Shop eligible items to earn cash off future purchases” and the user presses “checkout”.

  2. Honey doesn’t actually get the best deals for users. If a merchant joins Honey (and begins to pay Honey affiliate commissions), Honey allows the merchant to limit which coupons Honey shows to users. MegaLag points out that letting merchants remove discounts from Honey is squarely contrary to Honey’s promise to users that it will find “the Internet’s best discount codes” and “find every working promo code on the Internet.” (video at 16:20)

16 million views and growing, MegaLag’s video has prompted a class action lawsuit and millions of users uninstalling Honey.

I’m a big fan of MegaLag.  I watched most of his other videos, and they’re both informative and useful—for example, testing Apple AirTags by intentionally leaving items to be taken; exploring false claims by DHL about both package status and their supposed investigations.  Meanwhile, nothing in MegaLag’s online profile indicates prior experience in affiliate marketing.  But for a first investigation on this subject, he gets most things right, and he uses many appropriate methods including browser dev tools and screen-capture video.  Based on its size and its practice, Honey absolutely deserves the scrutiny it’s now getting.  Kudos to MegaLag.

Nonetheless there’s a lot MegaLag doesn’t say.  Most notably, he doesn’t mention contracts—the legal infrastructure that both authorizes Honey to get paid and sets constraints on when and how it may operate.  Furthermore, he doesn’t even consider whether merchants get good value for the fees they pay Honey.  In this piece, I explore where I see Honey most vulnerable—both under contract and for merchants looking to spend their marketing funds optimally.

The contracts that bind Honey

Affiliate marketing comprises a web of contracts.  Most affiliate merchants hire a network to track which affiliate sent which traffic, to provide reports to both merchant and publishers, and to handle payments.  For a single affiliate-merchant relationship, an affiliate ends up subject to at least two separate contracts: the network’s standard rules, and any merchant-specific rules.  Of course there are tens of thousands of affiliate merchants, and multiple big networks.  So it’s impossible to make a blanket statement about how all contracts treat Honey’s conduct.  Nonetheless, we can look at some big ones.  Numbering added for subsequent reference.

Commission Junction Publisher Service Agreement

C1 “You must promote Advertisers such that You do not mislead the Visitor”

C2 “the Links deliver bona fide Transactions by the Visitor to Advertiser from the Link”

C3 “You must accurately, clearly and completely describe all promotional methods by selecting the appropriate descriptions and providing additional information when necessary.”

C4 “You agree to: (i) use ethical and legal business practices”

C5 “Software-based activity must honor the CJ Affiliate Software Publishers Policy requirements (as such requirements may be modified from time to time), including but not limited to: (i) installation requirements, (ii) enduser agreement requirements, (iii) afsrc=1 requirements, (iv) requirements prohibiting usurpation of a Transaction that might otherwise result in a Payout to another Publisher (e.g. by purposefully detecting and forcing a subsequent click-through on a link of the same Advertiser) and (v) non-interference with competing advertiser/ publisher referrals.”

Rakuten Advertising Downloadable Software Applications (DSAs) Overview, Testing Process, Policies

R1 “Your DSA should become inactive on the sites of any advertisers who opt-out or stand down on those that do not want you to redirect their traffic.  Publishers who fail to comply with this rule will jeopardize their relationship with advertisers as well as with Rakuten Advertising.”

R2 “[W]e expect your DSA to: Stand down when it recognizes any publisher links”

R3 “[A]ll software must recognize Supplier domains and the linksynergy tracking links. When a Supplier domain or the linksynergy code is detected, the software may not operate or redirect the consumer to the advertiser site using the Software Publisher tracking ID (also known as Supplier Affiliate ID or Encrypted ID). We do not allow any DSA software that interferes with or deters from any Publisher or Advertiser website.”

R4 “The DSA must stand-down and not display any forms of sliders or pop-ups to prompt activation if another publisher has already referred an end user.”

R5 “The DSA must not force clicks or “cookie stuff”. The DSA must not insert a cookie onto the user’s computer without the user knowingly taking an action that results in the cookie being placed.”

R6 “The end user must click through the offer that is presented. Placing the mouse over an offer, only viewing it or viewing all offers is not a click through.”

R7 “The DSA must not automatically drop a cookie when the end user is only viewing offers. The cookie should only be dropped once the end user clicks on a specific offer.”

