What does incremental mean in marketing? A clear explanation and guide

You’re trying to figure out if your new workout routine is actually working. It feels like it’s working. You’ve definitely lost five pounds. But you also cut down on late-night snacks. So which one is responsible for the change you’re seeing on the scale? That’s essentially what marketers face every day when trying to understand if their marketing campaigns are driving real growth or just taking credit for sales that would have happened anyway.

Understanding what incremental means in marketing is crucial for anyone managing marketing spend. It doesn’t make sense to continuously pour money into marketing campaigns that aren’t moving the needle. But to get that level of clarity on your marketing strategy and budget, you have to figure out which sales would have happened naturally, and which your campaign generated. It’s the difference between correlation and causation in your marketing data. Unfortunately, it’s harder than it seems to measure incrementality, and getting it right can mean the difference between scaling or cutting budget on your various marketing channels.

Key takeaways

• Marketers are often trying to measure the additional revenue generated by specific ad campaigns beyond baseline performance
• Incrementality testing attempts to isolate marketing impact through test and control groups, but faces significant structural challenges
• These tests provide snapshots of immediate effects but often miss long-term brand building that compounds over months
• The quality of incrementality tests varies dramatically, and poorly designed tests can lead to costly budget misallocations
• Sophisticated marketers validate incrementality findings against continuous measurement approaches rather than relying solely on short-term tests
• Understanding both the value and limitations of incrementality measurement helps you make better marketing decisions
• The most sophisticated approach combines multiple measurement methods to cross-validate findings and avoid blind spots

Introduction to incremental sales

Incremental sales are the holy grail of marketing measurement. They represent the sales you generated specifically because of your marketing activity, not sales that would have happened anyway through word of mouth, existing brand equity, or natural market demand. Think of it as answering the question: “What did we actually accomplish with this marketing budget?”

The challenge is separating these sales driven by marketing activity from baseline sales. When sales increase after launching a marketing campaign, how much of that increase came from your ads versus seasonal trends, competitor actions, or economic factors? This is where incrementality measurement becomes both valuable and, unfortunately, quite complicated.

Understanding incremental value matters because it determines whether your marketing dollars are working efficiently or just taking credit for inevitable purchases. An ad campaign that appears successful based on total sales might actually be delivering minimal incremental lift if those customers would have purchased anyway. Campaigns that look underwhelming in immediate conversion data might be driving significant incremental impact through brand awareness that converts over time. If you can’t figure out which of your campaigns are which, your budget allocation will be less than optimal.

Understanding incrementality in marketing

Incrementality in marketing attempts to measure the cause-and-effect (causal impact) of your marketing efforts on business outcomes. It’s about proving that your campaigns didn’t just correlate with increased sales but actually caused them. This distinction matters enormously when you’re deciding whether to scale a specific campaign or pull back ad spend.

The concept sounds straightforward, but measuring true incrementality in real-world marketing environments is complex. Marketing doesn’t happen in a laboratory. Your campaigns interact with dozens of external factors including competitor marketing activities, economic conditions, seasonality, and previous marketing exposure. Isolating the impact of a single campaign from this noisy environment requires careful, and very thoughtful, marketing attribution.

Incrementality testing has become popular precisely because it promised to cut through this complexity with controlled experiments. By comparing a test group exposed to your marketing against a control group (also called a holdout group) that isn’t, you can theoretically measure the true lift generated by your campaigns. The reality is messier than the theory, but when done thoughtfully and validated against other measurement approaches, incrementality insights can inform smarter marketing decisions.

The role of incrementality testing

Incrementality testing is the experimental approach marketers use to measure what happens when they change something specific. It’s particularly useful for tactical questions like “Does changing this ad image improve performance?” or “What’s the impact of increasing frequency caps?” The methodology involves creating test markets exposed to your marketing and control markets that aren’t, then comparing the business outcome between the two groups.

The appeal is obvious. Incrementality testing appears to help marketers move beyond correlation to causation. Rather than guessing whether your Facebook campaign drove those conversions or just happened to be running when people were ready to buy, you can see what happens in regions where the campaign ran versus regions where it didn’t. When executed well, this provides cleaner data than analyzing historical trends where dozens of variables changed simultaneously.

