By Andy Crossen, Chief Product Officer, Partnerize
Incrementality. It’s a term marketers use often, but rarely with consensus. In affiliate marketing, it’s sometimes treated as a silver bullet, an exact measure of value that settles debates around campaign performance. But in reality, incrementality is still largely misunderstood across the industry. The result? A performance-driven channel with untapped potential continues to be underfunded and overlooked in favor of short-term wins and better-known attribution tactics.
Understanding Incrementality: A Missing Link
Affiliate marketing has always excelled at delivering measurable actions, clicks, leads, and conversions. However, incrementality is often where the conversation stalls when it comes to placing it alongside more traditional paid channels in the broader media mix. Part of the challenge stems from inconsistent approaches to measurement. Media mix modeling (MMM), attribution modeling, and incrementality studies are all discussed as potential solutions, but execution is often fragmented or superficial.
In some cases, incrementality is treated as a theoretical benchmark rather than a measurable, practical outcome. The tools, data inputs, and methodologies vary widely, making it difficult for CMOs to compare performance apples-to-apples across channels. Worse, this reinforces the idea that affiliates are hard to quantify, when in fact, they’re uniquely positioned to show precise value across the customer journey.
The affiliate industry needs to articulate this strength better. That means proving value through outcomes and doing so in a way that aligns with how modern marketers measure success.
Reframing the Role of Automation and Data
One of the clearest paths forward is leaning into automation and performance data to make more informed, scalable decisions. Unlike some channels, affiliate offers granular insights into what’s working and where there’s opportunity. However, manual processes dominate how publishers choose which brands to promote.
Consider a lifestyle publisher promoting three activewear brands. If they prioritize based solely on fixed commission rates, they may overlook the brand that drives higher conversion rates. That’s a missed opportunity for both publisher and brand. With access to historical performance data and automation tools, those decisions can shift from subjective guesswork to data-backed optimization.
Programmatic controls and predictive analytics aren’t just nice-to-haves; they’re essential for turning affiliates into truly data-driven channels that deliver measurable incrementality at scale.
Moving Beyond the Blame Game
It’s easy to point fingers when incrementality isn’t achieved. But the reality is, responsibility is shared. Brands, tech platforms, and partners all have a role in modernizing the way affiliate is measured, managed, and understood. Incrementality requires alignment, on data, on methodology, and on goals.
This is especially important in today’s landscape, where long-standing attribution models are challenged, and budget decisions hinge on demonstrable impact. Instead of expecting a one-size-fits-all solution, marketers should invest in collaborative frameworks that integrate affiliate insights into their broader measurement ecosystem.
When performance data is used proactively, not retroactively, everyone wins.
The Path Forward: Clarity, Collaboration, and Commitment
To unlock affiliate’s full potential, the industry needs a reset. This starts with clearer definitions of incrementality and how it’s measured. It continues with better integration of affiliate data into the overall marketing stack. And it hinges on trust, between brands and publishers, between marketing leaders and their partners, and across the tech platforms that power it all.
Affiliate marketing has always been about partnership. That spirit needs to extend into how we define success, share data, and build for the future. Incrementality isn’t a buzzword. It’s a business outcome. However, achieving it requires a shift in mindset: away from legacy approaches and toward a new model that prioritizes automation, transparency, and collaboration.
Let’s stop asking who’s responsible for the problem and start working together on the solution.
