Incrementality for CFOs - the full guide
May 7, 2025
Introduction
The relationship between the CFO and CMO is one of the most important in the C Suite. CMOs deal with an increasingly complex domain, with a vast array of channels and tactics to navigate. At the same time, the CFO’s job is more challenging. You must partner effectively with your CMO on measurement and experimentation while keeping an eye on profitability and predictability.
Enter incrementality, a method that isolates the causal impact of your marketing efforts. By focusing on what drives growth, rather than just tracking touch points, it provides a clearer picture of your true return on investment (ROI), helping align the CMO and CFO.
Gone are the days of guesswork, assumptions, and crude linear models. Incrementality gives you the data-driven insights to make informed decisions about your marketing budget and strategy. With a solid understanding of which activities are actually effective, you can optimize your spend and align marketing with your financial goals.
1. Understanding incrementality
Incrementality measurement aims to identify the incremental financial value generated by marketing efforts beyond what would have occurred naturally. It provides a quantifiable method to assess the true economic impact of investments.
Most multi-touch attribution models rely on basic tracking of a customer’s digital touch-points. These fall into the post hoc ergo propter hoc trap - “after this, therefore because of this.” This means that just because event A happened before event B doesn’t make event A the cause of event B. For example, just because someone clicked on a search ad and then converted on your website doesn’t mean the search ad caused the conversion. The cause might have been elsewhere - e.g. the podcast ad that led someone to search for your brand on Google, which resulted in an ad click and then a conversion.
Incrementality measurement seeks to distinguish between mere temporal sequence and genuine causal impact. When done correctly, here are some questions you can answer with incrementality measurement at the aggregate, channel, or sub-channel level:
What’s the incremental contribution from marketing to your key business and financial metrics?
What’s the cost of acquiring incremental conversions?
How should you reallocate spend to maximize conversions?
What’s the forecasted incremental conversions with an additional dollar of spend?
Incrementality provides a common language between marketing and finance, focusing on metrics that matter to CFOs. It allows the marketing team to focus on day-to-day growth activities. By understanding true incremental impact, CFOs and CMOs can prevent over-investment in channels that don’t drive additional value. Accurate measurement leads to better financial planning and resource allocation.
2. Measuring incrementality
Marketing is often a major P&L line item and hard to scrutinize. As CFOs, we want to know: Which dollars are actually working? Which are wasted? That requires more than vanity metrics or anecdotal wins. It requires sound methods for causal measurement. Here are three commonly used methods—each with strengths and caveats.
A. Randomized Controlled Trials (RCTs): High confidence, low uncertainty
RCTs are the most rigorous way to measure marketing ROI. By randomly assigning customers (or users) to a “treatment” group (who sees the marketing) and a “control” group (who doesn’t), you eliminate bias and isolate the true effect of the campaign.
RCTs quantify revenue or conversion lift with confidence intervals—like any scientific test. The clear results justify future investment or reallocation of spend. The downside is that RCTs aren’t always possible, especially in offline channels or broad markets.
Some channels offer “Conversion Lift” tests, the closest to RCTs. Meta is the only one with a self-serve capability, but LinkedIn is running a beta for this. Other channels allow larger advertisers to setup a Conversion Lift through their account reps (e.g. Google).
B. Geo-Testing: A scalable, business-friendly alternative
Geo-testing applies a similar principle as RCTs but at the regional level. One set of states gets a new campaign, while another set doesn’t. Then you compare business performance. It scales well for large campaigns and provides causal insights. It also tests incrementality before going national with spend.
No two geographies are identical, and economic conditions, competition, and customer mix can confound results. Aiming for perfection isn’t the goal here, but advanced statistical adjustment helps to normalize for those factors. This post details how geo-tests work and is recommended reading for finance and marketing teams.
C. Marketing Mix Modeling (MMM): Long-Term Strategic View
MMM uses historical data—ad spend, impressions, pricing, seasonality, promotions—to model the relationship between marketing inputs and business outputs. It relies on regression analysis, powered by ML, to infer impact, rather than being experimental. Experiment results from RCTs or Geo-tests calibrate MMMs, which is now a common practice.
MMM provides a high-level view of ROI across all marketing channels over time. It’s valuable for performance measurement, budgeting, and forecasting over longer periods. You can simulate different spend scenarios and allocate dollars accordingly. It’s also great for channels that can’t be evaluated through geo-tests (e.g. SEO and podcasts).
However, MMMs are only as good as the data you feed them. With the right analytics and data science support, it’s a highly valuable tool.
Here’s a deep dive on geo-testing and a comprehensive method we recommend at Paramark.
3. Implementing incrementality measurement
The biggest hurdle to implementing marketing measurement is change management. Before considering incrementality measurement, most organizations use touch-based or self-reported attribution. Transitioning from the latter to the former requires important changes and the CFO’s approval.
Data requirements: Collect accurate and comprehensive data (spend, impressions or proxy) across channels and time periods. Then, classify the data at the required granularity. For example, “Search Engine Marketing” isn’t enough — break it into Branded Search, Non Branded Search, Competitive Search, etc. This requires analytics, marketing, and finance teams to agree on the model hierarchy.