However, incrementality testing faces structural limitations that marketers need to understand. You cannot truly prevent people from moving between test and control regions. You can’t ensure your holdout group hasn’t been influenced by previous campaigns or competitor marketing. Most importantly, these tests typically run for a limited time period like 2-4 weeks due to cost constraints, capturing only immediate effects while missing the compound returns that happen over months. This is why incrementality testing works best for answering specific tactical questions rather than evaluating entire marketing strategies.

Types of incrementality tests

The most common approach to test incrementality is geographic testing, where different regions serve as your test group and control group. You might run a campaign in San Francisco and Portland while holding out Seattle and Denver, then compare sales trends between the groups. This method appeals to marketing teams because it’s relatively straightforward to execute and doesn’t require complex user-level tracking.

Another approach uses randomized control trials at the user level, where individual customers are randomly assigned to group a (exposed to marketing) or a treatment group (not exposed). This method theoretically offers cleaner data by ensuring the two groups are statistically similar before the test begins. However, it requires sophisticated tracking capabilities and becomes increasingly difficult as privacy regulations limit user-level data collection.

Some platforms offer holdout testing, where a portion of your audience is systematically excluded from seeing specific campaigns. This creates a natural experiment where you can measure incrementality by comparing the purchasing behavior of exposed versus unexposed audiences. Each method has tradeoffs between practical feasibility, statistical rigor, and the ability to capture real-world marketing complexity.

Incrementality measurement fundamentals

Incrementality measurement requires comparing performance between test and control groups to determine the incremental impact of your marketing activity. The basic concept is simple: if your test markets generated $500,000 in sales and your control markets generated $400,000 (adjusting for market size), you’ve driven $100,000 in incremental sales. That’s your incremental lift.

The execution is considerably more complex than this simple example suggests. You need to account for pre-existing differences between markets, ensure your sample sizes are large enough for statistical significance, and control for external factors that might affect one group differently than another. Poor execution leads to false confidence in flawed data, which is actually worse than having no data at all.

What makes incrementality measurement particularly challenging is that you can arrive at seemingly “correct” results even from poorly designed tests. A campaign might show positive incremental lift in your test purely by chance, or because an external factor (like a competitor pulling back in those specific test markets) created an artificial difference. This is why validation against other measurement approaches is crucial. Understanding incrementality requires recognizing both its value and its limitations.

Designing an incrementality test

Designing a solid incrementality test starts with defining clear key metrics that align with your business goals. Are you measuring immediate sales, customer acquisition, brand awareness, or some combination? Your test design should match what you’re trying to learn, which is probably obvious to you, but forgive us for starting with the basics. If you want to understand brand building effects, a two-week test focused solely on conversion data will miss the point entirely.

Next comes the hard part: creating comparable test and control groups. The groups should be as similar as possible in demographics, historical purchasing behavior, market conditions, and competitive dynamics. However, even with careful planning, no two markets are truly identical. Consumer behavior in Miami differs from Minneapolis regardless of demographic matching. This inherent limitation means your incrementality results will always include some degree of uncertainty.

The time period matters more than most marketers realize. Running tests during stable periods provides cleaner data than testing during promotional seasons or market disruptions. You also need a long enough testing period to capture meaningful patterns while keeping costs reasonable. Most incrementality testing runs for 2-4 weeks, which captures immediate campaign response but misses delayed effects and brand building that emerges over months. This tension between practical constraints and measurement completeness is unavoidable.

Running an incrementality experiment

Once your incrementality experiment is designed, execution requires discipline and careful data collection. You need systems to track sales, customer acquisition, and other key metrics across all test and control markets consistently. Any gaps or inconsistencies in measurement will compromise your ability to draw reliable conclusions about true impact.

External factors will inevitably affect your test. A competitor might launch a promotion in some of your test markets but not others. Weather events, local economic conditions, or viral moments can create noise in your data. You can’t prevent these disruptions, but you can document them and account for them in your analysis. This is why careful planning includes establishing protocols for identifying and addressing external influences.

The analysis phase separates good incrementality measurement from wishful thinking. You’re looking for statistically significant differences between test and control groups so you can reasonably say those marketing activities are driving customers. This requires a frank acknowledgment of limitations and a true effort to avoid cherry-picking results that confirm what you wanted to hear.

