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It’s been well-established that fraud, and in particular non-human traffic, is a problem in the digital advertising industry, but I’d like to spend a few moments exploring why it is such a problem. No, I’m not asking why there are unscrupulous people out there looking to hack the system to make a dishonest buck (that part I recognize from every other commercial endeavor ever undertaken). And no, I’m not asking about the industry norms and perverse incentives that can motivate publishers, intermediaries, and yes, even agencies and advertisers, to turn a blind eye to the problem. I’m asking why our marketing programs are so easily fooled by bots in the first place.

There’s no doubt the fraudsters are getting more sophisticated. While long-standing tactics like click fraud are still sadly alive and thriving, they have been joined by numerous other insidious new breeds of fraud. From visiting advertiser sites to attract retargeting dollars, to intentionally adhering to MRC-defined viewability criteria, the bots are getting better at blending in and looking like everyone else. No channel is unaffected and no publisher, no matter how premium or niche, is immune.

This state of affairs has led to a reactive mentality in our industry where the goal is to “avoid fraud,” which is completely rational and understandable. When you are under attack, you defend yourself. It’s why many major publishers, ad exchanges, and SSPs have implemented rigorous quality measures to filter fraud and other forms of undesirable traffic at the source. It’s why the leading DSPs have developed sophisticated algorithms to identify anomalous patterns at the user, site, IP address, and other levels, and quarantine fraud away from live buying environments before marketing budgets are exposed to it. And it’s why a wave of old and new verification and measurement vendors are offering an array of new fraud-related products. All with the determination to stay a step ahead of the increasing scale and growing variety of digital advertising fraud.

But is that it? As an industry, are we just to spiral forward in a never-ending arms race, trying to build new techniques to keep up with the ever-evolving new strains of fraud, playing a high-stakes game of whack-a-mole with hundreds of billions of dollars on the line?

Thankfully, the story doesn’t have to end there.

The reason it’s so easy for bots to mimic people is because the marketing definition of people is often so simplistic. Despite all the amazing advances in ad tech over the past decade, many digital campaigns are still just going after weakly defined audiences characterized by generic demographic and/or broad-based behavioral targeting, overlaid with easily-mimicked behaviors like views and clicks. These approaches are, in effect, propagating the broadcast mentality of the old offline world, where targeting “18-49 year-old males who make over $100,000 per year and are interested in electronics” might have been considered pretty decent (and pretty hard to fake, from an offline standpoint). But in the online world that’s about as easy to fake as the age on your dating profile.

Starting with a generic picture of a very broad audience is what I refer to as “guess-based marketing.” Those audiences, whether defined by characteristics like age, gender, and income (the estimation of which is often of dubious quality to begin with) or by simple behaviors like visiting sites or clicking on ads, are really just proxies to the advertiser’s desired business outcomes. The problem is those characteristics are easy for bots to fake and those behaviors are easy for bots to demonstrate, so a guess-based approach is playing right into the fraudsters’ sweet spot. If you’re doing that, you are broadcasting a signal that bots are tuned into, and you should work with a buy-side partner well-equipped to fight the fraud arms race with you, who combines proven proprietary pre-bid fraud detection and filtration with best-in-class third-party technologies.

But simply playing defense is not truly taking advantage of what programmatic is all about. The real power of programmatic is that it enables what I call “goal-based marketing.” Goal-based marketing is about applying the principles of marketing science across the entire funnel, with the realization that all marketers are performance marketers. What I mean by that is: No matter whether you are a brand marketer, a direct-response marketer, a loyalty marketer, etc., there is some quantifiable business goal you are looking to drive (whether brand awareness, purchase intent, social engagement, customer loyalty, lifetime value, you name it), and against which you are judging success. And therein is the key to goal-based marketing: If it can be measured, it can be made better by math. Made better by exposing all available data – about audiences, about media, about creatives – to a smart system that can determine the optimal combination of those elements to drive your business goals at scale, automating the right decision at every consumer touchpoint, in real time.

