The Value of Incrementality Measurement for Partner Programs
The old adage that “marketers waste half of their media spend, but don’t know which half” is fading quickly as more of their advertising goes to highly measurable digital channels. However, more data doesn't necessarily mean more understanding. Modern marketers are faced with an entirely new measurement challenge: how to make sense of it all and measure value most effectively. The modern customer journey has evolved from a linear funnel into an intricate web of interactions, and traditional measurement approaches are failing to capture this new reality, where discovery, research and purchase patterns span digital and physical touchpoints over extended timeframes.
According to eMarketer, multitouch attribution is the most popular marketing model. Its report notes that 30 percent of marketers believe media mix modeling is the most effective form of measurement to identify drivers of business value or outcomes. The second most popular approach, at 29 percent, is combining different forms of attribution. These two popular models deliver value because they can help marketers compare different channels and partners, but they have several significant limitations.
A newer approach, incrementality measurement, is quickly gaining traction to help solve for some of the holes in attribution and media mix modeling. Incrementality can help isolate the true impact of specific channels, helping marketers understand hard-to-measure elements of their media plan. This includes tests with new partners, channels that deliver both brand and sales lift, and partners that deliver short- and long-term lift.
Partner marketing, which is embedded in content experiences across social, video, websites and shopping sites, is particularly vulnerable to incomplete measurement from these models and can benefit from incrementality tests.
Why Today’s Popular Measurement Models Fall Short
The two most popular models today pose significant challenges for even the most sophisticated marketers. A whitepaper from Measured points out the flaws in media mix modeling, noting that, “Media Mix Modeling (MMM) is correlation based, not causal. It’s expensive, it relies heavily on historical data, it provides only a relatively big-picture view — and MMM isn’t fast enough or granular enough for daily optimization across today’s extremely complex channel ecosystem."
MNTN published research that explains how both cookie-based and cookie-less attribution have limitations. Notably, cookie-based attribution is not only fading due to lack of cookies, it has a short life span. Meanwhile, cookie-less attribution is even less accurate than cookie-based attribution and suffers from technical complexities that make it hard to implement comprehensively. Attribution modeling is losing ground to MMM for these reasons.
These issues mean that advertisers are left with an incomplete picture. A marketer looking to understand which partner program delivered higher sales lift needs to understand not only the direct conversions from an affiliate link, but the partial contribution of a more complex customer journey, as well as longer term lift associated with links that live in content for months or even years.
Partnership content creates lasting value that defies traditional measurement timeframes. Unlike paid media, which can be precisely controlled — turned on and off at will — affiliate links and content continue to drive value long after publication.
This persistence creates a fundamental measurement challenge: some of the most valuable partner content remains active for years, creating attribution windows that extend far beyond conventional 30-day lookback periods.
The implications of this persistence are significant:
- Content created months ago may actively influence today's purchases.
- Partners often contribute to more revenue than they're directly credited for.
- Traditional last-click models systematically undervalue long-tail content.
Partnership marketing/programs drive long-term, cross-channel value that traditional attribution models fail to capture. Unlike paid media, which can be turned on and off, marketing like partner content continues influencing purchases for months or even years — often outside conventional 30-day attribution windows. This persistence leads to underreported revenue contribution, misallocated budgets, and missed opportunities for optimization.
Using Incrementality Measurement to Complete the Picture
Incrementality measurement is designed to understand the full contribution of a specific marketing effort, be that a specific campaign, affiliate link, or creative asset. For example, an incrementality test uses controls such as suppression in certain locations or on certain pages or sites to measure the lift in performance that a specific marketing effort delivers.
Incrementality also can account for partial and longer term effects of a measurement effort, as long as the test is designed correctly. For partner marketing, this is particularly valuable, as only a small percentage of conversions are solely and directly the outcome of an affiliate link. There's a huge variation in customer journeys. On average, we've found that only 24 percent of customer journeys included only an affiliate link; the other three-quarters included multiple touchpoints. While MMM is designed to measure multiple channels, it's not granular enough to help marketers decipher the value of their partner marketing within complex customer journeys. Incrementality is.
Another benefit of incrementality measurement is the level of confidence it delivers. While media mix modeling is correlative, incrementality measurement uses a control methodology that is “causal” — i.e., it shows the direct effect of marketing on an outcome. Augmenting higher level insights from a media mix model with specific incremental tests can deliver a comprehensive picture of the lift a marketer gets from their various campaigns and partnerships.
For marketers looking to get a deeper understanding of the true contribution of partner marketing, or any marketing program, it's a good idea to test incremental measurement. Tests can be designed to answer specific questions and uncover insights connected to different key performance indicators such as return on investment, sales or brand lift. The beauty of incremental measurement is that it doesn't require a full measurement overhaul, and can be added to a marketer’s current measurement program — a great reason to test it out.
Jordan Dockendorf is director, strategic marketing programs at impact.com, an affiliate, influencer and referral marketing platform.
Jordan Dockendorf is an accomplished marketing leader and Head of Marketing Programs at impact.com, the leading partnership management platform, where she drives strategic, cross-functional initiatives across ABM, research-led campaigns, webinars, and other high-impact programs. Over her seven years with the company, she has played a key role in shaping the category, driving thought leadership, and helping the industry evolve—contributing to initiatives like The Partnership Economy book and forward-thinking campaigns. She specializes in turning big ideas into compelling strategies that build brand awareness, generate demand, and push the industry forward. Passionate about creativity and innovation, she thrives on crafting narratives and experiences that connect with audiences and keep impact.com at the forefront of the market.