Awin including ShareASale – Code of Conduct, Awin US Publisher Terms, SAS US Publisher Agreement

A1 “’Click’ means the intentional and voluntary following of a Link by a Visitor as part of marketing services as reported by the Tracking Code only;”

A2 “Publishers only initiate tracking via a tracking link used for click tracking if the user voluntarily and intentionally interacted with the Ad Media or Tracking link.”

A3 Publishers only initiate tracking for a specific advertiser if the consumer interacted directly with ad media for this advertiser.”

A4 ”do not mislead consumers”

A5 “transparency about traffic sources and the environment that ads are displayed in”

In addition, all networks indicate that publishers must disclose their practices to both networks and merchants.  Awin Code of Conduct is representative: “Publishers proactively disclose all promotional activities and obtain advertiser approval for their activities.”  Rakuten’s Testing Process is even more prescriptive, requiring that an affiliate both to submit a first version and to notify Rakuten about any changes to its software so it can retest; plus requiring publishers to answer 16 questions about their software including technical details such as DOM ID and Xpath of key functions.

Honey violates network policies

MegaLag’s video show violations of these network policies.  I see three clusters of violations.

(1) Honey invokes its affiliate links although users did not fairly request any such thing.  Consider “We searched for you but didn’t find any deals” with button labeled “Got it” (MegaLag scenario iii above). “Got it” doesn’t indicate that the user wants, expects, or agrees that Honey will invoke its affiliate link.  That’s certainly misleading (contrary to rule C1).  Nor can Honey claim that a user who clicks “Got it” is “knowingly taking an action that results in the cookie being placed” (R5) because clicking “Got it” isn’t the kind of action that rule contemplates.  Rakuten rules R6 and R7 are equally on point, disallowing invoking an affiliate link based on an activity that doesn’t indicate intent (such as a mouseover), and requiring that an affiliate link only be invoked “once the end user clicks on a specific offer.”  “Got it” isn’t an offer, so under R7, that’s not grounds for invoking a Rakuten link.  So too for Awin, where A1 defines “click” to include only links that are “part of marketing services” (but “Got it” is not marketing service).  See also A2 and A3 (allowing links only as part of “ad media”, but “Got it” is not ad media); and of course A4 (“do not mislead consumers”).

Honey’s invocation of affiliate links upon a “Get rewarded with PayPal” message (MegaLag scenario iv above) is on similarly shaky ground.  For example, responding to a PayPal offer is not “knowingly taking an action that results in the cookie being placed” (R5) – the user knows only that he’s closing the message, not that he’s requesting an affiliate referral back to the merchant.  Similarly, a PayPal offer is not “marketing services” or “ad media” for an Awin merchant (rules A1-A3).

The rule to invoke affiliate links only when a user so requests is no mere technicality.  In affiliate marketing, an affiliate may be paid if 1) the user sees a link, 2) the user clicks the link, and 3) the user buys from the specified merchant.  Skipping step 2 sharply increases the circumstances in which a merchant has to pay commission—not a term a merchant would agree to.  When an affiliate skips step 2, it’s cookie-stuffing.  Publishers have gone to jail for this (and had to pay back commissions received).  Honey didn’t quite stuff cookies as that term is usually used—the user did click something.  But when nothing on the button (not its label, not the surrounding message, not any principle of logic or engineering) indicates or even suggests the button will activate an affiliate link—that’s terrible value for the merchant.

(2) Honey presents its affiliate links although a user recently clicked through another publisher’s offer.  (MegaLag at 2:50)  But networks’ rules require Honey to stand down if another publisher has made a referral.  See rule C5.v (“non-interference with competing advertiser/ publisher referrals”) and R2 (“Stand down when it recognizes any publisher links”).  Rakuten even makes explicit that the stand-down obligation applies not just to automatic clicks (which, uh, aren’t permitted in any event) but also to sliders and popups: “The DSA must stand-down and not display any forms of sliders or pop-ups to prompt activation if another publisher has already referred an end user.” (R4)

Here too, this is no technical violation.  Other publishers need “stand down” rules so they have a fair chance to earn commission for their work promoting a given merchant.  Standing down from another affiliate’s click is the most fundamental affiliate network rule for downloadable software and browser plug-ins.

