In this episode, Praneet and Shailin return to the show to discuss how advertising fraud is getting worse–not better. Praneet and Shailin worked with BuzzFeed reporter Craig Silverman, who was a previous guest on the show to talk about his remarkable findings about mobile advertising fraud, which accounts for hundreds of millions of dollars in theft every year.
We have released a demonstration of how web traffic can rapidly be generated using AWS EC2. In just 5 minutes and a few mouse clicks, we generated over 1000 concurrent visitors from 290 cities around the world.
In this case, the traffic was sent to a site maintained by Method Media Intelligence with no advertisements. But everyday, data center traffic is directed around the web to consume advertising and causes financial harm to advertisers.
Method Media Intelligence offers Proactive Auditing as a solution to this scenario. Proactive Auditing is a real-time auditing service which prevents ads from being rendered (and paid for) if a site is visited by a data center. Contact info@methodmi to learn more.
Introduction - How Sampling is Currently Used in Ad Verification
Sampling is used to estimate a characteristic of a larger population. Sampling is an appropriate method when direct measurement of the entire population is overly burdensome or impossible to do within the required time. Sampling is not appropriate in cases where every item in a data set can be measured quickly and cheaply.
Ad-verification vendors sample impressions for two reasons:
The length of the verification process is longer than the auction cycle of selling an ad-impression. In this case, analyzing 100% of impressions would significantly hinder or prevent ad-delivery by causing timeouts* and non-renders.
Verification methods rely on computationally expensive behavioral checks. To reduce costs, vendors measure a subset of supply to provide acceptable pricing to clients.
*In all cases of time-outs, advertisers lose opportunities to reach consumers. If an advertiser is not on ad-server billing, they will be paying for each non-render event.
Types of Users
Internet users can be divided into three categories, human users (48%), “good” bots (23%), and “bad” bots (29%). Publishers create value and generate revenue by selling their online real estate to advertisers, who pay for access to human users’ eyes. Advertisers lose when those ads are displayed to bots, both good and bad.
“Good” bots have numerous legitimate uses. For example, good bots include search engine crawlers that are critical for keeping the internet running smoothly. Good bots primarily operate from data centers, as opposed to human-operable machines.
If websites blocked search engine bots, their content would disappear from search results and the website would lose many human users overnight. If search engine crawlers stopped visiting websites, the search engine would lose its value. If your website shows up in a Google search, it is only because the Googlebot has visited your site. Therefore, it is necessary for these bots to visit websites and the digital media industry must adapt to find a way to prevent ads from being served to them.
“Bad” bots include content scrapers, headless browsers, botnets, and other unwanted visitors to a site. Like good bots, they consume advertising and server resources, but they offer no benefit in return. Bad bot traffic can originate from data centers or human-operable devices. Effective ad-tech partners prevent their clients’ media spend from being consumed by “bad” bots.
In summary, advertisers, agencies and publishers must accept that both good and bad bots will be visiting their websites. Those serving the interests of advertisers must focus on how to measure and prevent the delivery of ads to both good and bad bots. Each ad viewed by a bot is a waste of advertiser funds and publisher resources. Advertisers must not be billed for impressions viewed by good or bad bots.
The Problem: How Advertisers Lose When Using Sampled Data
Imagine the following scenario:
An advertiser’s agency enlists a verification vendor to monitor its digital media spend. The vendor monitors a $1M campaign and samples 10% of impressions. Of those 10%, the advertiser measured 25% bot traffic (fraudulent).
The advertiser extrapolates the data and determines that 25% of their $1M campaign was spent on waste, and asks for a refund of $250k from its DSP.
The DSP refuses to refund the advertiser until it speaks with the SSP’s and Exchanges in its supply chain. The SSP’s states that the verification vendor’s sample cannot be guaranteed to be representative of the entire campaign.
The DSP also tells the advertiser that impressions are not all the same cost. 25% of impressions being fraudulent does not mean 25% of spend was wasted. The 25% waste could be on low CPM impressions, and the 75% on high CPM impressions. Therefore, a 25% refund could be excessive.
The advertiser does not have the data to refute these two points, and accepts the waste as “the cost of doing business”.
Method believes the following must be true to protect the interests of advertisers and avoid the above scenario:
Verification vendors only provide actionable analytics when verifying every impression.
Advertisers must have rapid access to full receipts of campaign spend (data for every impression).
As shown by the example above, only complete monitoring can be used to recover funds spent on waste. Analytics on every impression (cost, domain, IP, human/bot) are required to calculate the exact amount wasted advertiser spend. But before advertisers can be refunded for previous waste and prevent waste in future campaigns, they must first obtain this data.
Every hour of every day a million little crimes are committed online. And every time it happens, hundreds of legitimate businesses all over the world, with boards and shareholders and mission statements — some of them publicly listed — put the proceeds of those crimes in their own pockets. They do so knowingly.
Some people might consider it extraordinary that technology businesses — businesses that claim they can discern the intent of one buyer from a billion in milliseconds — somehow can’t recognize when millions of ads a month are served to a single unique user ID in, for instance, Belarus.
Method Media Intelligence's Shailin Dhar states that "when you study actual bot traffic in isolation as well as in live ad exchanges, you realize quickly that the truth can be described best as ‘take the industry estimates of the financial impact of fraud and multiply by ten.’”
Ad safety company Pixalate has uncovered what it says is a ‘sophisticated’ mobile app fraud potentially costing advertisers up to $75 million a year if allowed to run unchecked.
The company says it discovered that Android app, MegaCast which allows users to broadcast content to Google’s Chromecast, was using ‘mobile app laundering’ to generate fraudulent ad impressions.
Read commentary from Which 50's Andrew Birmingham about the Kerrisdale Capital report on QuinStreet. Kerrisdale Capital is a New York based hedge fund which focuses on short activism.
Andrew Birmingham interviewed Shailin Dhar of Method Media Intelligence for his view on QuinStreet. “Re-brokering and arbitrage are the main pillars of affiliate and performance marketing. The main reasons are that if you can create an image palatable enough for actual advertisers, you can go out and find 100 individual parties that will provide small amounts of traffic that can be aggregated to look like an attractive level to advertisers.", Shailin said.
In addition, he said, “advertisers will slowly but surely get wind of the fact that the party they purchase leads from usually has zero idea how they were generated. Lead scrubbing is essential; it was my first encounter with ad fraud in 2011.”