It's a Trap: Guaranteed Performance
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It's a Trap: Guaranteed Performance
I'm not saying it is a trap. But it is a trap.
Pay-for-performance models are not inherently bad. In fact, they can offer significant benefits—if implemented correctly and transparently.
Sadly, that is an enormous “if.”
It's a self-perpetuating cycle where the more numbers look good on paper, the less inclined we are to probe and challenge.
The appeal of "guaranteed performance," especially when it comes from a trusted partner like an ad agency, can often be misleading. It's crucial to dig deeper and understand the underlying tricks of the trade.
Details are a Pesky Thing
Saving money is irresistible, especially in an environment where every marketer is under pressure to "do more with less." These guaranteed performance offers are cunningly designed to prey on this urgency. They play to marketers' desires to cut costs and improve ROI, knowing full well that these are some of the KPIs against which marketers themselves are measured.
This creates some of the many conflicts of interest we’ve previously discussed.
Performance promises typically come with ops and attribution details that are conveniently buried in the fine print or glossed over in pitch decks.
What makes this even more complicated is that these offers often come from agencies considered to be reliable partners. Trust, in this context, becomes a tool for complacency, leading marketers to ignore potential red flags.
Separating Planning from Measurement: Why It's Essential
I see this mistake all the time. The same company (agency) is chosen to plan, execute, and measure a campaign. While it might seem like a convenient one-stop-shop solution, it opens the door for significant conflicts of interest.
Why should you separate the two?
The agency promising to deliver specific performance outcomes or savings shouldn't be the one determining the metrics used to gauge those outcomes. They also should not be the ones that own or select the measurement vendors —the marketer should.
Guaranteed performance means more work, for you
Contrary to popular belief, "guaranteed performance" models are not time savers when executed in the best interest of the brand. In fact, it demands more work to ensure that there are no conflicts of interest.
To achieve this, marketers must independently engage with a third-party measurement and/or attribution vendor. You have to own the contracts, the rights to the data, and define the attribution model in-house.
Asking the Right Questions
Defining success might seem straightforward, but it requires a more nuanced approach than most marketers initially consider. Rather than stopping at a superficial understanding of success, dig deeper:
What is the desired outcome? This could be brand awareness, lead generation, or direct sales. While it is the most obvious step, it is also the one where most brands stop…but there’s more!
How will we know it happened? How will we know if consumer attitudes changed? How will we know if the cash register rang? How will we know that an online transaction occurred?
What's the causal relationship? Nerd alert! Determine how the media spend influences the achievement of these outcomes. In other words, how do we know that the media caused the outcome?
This is your media attribution model in its simplest form. Create this model before even thinking about executing your media spend.
The Common Mistake
Unfortunately, many marketers take the reverse approach. They let their media partners define what success looks like (#2) and how it will be attributed (#3). This is not just backwards; it's a loophole that performance agencies are more than happy to exploit.
The diagram below outlines the complete set of steps. Read more about it in this ADLINGO classic.
The Absurdity of "Good Enough" in Causal Measurement
As you dive into the data-driven intricacies of a campaign, you'll quickly discover that "good enough" simply doesn't cut it. So why is this subject so frequently ignored or undervalued? It's because there's a fundamental gap in understanding how causal measurement works.
Understanding Causal Measurement
At its core, causal measurement aims to determine the direct impact of a specific action—like an ad—on a particular outcome, such as sales or website traffic. The concept sounds simple enough but is surprisingly easy to overlook.
Why is it Critical?
No matter how creative, eye-catching, or viral your campaign may be, it's crucial to understand if it's actually serving its intended purpose. Are you generating meaningful engagement, or are you merely collecting vanity metrics that make for a good PowerPoint presentation but lack any real substance?
Imagine a brand that spends millions on a digital advertising campaign targeting "young, tech-savvy consumers." The campaign reports high engagement rates and excellent cost efficiencies. Champagne corks are popping at the agency.
But here's the kicker: the campaign took credit for driving outcomes from consumers that were already going to buy the product.
The campaign looks excellent on paper. The metrics are all in the green, but the truth is that it's an abject failure.
Why?
Because the brand didn't accurately define what success looks like nor did it establish a causal relationship between the media and the outcome.
As marketers, it's easy to become complacent and fall into the trap of letting agencies or in-house teams define success metrics and measurement methodologies. However, it's our responsibility to understand and apply rigorous standards to measurement and attribution.
If you're not thinking in terms of causal measurement—especially for guaranteed performance models—you're effectively flying blind.
Breaking Down "Faux Transparency"
Transparency is a buzzword in the industry, but it's been co-opted, diluted, and manipulated to the point where its meaning has been muddied. Everyone claims they offer "full transparency," but if you dig a little deeper, you might find the transparency is a carefully curated illusion.
When agencies or media vendors claim they are "transparent," they are often revealing only a selective layer of the data—enough to make you believe they're transparent, but not enough to give you true visibility into how your investment is working.
For example, telling me where my campaign could run is way different than telling me where my campaign actually ran. Telling me what my costs are is way different than disclosing all of the fees.
What True Transparency Should Look Like
For real transparency, marketers should expect the following:
Domain/App Level Reporting: This should be table stakes. You need to know not just where your campaign could run but where it did run. That is the difference between making informed decisions and shooting in the dark.
Cost Transparency: This goes beyond just knowing the final amount you're billed. You should know the exact costs of media buying, data usage, targeting strategies, and ad tech involved. Any "bundled" costs should be a red flag.
Quality Transparency: This should address the validity and viewability of impressions. Were your ads actually seen by human eyes? This becomes particularly important in an age when bots and fraudulent activity can easily distort metrics.
Why It Matters
Without these components, any claim of transparency is incomplete at best and deceptive at worst. Brands should be able to audit the quality and effectiveness of their campaigns easily, and that's only possible with a fully transparent model.
Takeaway
Focus on transparency, causal measurement, and rigorous checks and balances. Demand more from your partners, question their methodologies, and scrutinize their results. Remember, digital advertising enlightenment comes not from what you can see, but from understanding what you can't.
So the next time someone guarantees you a 15% savings, take it not as a promise, but as a point for further investigation. Because the only guarantee in digital advertising is that there’s always more than meets the eye.