(3) Honey falls short of disclosure obligations.  “You must accurately, clearly and completely describe all promotional methods by selecting the appropriate descriptions and providing additional information when necessary” (C3).  Publishers must provide “transparency about traffic sources and the environment that ads are displayed in” (A5).  I’m open to being convinced that Honey told networks and merchants it would invoke affiliate links with buttons as weakly labeled as “Got it.”  I don’t buy it.  Merchants have a clear contractual basis to expect complete and forthright disclosures—it is literally their money being paid out.  And merchants authorized networks to collect and evaluate these disclosures for them.  No shortcuts.

One might object that networks can waive rules or create exceptions for key partners.  Not so fast!  Merchants and publishers rely on networks to enforce their published rules exactly as promised.  In fact, in 2007, both merchants and publishers sued ValueClick to allege that it had been less than diligent in enforcing its rules.  ValueClick’s Motion to Dismiss argued that it could do what it wanted, that it had disclaimed all warranties, and that it made no promises that merchants or publishers were entitled to rely on.  But the court denied ValueClick’s motion, eventually yielding a settlement requiring both improved efforts to detect affiliate fraud as well as certain refunds to merchants and payments to publishers.  There’s room to disagree about how much benefit the settlement delivered.  (Maybe the settlement promised changes that ValueClick was going to do anyway.  Maybe the monetary payments were a small fraction of the amount lost by merchants and publishers.)  But the fundamental principle was clear: Networks must follow their contractual representations including policies about prohibited behaviors.  And while networks may try to disavow quality responsibilities, for example via disclaimers in contracts, courts are skeptical of the unfettered discretion these provisions purport to create.  A network that promises to track affiliate transactions ultimately ought to do so accurately, and should neither grant arbitrary waivers nor look the other way about serious misconduct.

How did we get here?

Honey’s one-sentence response to MegaLag was “Honey follows industry rules and practices, including last-click attribution.”  It’s no surprise that Honey claims compliance.  But I was surprised to see affiliate thought-leaders agree.  For example, long-time affiliate expert Brook Schaaf remarked “Honey appears to be in compliance with network standards.”  Awin CEO Adam Ross says MegaLag’s video “portray[s] performance marketing attribution as a form of theft or scam”—suggesting that he too thinks Honey did nothing wrong.

I’ll update this piece with when others dig into the contracts and compare Honey’s practices with the governing requirements.  But after more than 20 years working on affiliate fraud—my first piece on this subject was, wow, 2004—let me offer four observations.

One, it’s easy to get complacent.  Much of what Honey does is distressingly normal among browser extensions.  Test the Rakuten Cashback app and you’ll find much the same thing.  Above, I linked to litigation against Honey, but there’s also now similar litigation against Capital One, alleging that its Capital One Shopping browser extension does much the same.  Brook and Adam are right that Honey’s tactics aren’t a surprise to anyone who’s been in the industry for decades.  Many people have come to accept behaviors that don’t follow the literal meaning of stated policies.

Two, networks’ incentives are mixed.  On one hand, networks want affiliate marketing to be seen as trusted and trustworthy, which requires eliminating practices widely seen as unfair.  At the same time, affiliate networks typically charge a commission on every dollar of commission paid.  As a result, networks directly benefit from anything that increases the number of dollars of commission paid—such as allowing browser plug-ins to change noncommissionable traffic into commissionable traffic.  Merchants should be skeptical of networks too quickly declaring traffic compliant when networks literally get paid for that finding.  With Rakuten operating both a cashback service (with browser plugin) and an affiliate network, their incentives are particularly muddy: If Rakuten Advertising declares a given browser plugin tactic to be permitted, Rakuten Cashback can then use that tactic, increasing both Cashback fees (the Cashback margin on each dollar of rebate) and Advertising fees (the network margin on each dollar of affiliate activity).  I like and respect Rakuten and its leaders, but their complicated incentives mean serious people should give their pronouncements a second look.

Three, most people read the governing contracts hastily if at all.  I’m proud to have pulled out the 17 rules above, and I encourage readers to follow my links to see these and other rules in the larger policy documents.  Fact is, there’s lots of material to digest.  I’ve found that networks’ compliance teams often build rules of thumb that diverge from what the rules actually say, and ignore rules that are in some way seen as inconvenient or overly restrictive.  To me, all this is a mistake.  The rules may not be holy, but they have the force of contract, and there’s real money at issue.  Networks are spending other people’s money­­­—making sure normal publishers get every dollar they fairly earned; and making sure merchants pay the correct amount, but not a penny more.  This calls for a high level of care.  We’re two weeks into the response to MegaLag.  How many people posted video-responses, blogs, or other remarks without finding, reading, and applying the governing policies?