Choosing the right methodology: Starting with both MMM and Experimentation at the same time can be challenging due to the time and effort required. Our recommendation is to pick one and gain experience before attempting the other. If you’re staffed well, you can do both simultaneously with a vendor that supports both. We can recommend one! ;)
Defining success: Finance and marketing teams must work together to define the implementation’s success. How will you know when you have trustworthy models and results? Write that down and hold yourselves accountable!
Several detailed posts on MMM, Experimentation, and Brand can help with this change management process.
4. Real-world application of incrementality
Brand marketing measurement: It’s a misconception that “brand marketing” can’t be measured or is difficult to measure. Yes, measuring the long-term effect is hard, but measuring the short-term impact on metrics like awareness, traffic, and conversion is possible. Incrementality measurement (using MMM or Experimentation) levels the playing field for “brand” and “performance marketing.” We don’t love those arbitrary delineations. Here’s a post with more detail.
Planning & forecasting: Incrementality measurement better understands the short-term ROI of marketing on business metrics and the diminishing returns of marginal investments than touch-based attribution. This data set informs future budget planning and forecasting. Whether for a quarterly board meeting or annual planning, incrementality measurement is a valuable tool.
Diversifying your marketing portfolio: Like in finance, over reliance on one investment strategy is unwise. When diversifying from bottom-of-funnel/direct-response channels to a healthy mix, incrementality measurement can be helpful. Without causal experimentation, this diversification can lead to false positives and negatives. A robust incrementality measurement program allows for evidence-based cross-functional conversations.
Any marketing problem or opportunity can be expressed as a measurable hypothesis through experimentation and included in your ongoing marketing mix model.
5. Misconceptions and challenges
Below, we break down several misconceptions about incrementality measurement.
Incrementality measurement is only for massive brands with big budgets. However, modern incrementality measurement is accessible to companies of all sizes and industries, including B2B and digitally native brands. Thanks to automation, APIs, and cloud computing, this is no longer expensive, slow, or exclusive. Tools like Paramark make it usable for mid-sized tech companies, not just giants like P&G.
Incrementality measurement disagrees with attribution, so it is wrong. Touch-based attribution over-credits digital, last-click channels. Incrementality includes upper-funnel and offline impact, so it offers a broader, less biased view of true ROI. There will always be tension between attribution and incrementality measurement. Use it to guide investment allocation; use attribution to understand digital conversion paths—not ROI.
Incrementality measurement is a black box. There’s a belief that all incrementality tools are black boxes and that you’re better off building it yourself or using the most experienced vendor. The reality is there are tools built with radical transparency (e.g. open source). Internal teams often lack the time or expertise to keep up with the latest practices. Meanwhile, legacy vendors may still rely on black-box methods. Choose partners who are agile, tech-savvy, and focused on actionability—not just history. A good partner will manage the model, collaborate cross-functionally, and support ongoing testing and decision-making.
Incrementality measurement is too slow. There’s a belief that MMM and Incrementality Testing take too long to be useful. This is an outdated view. Modern incrementality measurement works with daily data through APIs and can update weekly or monthly. With machine learning, the model can continuously reprocess new inputs to keep forecasts and ROI estimates current. Your measurement should align with your marketing team’s cadence — how often are you changing budgets and launching new tests across channels?
You don’t need both MMM and Experimentation — one is enough. Each method has benefits. MMMs help you understand the relationships between your marketing channels and business metrics, while experimentation provides causal insights. There are downsides to only doing experimentation. They are point-in-time analyses and many channels don’t lend themselves to experimentation (e.g. affiliates, SEO, organic social).
Conclusion
For CFOs, marketing has long been the hardest spend category to evaluate accurately. But that’s changing. Incrementality measurement—through modern Marketing Mix Modeling (MMM) or Experiments—gives finance leaders the ability to separate activity from impact.
This isn’t about validating every click or creative idea. It’s about knowing which marketing dollars drive business outcomes and which are noise. Incrementality enables capital-efficient growth, helps rebalance budgets in real time, and builds a direct link between marketing actions and financial performance.
It turns marketing from a black box into a portfolio of quantifiable bets.
Pixel-perfect tracking and endless attribution models are ending. Between GDPR, CCPA, iOS changes, and the death of third-party cookies, marketers (and CFOs) can’t rely on granular user-level data to justify spend. That’s where incrementality shines. Unlike touch-based attribution, it doesn’t depend on tracking individual users across platforms. MMM uses aggregate data, and experiments test real-world outcomes. Together, they form a privacy-resilient, future-proof measurement stack.
Think of it as a hedge against data disruption and a foundation for confident bets when the ground shifts.
Marketing measurement isn’t just a marketing challenge—it’s a business discipline. To unlock its potential, finance and marketing teams must work together to:
Align on goals and definitions of success.
Co-design experiments and scenario plans.
Invest in the right tools and models.
Use insights to guide reallocation, not just reporting.
Here’s how CFOs can bring financial discipline to marketing—without stifling creativity. When both sides speak the same language of impact, measurement becomes a bridge, not a barrier.
