A recent study from the Association of National Advertisers (ANA) raised eyebrows with its finding that there was more than $20 billion of “ad waste” in global programmatic advertising—almost a quarter of the total spend.
But by my definition of “ad waste,” the ANA’s math is drastically understated—and a whole other set of root causes are involved. That’s the bad news. The good news is that there’s huge upside for marketers who get it right.
The ANA study pointed the finger at the use of fraudulent or deficient websites as a main cause of ad waste and said better data transparency and smarter media buying are needed. That’s no doubt true according to the traditional mistargeting/ad blocker/fraud definition of ad waste.
But my definition of ad waste is more business-centric, which is this: Ad waste is any failure to maximize return on ad spend. Most advertisers are systematically underperforming through a combination of linked marketing execution failures. It’s fixable, but it requires disciplined process, quality tech resources, careful measurement, and dogged iteration.
It’s worth it, though: fixing it can materially increase revenue growth rates. The scale of the underperformance is easily as costly as the fraud problem, and much more in the control of the marketer.
The underperformance gap in far too many large enterprises is fueled by four interlocking elements, and with all four, the most common failure is a lack of repeatable process and systemization.
Creative strategy problems
In the programmatic advertising ecosystem, creative strategy tends to be a horrible underperformer. We’re not talking about who can make the funniest Super Bowl ad; creative strategy is the process by which your company seeks to get outsized commercial wins through your ad creative.
Creative strategy is the logic and process of formulation of ad content to deliver desired business outcomes. It tends to be very test-driven in most companies today—which is directionally correct but insufficient on its own.
Creative strategy needs to be built through a data- and trend-fueled, hypothesis-driven (“what works for this consumer segment is…”) process. It is ideally constructed with what could be referred to as a “stackable learning framework,” where each learned truth is standardized, documented and foundational for the next tactical building block.
But that’s not what happens in most companies. Too many in-house marketing teams underinvest in the process for developing outcome-driving creative—if a formalized process even exists. There’s usually insufficient documentation, insufficient testing, and therefore insufficient confirmation of the strategy.
That means no learnings to build on for the next iteration. Far too often, it’s “We have a launch coming up—let’s get ads up and get in front of our target audience!” Instead of a creative formulation process driven off data and learnings, it’s too-often a scramble of individual-dependent creative ideation.
Media buying problems
The ANA study did identify media buying as a problem—though its suggested solutions were more complex than mine. As more and more companies have brought media buying in-house, believing that it’s not worth the expense to pay an agency to do it, they have too often failed (again) to build the systems that will lock in learnings and pave the way for iterative success.
Reactive buying is what happens too often, with poor utilization and under-analysis of data. ANA said the average participating advertiser in its study had ads appearing on 44,000 websites. That’s crazy.
The two great monolithic platforms—Google and Meta—are remarkably sophisticated and remarkably powerful for most advertisers…when properly maximized! But what I see so many times is, again, reactive behavior based on too little data—usually landing page CRO testing and not much else—and no real analytics roadmap to build buying decisions on better data.
Too often a CEO says, “Hey I want us on TikTok! It’s blowing up right now and we gotta be on it!” and so they are. That’s like throwing money out the window and hoping it lands in a good spot. Master one platform before buying others – it’s too easy to waste money hoping the next platform you buy will be the silver bullet.
Conversion Rate Optimization (CRO) is another leaky bucket of ad spend. It’s the rubber-meets-the-road zone for waste because every time we get the message right, get the creative right, and get the targeting right, we’re driving prospects into the maw of a machine that’s designed to turn them into paying customers.
Except it often doesn’t. Sometimes, obviously, that’s because you can’t win them all. But a lot of times, it’s because our machine is creaky or broken or even directly repels prospects. The right user experience is critical to ensure conversions, but that takes inter-departmental coordination that many large enterprises find surprisingly hard.
In many businesses a 20% increase in CRO can translate into a 20% increase in the business’ bottom line – especially those with predictable fixed cost structures like gaming, SaaS, and streaming.
But CRO is a function that exists in the interface zone between marketing, product, and the company’s website or landing pages. It requires resource sharing and agreement – and if it’s not done properly, bottom line growth takes a direct hit.
In larger enterprise environments where multiple cross-functional stakeholders (legal, brand, PR, engineering, product, finance) are all jockeying for their agendas, there are horror stories in which literally millions of marketing dollars are spent driving prospects to a landing page the marketers weren’t allowed to touch. That work could only be done by engineering, and engineering didn’t see the value of making strong resources available to marketing. They wanted their best people and substantially all their engineering resources on product, forcing marketing to create workaround hacks for CRO.
Insanity. And incredibly wasteful of all of that spend for ad creative and media buys. The leadership team—the CEO, the CMO, and the engineering C—all need to join hands over CRO to plug those leaks in the ad spend bucket.
Attribution and analysis problems
Too many companies have a jumble of Google or Excel spreadsheets that an overworked analyst is trying to keep track and make sense of. There’s too little automation, too little historical trend analysis, and too little sharing of common data between marketing and finance.
With manual processes and divergent dashboards come time lags, and again a lack of disciplined data analysis. We all know that Apple OS14 has made the kind of precise attribution we had grown used to disappear; that fact alone requires marketers to up their data analytics game, and too many are failing to.
I applaud the ANA study and agree with its primary finding—that there’s a ton of ad waste that comes from the programmatic system itself. But it’s incumbent on enterprise marketers to look inward at their core processes if they truly want to maximize the effectiveness of their entire ad spend.