Four, personalities and work styles invite even merchant staff to accept what Honey is doing.  Representative short-hand: “Go along to get along.”  Many marketers chose this line of work to make connections, not to play policeman.  Attend an affiliate marketing conference and you’re a lot more likely to see DJs and beer (party!) than network sniffers and virtual machines (forensic tools).  Meanwhile, it’s awfully easy for an affiliate manager to tell a boss “we’re working with Honey, the billion-dollar product from PayPal”—then head to the Honey gala at an industry conference.  Conversely, consider the affiliate manager who has to explain “we wasted $50k on Honey last month.”  People have been fired for less.  Ultimately, online marketing plays a procurement function—trying to spend an employer or client’s money as skillfully as possible, to get as much benefit as possible for as little expenditure as possible.  But that’s hard work.  I don’t fault those who want an easier path.  And I don’t fault those who prefer the networking and gala side of marketing over the software forensics.  Nonetheless, collective focus elsewhere goes a long way towards explaining how problems can linger for years.

Is Honey profitable for merchants?

For a merchant evaluating Honey, the fundamental question is pretty simple: Does Honey bring the merchant incremental sales and positive ROI?  Clearly Honey’s browser extension positions it to claim credit on purchases users were already going to make, but incremental sales are what matter to merchants—purchases made only thanks to Honey.

My hypothesis is that Honey is ROI negative for most merchants.  If a user goes to (say) dell.com, the user is already interested in Dell.  Why should Dell let Honey’s browser plug-in jump in and claim a commission on that user’s purchase?  Maybe Honey will increase the user’s conversion rate from 5% to 5.1% (by proclaiming what a good deal the user has found, or by touting a Honey Gold sweetener).  But with payment to Honey, Dell’s margin will drop from (say) 7% to 5%.  Would Dell prefer 7% profit on 500 sales, or 5% profit on 510?  That math is pretty easy.

Of course the numbers in the preceding paragraph are just hypotheticals.  If users sufficiently trust Honey (whether correctly or otherwise), their conversion rate might increase enough to justify Honey’s fees to merchants.  If Honey could somehow persuade users to spend more—“add one more item to your cart, and you can get this $10 coupon”—that could increase value to merchants too (though I’ve never seen Honey deliver such a message).  Some merchant advisors think this is plausible.  I have my doubts.

Alarmingly, many merchants decide to work with Honey (and other “loyalty” software) without rigorously measuring incrementality (or even trying).  Most merchants take some steps to measure the ROI of search and display ads.  For years, affiliate ROI has been more challenging.  But I recently devised a rigorous method that’s doable for most merchants.  I’d enjoy discussing with anyone interested.  When I have findings from a few merchants, with their permission I’ll share aggregate results.

Looking ahead

It’s easy to watch MegaLag’s piece and come out sour on affiliate marketing.  (“What a mess!”)  For that matter, the affiliate marketing section of my site has 28 articles over 20+ years, almost all about some violation or abuse.

Yet I am fundamentally a fan of affiliate marketing.  Incentives aren’t perfectly aligned between affiliate, network, and merchant, but they’re a whole lot closer than in other kinds of online advertising.  One twist in affiliate is that when a rogue affiliate finds a loophole, they can often exploit it at scale—by some indications, even more so than in other kinds of online advertising.  Hence the special importance of networks and merchants both providing fairness and being perceived as providing fairness.  MegaLag’s critique of Honey shows there’s no shortage of work to do.

The Design of Online Advertising Markets

Edelman, Benjamin. “The Design of Online Advertising Markets.” Chap. 15 in The Handbook of Market Design, edited by Nir Vulkan, Alvin E. Roth, and Zvika Neeman. Oxford University Press, 2013.

Because the market for online advertising is both new and fast-changing, participants experiment with all manner of variations. Should an advertiser’s payment reflect the number of times an ad was shown, the number of times it was clicked, the number of sales that resulted, or the dollar value of those sales? Should ads be text, images, video, or something else entirely? Should measurement be performed by an ad network, an advertiser, or some intermediary? Market participants have chosen all these options at various points, and prevailing views have changed repeatedly. Online advertising therefore presents a natural environment in which to evaluate alternatives for these and other design choices. In this piece, I review the basics of online advertising, then turn to design decisions as to ad pricing, measurement, incentives, and fraud.

