Marketing impacts are not purely additive, they multiply or synergize
Most performance models treat marketing like a math problem, add a campaign here, add another one there, total them up. Sounds efficient but completely misses what’s really happening. Marketing doesn’t just stack effects. It fuels them.
When you bring two campaigns together, say, brand awareness and direct response, you don’t just get more leads. You create a stronger system. Brand campaigns open the door by embedding trust and recognition. Direct response campaigns close it by converting pre-qualified, emotionally engaged prospects. That’s not additive, that’s performance scaling.
This synergy isn’t a theory. Les Binet, global head of effectiveness at adam&eveDDB, has documented how long-term brand building supports short-term activations. His research confirms that emotionally resonant brand campaigns make people more receptive to rational follow-up messaging. So when your media mix is structured right, the output isn’t linear, it’s exponential.
If the only thing your measurement model can do is count leads per campaign, you’re underestimating your return. You’re treating coordinated strategy like isolated activity. Fix that, and performance goes from flat growth to real momentum.
Know the difference, additive, multiplicative, and synergistic effects matter
Additive is simple. Campaign A produces 100 conversions. Campaign B gets 200. You call it a 300-lead success. No crossover, no acceleration. These are baseline results, and they’re your floor, not your ceiling.
Multiplicative effects happen when one effort actively enhances another. Think of this as compound performance. If brand awareness doubles and that leads to a 1.5x increase in conversion rate, you’ve scaled your output by 3x, not by adding more, but by activating smarter overlaps in the funnel.
Then there’s synergy. This is when two well-executed campaigns don’t just scale each other, they create outcomes that neither could drive alone. Measured correctly, synergy makes the case for integrating your media plan rather than fragmenting it. But it’s harder to define with basic tools. It demands better measurement practices and teams that understand campaign sequencing, psychological readiness, and timing.
Get this distinction wrong, and you end up funding isolated wins with blind confidence, quick results, flat long-term ROI. Get it right, and you unlock strategic levers that turn traditional spend into transformational outcomes. That’s not fluff. That’s how high-growth companies outperform their competition.
Traditional attribution models are flawed, they assume additive effects by default
Most attribution models used today operate under a flawed premise. They treat all marketing efforts as if they’re operating in isolation. Google Ads, Meta Ads, Salesforce, they all rely primarily on additive frameworks. Click happens, sale closes, credit’s assigned. Clean lines, but incomplete logic.
When these platforms tally results, they usually use last-click or first-click attribution. That means only the final or initial interaction in a buyer’s journey gets the credit. Everything else gets ignored. That’s not how human behavior works. And it certainly isn’t how marketing impact unfolds.
Even multi-touch attribution (MTA), often seen as more advanced, only partially covers the interaction between campaigns. It tries to distribute credit across touchpoints, but its linear assumption still applies, a bit from here, a bit from there, without capturing how certain interactions actually reinforce the entire system.
Media Mix Modeling (MMM) takes a step in the right direction. It measures broader trends and recognizes interdependencies between TV, digital, print, and other channels. It handles nonlinear impacts better than most attribution tools. But even MMM doesn’t always highlight synergy explicitly. The data is there, but only if you’re disciplined enough to look for it.
If your model only quantifies marketing by assigning credit to isolated touchpoints, it’s not telling you what’s working. It’s sanitizing complexity. And in that process, you’re missing the bigger value drivers across your marketing ecosystem.
Incrementality testing isn’t enough, it’s only part of the picture
Let’s be clear: measuring incrementality is the right move. It tells you whether a campaign is truly driving new behavior or just capturing activity that would’ve happened anyway. That kind of clarity is essential when budget scrutiny is getting tighter and performance needs are rising.
But incrementality, conducted in isolation, has limitations.
Most tests are built to assess bottom-of-funnel performance. They’re great for validating conversions from direct response campaigns, but not as effective at evaluating the full impact of upper-funnel activity. Brand campaigns that generate demand don’t always convert immediately. Direct response often capitalizes on that built-up intent. If you test those elements separately, incrementality skews in favor of last-click outcomes.
