For decades now, AI technologies have played an increasingly critical role in the evolution of advertising. In recent years, AI’s ability to personalize and optimize campaigns has been particularly game-changing for marketers, helping them unlock efficiencies and tailor their targeting in real-time.
But, with more businesses in the region investing in AI solutions, it is increasingly crucial to foster an understanding of the potential pitfalls of these solutions.
For marketers, one of the biggest challenges is the ‘AI black box’, or lack of visibility over how exactly their AI solutions are making decisions to target and optimize. A challenge that is costing businesses through not only wasted ad spend and diminishing returns but also vulnerability to ad fraud.
The Problem With AI Black Box Models
The term “AI black box” refers to the inherent opacity or lack of transparency in certain advanced machine-learning models. While these models can make highly accurate predictions, understanding the specific reasons behind those predictions can be challenging. For marketers, this problem applies across all AI and machine learning-driven advertising platforms – whether originating from Google, the Trade Desk, Yahoo, or numerous others — where they remain at the mercy of the all-mighty algorithm.
To illustrate, when Google announced Performance Max (PMax), it seemed like the answer to every marketer’s dream – drive marketing efficiency, performance, and better ROI across all Google’s channels including YouTube, Search, Shopping, and Discovery. At the core of the PMax promise was Google’s AI, making decisions on everything from bidding, to creative, to search query matching and media environments.
Read More: AiThority Interview with Keri Olson, VP at IBM IT Automation
But Pmax’s limited granular reporting means that while marketers get broad campaign insights, you won’t be able to see if it’s display, search, video, or shopping ads driving your clicks and conversions. This would be great if the world was perfect or you only spent money on Google channels – but marketing is complex, with multiple media partners in any one campaign and we all know from experience that industry opacity can be exploited to the detriment of marketers and their budgets.
Why the Black Box Is a Bigger Problem Than Advertisers Think
Transparency and accountability are indispensable for marketers to operate with utmost effectiveness. And particularly in the realm of traditional programmatic, where invalid traffic and click fraud occur on a daily basis, spanning search, mobile, and affiliate campaigns.
In our research examining the issues surrounding invalid traffic in search campaigns, we’ve determined that roughly 5-15% of search clicks are classified as invalid traffic. This type of traffic typically stems from disengaged users or bots, and it neither results in conversions nor contributes positively to resource allocation for targeting potential prospects. In certain instances, it may even be a deliberate tactic to deplete search budgets, presenting a competitive challenge.
Furthermore, we have observed a concerning trend in the rise of affiliate fraud. This includes cases of click fraud and affiliate cookie stuffing, often facilitated by malicious browser extensions. These fraudulent activities result in substantial monthly losses in affiliate payouts, adding another layer of complexity to the issue.
It’s not surprising that malicious actors have shifted their focus from general programmatic fraud to specifically targeting campaigns lacking insights. PMax is no exception to this trend and currently faces vulnerability to both established and emerging fraud tactics. For instance, akin to most AI systems, PMax operates under the assumption that every user engagement carries a positive intent.
However, when bad actors manipulate or introduce invalid traffic that contaminates the system with counterfeit intent signals, it can inadvertently train the algorithm to optimize toward the source of this invalid traffic. While this isn’t a deliberate error on Google’s part, AI that optimizes for invalid traffic can result in budget exploitation, particularly when transparency and real-time third-party oversight and intervention are lacking.
How Brands Can Protect Against the Black Box Problem
PMax is simply a microcosm of what happens across the entire internet — when marketers employ AI buying systems without independent third-party auditing and analysis, they run the risk of dumping money into a high-speed invalid traffic machine.
Transparency remains key in protecting against the challenges of black box models. Whether it’s PMax or any other AI-led solutions, marketers must push for algorithmic transparency, invest in independent oversight, and not blindly trust the little black box of algorithms if they truly want to drive the best fraud-free performance.
Comments are closed.