The Online Ad Scams Every Marketer Should Watch Out For

The Online Ad Scams Every Marketer Should Watch Out For. HBR Online. October 13, 2015.

Imagine you run a retail store and hire a leafleteer to distribute handbills to attract new customers. You might assess her effectiveness by counting the number of customers who arrived carrying her handbill and, perhaps, presenting it for a discount. But suppose you realized the leafleteer was standing just outside your store’s front door, giving handbills to everyone on their way in. The measured “effectiveness” would be a ruse, merely counting customers who would have come in anyway. You’d be furious and would fire her in an instant. Fortunately, that wouldn’t actually be needed: anticipating being found out, few leafleteers would attempt such a scheme.

In online advertising, a variety of equally brazen ruses drain advertisers’ budgets — but usually it’s more difficult for advertisers to notice them. I’ve been writing about this problem since 2004, and doing my best to help advertisers avoid it.

In this piece for HBR Online, I survey these problems in a variety of types of online advertising — then try to offer solutions.

Accountable? The Problems and Solutions of Online Ad Optimization

Edelman, Benjamin. “Accountable? The Problems and Solutions of Online Ad Optimization.” IEEE Security & Privacy 12, no. 6 (November-December 2014): 102-107.

Online advertising might seem to be the most measurable form of marketing ever invented. Comprehensive records can track who clicked what ad–and often who saw what ad–to compare those clicks with users’ subsequent purchases. Ever-cheaper IT makes this tracking cost-effective and routine. In addition, a web of interlocking ad networks trades inventory and offers to show the right ad to the right person at the right time. It could be a marketer’s dream. However, these benefits are at most partially realized. The same institutions and practices that facilitate efficient ad placement can also facilitate fraud. The networks that should be serving advertisers have decidedly mixed incentives, such as cost savings from cutting corners, constrained in part by long-run reputation concerns, but only if advertisers ultimately figure out when they’re getting a bad deal. Legal, administrative, and logistical factors make it difficult to sue even the worst offenders. And sometimes an advertiser’s own staff members prefer to look the other way. The result is an advertising system in which a certain amount of waste and fraud has become the norm, despite the system’s fundamental capability to offer unprecedented accountability.

Pitfalls and Fraud in Online Advertising Metrics: What Makes Advertisers Vulnerable to Cheaters, and How They Can Protect Themselves

Edelman, Benjamin. “Pitfalls and Fraud in Online Advertising Metrics: What Makes Advertisers Vulnerable to Cheaters, and How They Can Protect Themselves.” Journal of Advertising Research 54, no. 2 (June 2014): 127-132.

How does online advertising become less effective than advertisers expect and less effective than measurements indicate? The current research explores problems that result, in part, from malfeasance by outside perpetrators who overstate their efforts to increase their measured performance. In parallel, similar vulnerabilities result from mistaken analysis of cause and effect–errors that have become more fundamental as advertisers target their advertisements with greater precision. In the paper that follows, the author attempts to identify the circumstances that make advertisers most vulnerable, notes adjusted contract structures that offer some protections, and explores the origins of the problems in participants’ incentives and in legal rules.

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.)

A Holiday “Top 10”: Rogue Affiliates at Commission Junction and LinkShare with Wesley Brandi

Our automation continuously scours the web for rogue affiliates. In our query tool, we provide a basic sense of how much we’ve found. We have also written up scores of sample rogue affiliates, but the holiday season provides an impetus for more: Thanks to high online spending, affiliate fraud at this time of year is particularly profitable for perpetrators — and particularly costly to merchants.

In today’s article, we report the ten Commission Junction affiliates and ten LinkShare affiliates most often seen by our automation. Our findings:

Twenty Oft-Found Commission Junction and LinkShare Affiliate Violations

Affiliate Fraud Litigation Index

Some analysts view affiliate marketing as “fraud-proof” because affiliates are only paid a commission when a sale occurs. But affiliate marketing nonetheless gives rise to various disputes — typically, merchants alleging that affiliates claimed commission they had not properly earned. Most such disputes are resolved informally: merchants withhold amounts affiliates have purportedly earned but have not yet received. Occasionally, disputes end up in litigation with public availability of the details of alleged perpetrators, victims, amounts, and methods.