Challenges in creating true control groups

Here’s an uncomfortable truth about incrementality testing: creating genuine control groups for marketing experiments is nearly impossible. The theoretical ideal is a group that’s identical to your test group in every way except exposure to your marketing campaign. In practice, you’re working with populations that differ in countless ways, many of which you can’t even measure.

Geographic control groups face the obvious problem that no two cities behave identically. Even if San Francisco and Seattle match demographically, their food culture, shopping patterns, and response to marketing differ substantially. Think about how differently people order takeout in New York versus Los Angeles. These regional quirks create baseline differences that can easily be misinterpreted as marketing effects.

User-level control groups have different issues. People move between test and control regions. Someone might see your ad in a test market but make their purchase in a control market, or vice versa. You also can’t prevent control group members from being exposed to your marketing through channels you’re not testing (unless you’re willing to turn off all other marketing efforts), or from seeing competitor campaigns that influence their behavior.

The point-in-time problem

We consider time the most critical limitation of incrementality testing. These tests measure what happened during a specific time period, typically 2-4 weeks. This snapshot approach misses the reality that marketing effects unfold over time, building and decaying at different rates for different campaigns and media channels.

Your awareness campaigns might show minimal lift during a short test window because they’re building mental availability in your potential customers that converts to sales months later. A podcast sponsorship might take six weeks to generate its first conversion, but then continue driving sales for the next six months as episodes remain available and word-of-mouth spreads. Standard incrementality testing would dramatically undervalue both examples.

We hope you can see the dangerous trap here: campaigns that show weak incrementality results get cut, even though they’re potentially your best long-term performers. You optimize for immediate conversion at the expense of brand building. Six months later, you’re wondering why your conversion campaigns are getting more expensive and less efficient. The answer is that you’ve been starving the top of your funnel based on test data that couldn’t capture long-term effects.

Incrementality measurement in practice

Despite these limitations, incrementality measurement provides real value when used appropriately. It works best for answering specific tactical questions where immediate effects dominate and external factors are relatively stable. Testing ad creative variations, frequency caps, or bidding strategies are good use cases. Testing the value of entire marketing channels or long-term brand building campaigns is risky.

The key is combining incrementality data with other measurement approaches rather than relying on it exclusively. Marketing attribution should draw on multiple data sources: platform reporting, media mix modeling, incrementality tests, customer surveys and, potentially, longitudinal analysis. Each method has blind spots. Using multiple approaches helps you triangulate toward more accurate understanding.

Smart marketing teams also validate incrementality results before making major budget shifts. If an incrementality test suggests cutting your YouTube spend, but your MMM shows YouTube driving significant halo effects and your branded search volume correlates with YouTube flights, you have conflicting signals. Rather than acting immediately, investigate why the test showed what it showed and whether the test design might have missed important dynamics.

Customer acquisition through an incremental lens

Understanding incremental value becomes especially important when evaluating customer acquisition strategies. Your acquisition campaigns don’t just generate immediate purchases. They bring new customers into your ecosystem who may purchase repeatedly, refer others, and contribute to lifetime value that extends far beyond the test window of any incrementality experiment. They might hang out in your upper funnel for a while before doing any of these things.

A campaign that shows modest incremental lift in immediate sales might actually be acquiring higher-value customers who remain loyal for years. Conversely, a campaign with strong short-term incrementality might be acquiring price-sensitive customers who churn immediately. Measuring customer acquisition cost without considering customer quality and lifetime value gives you incomplete information.

This is where incrementality testing’s short time horizons become particularly problematic. You can measure how many new customers a campaign acquired during the test period, but you can’t measure whether those customers are more or less valuable than customers acquired through other channels. That requires tracking cohorts over months or years, which is beyond the scope of typical incrementality tests.

Budget allocation decisions

Budget allocation might be the highest-stakes way to apply incrementality data; that’s why it’s crucial to understand what incrementality testing can and cannot tell you. These tests can show you what happened during a specific campaign period, but they can’t reliably predict what will happen if you permanently shift your channel mix based on those results.

Here’s a common scenario: An incrementality test shows weak lift from your awareness campaigns, so you reallocate that media spend to conversion campaigns showing stronger incrementality. Initially, this looks smart. Your immediate ROAS improves. But over the following quarters, your conversion campaigns get more expensive and less efficient as your audience pool shrinks. You’ve optimized for short-term conversions while damaging long-term growth.