If you are using programmatic technology to drive goal-based marketing, the fraud picture becomes very different. It shifts from a purely defensive and reactive mentality of “avoid fraud” to a proactive posture of “generate business outcomes.” The fact that bots are getting better at blending in and looking like everyone else is suddenly not their strength but rather their weakness, because your customers are not just like everyone else’s and the goals you are trying to drive are not the same as everyone else’s. Browsing and clicking are easy markers to fake, but the combined online and offline data you use to define truly actionable audiences, the category-, brand-, and product-specific behaviors that become the triggers for your marketing actions, and the specific and measurable outcomes that matter to you as a marketer — these are things not known to the fraudsters and therefore much harder for bots to fake (not to mention economically infeasible, in the case of actual purchases). Moreover (and somewhat ironically), those true business outcomes are often more accurately and reliably measurable than the easily spoofed, guess-based audiences that were supposed to be the proxies for those outcomes in the first place.

A guess-based approach might simply be looking to buy those 18-49 year-old males who make over $100,000 per year and are interested in electronics — an easy target for bots to fake. (It’s also worth noting that even in a bot-free world, the accuracy of that kind of data is often extremely poor, based on coarse extrapolations from very limited data). By contrast, a goal-based approach might look to raise awareness for a particular brand by X percent. Or do so specifically among consumers who have actually purchased a competitive brand, online or offline, in the past year. Or increase purchase intent among lapsed customers by X percent. Or drive conversion of consumers who have expressed interest in a particular category or product, at an average $X cost per conversion. Or drive an overall return on ad spend of greater than X:1 from combined online and offline sales. Or convert X percent of current customers to a loyalty program every month. And so on. Bots won’t easily show up in those audience definitions and won’t easily contribute to those outcomes. The avoidance of fraud simply becomes a natural consequence of goal-based marketing. And achieving your business goals at scale is what programmatic is all about.

Moreover, non-human traffic isn’t the only kind of fraud addressed by the use of goal-based marketing. Many of the various types of ad laundering and publisher misrepresentation tactics that can be perpetrated by malware or other forms of browser manipulation, even on the browsers of actual people, are also minimized. Common examples include “invisible ads” (either stacked atop each other or rendered as an invisible 1×1 pixel), or the impersonation of legitimate publishers via “URL masking.” But since ads never actually rendered to a user don’t drive true business outcomes, and impostor sites don’t actually drive business outcomes like the legitimate publishers they are spoofing, goal-based techniques naturally optimize away from such traffic and towards the quality environments that do generate those outcomes.

The evidence is in the data: Goal-based campaigns see no inhumanly high click-through rates, no droves of site visitation with little to no engagement, no lack of bona fide purchase events — all things commonly associated with fraudulent activity. Moreover, when fraudulent publishers are outed in the press, these campaigns see little to no delivery against such publishers. When conversion events have some post-conversion measure of quality, these campaigns strongly outperform their guess-based counterparts.

That’s not to say it’s an either/or proposition.  The best results, by far, come when you combine goal-based marketing with powerful pre-bid anti-fraud technology. A guess-based approach invites an onslaught of fraud to begin with, relying solely on anti-fraud measures to take things from bad to good. By contrast, a goal-based approach aligned with your true business objectives intrinsically blunts the onslaught of fraud so you’re starting at good, targeting audiences which bots can’t easily resemble and outcomes bots can’t easily reproduce. The overlay of industry-leading anti-fraud technology atop a goal-based approach then imposes additional filters to take good to great.

We who build anti-fraud solutions believe the good guys will win the arms race — through technology, through the definition of standards amd policies, through education, and through industry-wide data-sharing, transparency, and collaboration. In the meantime, fraud will continue to be a problem in the digital advertising industry, just a much smaller problem for those using programmatic technology built to drive goal-based marketing.

Ari Buchalter is president, technology at MediaMath

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