In today’s posting, I present known litigation in this area including case summaries and primary source documents:

Affiliate Fraud Litigation Index

Flash-Based Cookie-Stuffer Using Google AdSense to Claim Unearned Affiliate Commissions from Amazon with Wesley Brandi

Merchants face special challenges when operating large affiliate marketing programs: rogue affiliates can claim to refer users who would have purchased from those merchants anyway. In particular, rogue “cookie-stuffer” affiliates deposit cookies invisibly and unrequested — knowing that a portion of users will make purchases from large merchants in the subsequent days and weeks. This tactic is particularly effective in defrauding large merchants: the more popular a merchant becomes, the more users will happen to buy from that merchant within a given referral period.

To cookie-stuff at scale, an attacker needs a reliable and significant source of user traffic. In February we showed a rogue affiliate hacking forum sites to drop cookies when users merely browse forums. But that’s just one of many strategies. I previously found various cookie-stuffing on sites hoping to receive search traffic. In a 2009 complaint, eBay alleges that rogue affiliates used a banner ad network to deposit eBay affiliate cookies when users merely browsed web pages showing certain banner ads. See also my 2008 report of an affiliate using Yahoo’s Right Media ad network to deposit multiple affiliate cookies invisibly — defrauding security vendors McAfee and Symantec.

As the eBay litigation indicates, display advertising networks can be a mechanism for cookie-stuffing. Of course diligent ad networks inspect ads and refuse cookie-stuffers (among other forms of malvertising). So we were particularly surprised to see Google AdSense running ads that cookie-stuff Amazon.

The 'Review Different Headphones' ad actually drops Amazon Associates affiliate cookies.
This innocuous-looking banner ad sets Amazon Associates cookies invisibly.
The Imgwithsmiles attack

We have uncovered scores of web sites running the banner ad shown at right. On 40 sites, on various days from February 6 to May 2, our crawlers found this banner ad dropping Amazon Associates affiliate cookies automatically and invisibly. All 40 sites include display advertising from Google AdSense. Google returns a Flash ad from Imgwithsmiles. To an ordinary user, the ad looks completely innocuous — the unremarkable “review different headphones” image shown at right. However, the ad actually creates an invisible IMG (image) tag loading an Amazon Associates link and setting cookies accordingly. Here’s how:

First, the ad’s Flash code creates an invisible IMG tag (10×10 pixels) (yellow highlighting below) loading the URL http://imgwithsmiles.com/img/f/e.jpg (green).

function Stuff() {
  if (z < links.length) {
    txt.htmltext = links[z];
    z++;
    return(undefined);
  }
  clearinterval(timer);
}
links = new array();
links[0] = "<img src="http://imgwithsmiles.com/img/f/e.jpg" width="10" height="10"/>";z = 0;timer = setinterval(Stuff, 2000);

While /img/f/e.jpg features a .jpg extension consistent with a genuine image file, it is actually a redirect to an Amazon Associates link. See the three redirects preserved below (blue), including a tricky HTTPS redirect (orange) that would block many detection systems. Nonetheless, traffic ultimately ends up at Amazon with an Associates tag (red) specifying that affiliate charslibr-20 is to be paid for these referrals.

GET /img/f/e.jpg HTTP/1.0
Accept: */*
Accept-Language: en-US
Referer: http://pagead2.googlesyndication.com/pagead/imgad?id=CICAgICQvuXgahDQAhiYAjII3bQHU19r_Isx-flash-version: 10,3,183,7User-Agent: Mozilla/4.0 (compatible; MSIE 8.0; ...)Host: imgwithsmiles.comConnection: Keep-AliveHTTP/1.1 302 Moved TemporarilyDate: Wed, 02 May 2012 19:56:59 GMTServer: Apache/2.2.21 (Unix) mod_ssl/2.2.21 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4
X-Powered-By: PHP/5.2.17
Expires: Thu, 19 Nov 1981 08:52:00 GMT
Cache-Control: no-store, no-cache, must-revalidate, post-check=0, pre-check=0
Pragma: no-cache
Set-Cookie: PHPSESSID=174272468a212dd0862eabf8d956e4e0; path=/
Location: https://imgwithsmiles.com/img/kick/f/e.jpg
Content-Length: 0
Keep-Alive: timeout=5, max=100
Connection: Keep-Alive
Content-Type: text/html-