That leads to incorrect spending decisions. You cut top-funnel campaigns because they look inefficient in a vacuum, when in reality, they’re fueling your conversions downstream.
To get an accurate view, incrementality testing needs to be combined with models that highlight how different campaigns interact. Media Mix Modeling and multi-channel experiments allow teams to expose when two efforts work together to outperform their individual results.
Executives should think of incrementality as a diagnostic, not the full strategy. On its own, it answers isolated questions. But when layered with more sophisticated models, it becomes part of a system that identifies how marketing actually scales.
Structured experimentation reveals how marketing actually performs
If you want clarity on how your campaigns interact, design experiments that isolate and recombine your efforts. Testing isn’t guesswork, it’s how you separate noise from signal in a system that’s fundamentally nonlinear.
Start by running brand campaigns on their own. Evaluate their performance, recognize upfront that they often won’t show strong conversion metrics in isolation. Then test direct response campaigns separately. These will look more effective short-term, especially in standard attribution platforms that favor last-touch interactions.
Now run both campaigns together. Measure them using Media Mix Modeling or a statistical framework that accounts for overlapping influence. If the combined result exceeds the sum of the two independent campaigns, you’re seeing multiplicative or synergistic effects, something you would never detect using siloed measurement tools.
Push further by scaling the combined campaigns. Expand reach, increase frequency, and track the impact. If the return grows nonlinearly, you’ve confirmed a synergy that basic attribution models can’t surface.
Ignore this step, and you’re only optimizing at the margins. Include it, and you expose interactions that lead to better budget decisions, stronger ROI, and more durable growth.
Simple attribution models fall short, move toward models that capture synergistic effects
The most common attribution models oversimplify what’s actually happening across marketing systems. They count actions, assign credit, and declare winners based on linear output. But marketing doesn’t operate on clean timelines and isolated paths, it moves through feedback loops, influence chains, and cross-functional triggers.
If your attribution process doesn’t reflect this, you’re optimizing around a distortion. That leads to under-funding campaigns that are essential and over-valuing ones that merely close the loop.
To get an accurate understanding of performance, shift to models that capture multiplicative and synergistic outcomes. That includes Media Mix Modeling and experiment designs that track interactions between campaigns over time. These tools highlight the real drivers of scaled performance, campaign structures that build toward each other, not just operate in parallel.
Doing this will take more effort. Attribution becomes less about counting and more about seeing. But the result is an accurate map of how your investment actually works, channel to channel, upper funnel to bottom, impression to revenue.
Fixating on simplistic models is a strategic risk. Upgrading to real insight is a competitive move. If the systems you’re using don’t reflect the interconnected nature of your marketing, it’s time to build better ones.
Key takeaways for decision-makers
- Marketing is not additive, it’s exponential: Leaders should recognize that campaign results often scale through interaction. Multiplicative and synergistic effects drive greater impact than traditional additive assumptions.
- Understand effect types to optimize execution: Differentiate between additive, multiplicative, and synergistic effects to build smarter campaign structures. Investing in the right combination yields non-linear returns.
- Attribution tools miss the real drivers: Most platforms still assign credit using outdated additive logic. Executives should adopt modeling solutions that account for cross-channel reinforcement to avoid misattribution.
- Incrementality alone gives a partial view: Testing for incremental lift is essential but doesn’t capture how brand and performance efforts work together. Combine it with broader measurement models to guide more accurate budget decisions.
- Structured testing exposes high-leverage campaigns: Run isolated and combined campaign tests to identify whether effects are additive, multiplicative, or synergistic. Use media mix modeling for precise performance assessment.
- Old models limit growth, advanced models unlock it: Attribution frameworks that ignore synergy distort performance data and hinder returns. leaders should prioritize tools that highlight how multiple efforts interact to create scalable outcomes.