The most sophisticated approach to budget allocation combines incrementality insights with continuous measurement that captures long-term effects and cross-channel interactions. You’re looking for marketing strategies that deliver both strong incrementality results AND sustainable performance over time. When these signals conflict, it’s better to investigate rather than blindly following whichever metric seems most scientific.

Marketing attribution complexity

Marketing attribution aims to assign credit to campaigns for the sales and customer acquisition they drive. Incrementality testing approaches this by trying to isolate each campaign’s impact through controlled experiments. The appeal is moving beyond correlation-based attribution models that assign credit based on touchpoints.

Attribution based solely on incrementality testing has serious blind spots, though. It undervalues awareness campaigns that show weak short-term incrementality but boost all your other marketing. It can’t capture how multiple campaigns work together synergistically. A customer might see your TikTok ad, then search for your brand on Google, then convert from a retargeting ad. Which campaign deserves credit?

The reality is that customer journey is messy and non-linear. People interact with multiple channels before purchasing, and the combined effect of these touchpoints often exceeds the sum of individual campaign incrementality. This is why accurate attribution requires measurement approaches that capture the full ecosystem of marketing effects, not just isolated campaign impacts measured in controlled experiments.

Best practices for incrementality

If you’re going to run incrementality tests, commit to doing them properly. Document everything so you can replicate or improve the methodology later.

Be realistic about what this methodology can tell you. Incrementality testing helps answer tactical optimization questions. It provides useful validation when combined with other measurement approaches. It’s problematic as your sole method for evaluating marketing strategy or making major budget allocation decisions. Understanding these boundaries helps you extract value while avoiding the pitfalls.

Perhaps most importantly, validate incrementality results before acting on them. Run your incrementality data through your marketing mix model to see if incorporating it improves or degrades overall measurement accuracy. Compare incrementality findings against historical data, platform reporting, and business intuition. When signals conflict, investigate why rather than assuming the incrementality test must be correct because it sounds scientific.

The role of validation in incrementality

This is where Prescient’s approach differs from typical incrementality testing. Rather than assuming incrementality tests always help or always hurt measurement accuracy, we run parallel models with and without your incrementality data through our feature, Validation Layer. The actual performance determines which approach is more reliable for your specific campaigns and business context.

Some clients discover their incrementality tests significantly improve model accuracy, providing validation that the tests captured real effects. Others find that incorporating incrementality data actually degrades accuracy, revealing that test design issues introduced more noise than signal. This validation approach prevents you from making expensive budget decisions based on flawed test data.

The goal isn’t to prove incrementality testing is good or bad, but to determine whether specific incrementality results should inform your marketing decisions. This data-driven approach removes the guesswork and false confidence that comes from accepting all incrementality test results at face value. You get the benefits when tests are valid while protecting yourself from the costs when they’re not.

Moving beyond point-in-time testing

Understanding incrementality in marketing ultimately requires looking beyond snapshot tests to continuous measurement approaches. Marketing effects compound over time. Campaigns interact with each other and with external factors. Brand building happens gradually while conversion optimization delivers immediate results. Capturing this complete picture requires measurement that tracks ongoing optimization and long-term trends.

This doesn’t mean abandoning incrementality testing entirely. It means positioning these tests as one input among many rather than your measurement foundation.

The brands succeeding with incrementality understand its role in a larger measurement landscape. They use tests to pressure-test assumptions and validate channel performance. They also invest in media mix modeling that captures long-term effects, cross-channel interactions, and the compound returns that determine sustainable growth. This balanced approach extracts value from incrementality while avoiding its blind spots.

Wrapping it up…

So what does incremental mean in marketing? At its core, incremental sales represent the additional revenue generated specifically because of your marketing efforts, beyond what would have occurred through business as usual. Measuring this incremental impact helps you understand which campaigns are truly driving growth versus simply taking credit for inevitable purchases.

Incrementality testing offers a structured approach to measuring this lift through controlled experiments comparing test and control groups. When designed carefully and used appropriately, these tests provide valuable insights into campaign performance and marketing efficiency. The challenge is that even well-designed incrementality tests face inherent limitations.

The most sophisticated marketers use incrementality measurement as part of a comprehensive approach rather than their sole measurement method. By combining multiple measurement approaches and validating findings before acting, you can leverage incrementality insights while avoiding costly mistakes based on incomplete data.