HTTPS redirect decoded via separate manual request
GET /img/kick/f/e.jpg HTTP/1.1 Accept: text/html, application/xhtml+xml, */* Accept-Language: en-US User-Agent: ... Accept-Encoding: gzip, deflate Host: imgwithsmiles.com Connection: Keep-AliveHTTP/1.1 302 Moved Temporarily Date: ... Server: Apache/2.2.21 (Unix) mod_ssl/2.2.21 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4 X-Powered-By: PHP/5.2.17 Location: http://imgwithsmiles.com/img/t/f/e.jpg Content-Length: 0 Connection: close Content-Type: text/html-GET /img/t/f/e.jpg HTTP/1.0 Accept: */* Accept-Language: en-US x-flash-version: 10,3,183,7 User-Agent: Mozilla/4.0 (compatible; ...) Connection: Keep-Alive Host: imgwithsmiles.com Cookie: PHPSESSID=174272468a212dd0862eabf8d956e4e0HTTP/1.1 302 Moved TemporarilyDate: Wed, 02 May 2012 19:56:59 GMT Server: Apache/2.2.21 (Unix) mod_ssl/2.2.21 OpenSSL/0.9.8e-fips-rhel5 mod_bwlimited/1.4 X-Powered-By: PHP/5.2.17 Expires: Thu, 19 Nov 1981 08:52:00 GMT Cache-Control: no-store, no-cache, must-revalidate, post-check=0, pre-check=0 Pragma: no-cache Location: http://www.amazon.com/gp/product/B002L3RREQ?ie=UTF8&tag=charslibr-20 Content-Length: 0 Keep-Alive: timeout=5, max=99 Connection: Keep-Alive Content-Type: text/html

If a user happens to make a purchase from Amazon within the subsequent 24 hours, Amazon will pay a commission to this affiliate — even though the affiliate did nothing at all to cause or encourage the user to make that purchase.

Does Amazon know?

The available information does not reveal whether or not Amazon knew about this affiliate’s practices. Nor can we easily determine whether, as of the May 2, 2012 observations presented above, this affiliate was still in good standing and receiving payment for the traffic it sent to Amazon.

On one hand, Amazon is diligent and technically sophisticated. Because Amazon runs one of the web’s largest affiliate programs, Amazon is necessarily familiar with affiliate fraud. And Amazon has ample incentive to catch affiliate fraud: Every dollar paid to fraudulent affiliates is money completely wasted, coming straight from the bottom line.

On the other hand, we have observed this same affiliate cheating Amazon for three months nonstop. All told, we’ve seen this affiliate rotating through 49 different Associates IDs. If Amazon had caught the affiliate, we would have expected the affiliate to shift away from any disabled affiliate accounts, most likely by shifting traffic to new accounts. Of the 28 Associates IDs we observed during February 2012, we still saw 6 in use during May 2012 (month-to-date) — suggesting that while Amazon may be catching some of the affiliate’s traffic, Amazon probably is not catching it all.

A further indication of the affiliate’s earnings comes from the affiliate’s willingness to incur out-of-pocket costs to buy media (AdSense placements from Google) with which to deliver Amazon cookies. As best we can tell, Amazon is the affiliate’s sole source of revenue. Meanwhile, the affiliate must pay Google for the display ad inventory the affiliate receives. These direct incremental costs give the affiliate a clear incentive to cease operation if it concludes that payment from Amazon will not be forthcoming. From the affiliate’s ongoing actions we can infer that the affiliate finds this scheme profitable — that its earnings to date have exceeded its expenses to date.

How profitable is this affiliate’s attack? Conservatively, suppose 40% of users are Amazon shoppers and make an average of four purchases from Amazon per year. Then 0.4*4/365=0.44% of users are likely to make purchases from Amazon in any given 24-hour period. Suppose the affiliate buys 1,000,000 CPM impressions from Google. Then the affiliate will enjoy commission on 0.44%*1,000,000=4,384 purchases. At an average purchase size of $30 and a 6.5% commission, this would be $8,547 of revenue per million cookie-stuffing incidents. How much would the affiliate have to pay Google for 1,000,000 CPM impressions? We’ve seen this affiliate on a variety of sites, but largely sites in moderate to low-priced verticals. At $2 CPM, the affiliate’s costs would be $2,000 — meaning the affiliate would still be slightly profitable even if Amazon caught 3/4 of its affiliate IDs before the first payment!

We alerted our contact at Amazon Associates to our observations. We will update this post with any information Amazon provides.