What is Incrementality in Marketing?

Many marketers rely on performance metrics that look good at first glance but don’t tell the whole story. Traditional attribution models can show you who clicked or bought, but they don’t show whether your marketing made the difference. Would that customer have converted anyway, even without seeing your ad? That’s where incrementality comes in.

Incrementality helps you measure the true impact of your marketing by showing what results were directly caused by your efforts versus what would have happened on its own. It’s a smarter way to understand what’s working, and where your budget is making a difference.

In this guide, we’ll explain incrementality in simple terms, walk through ways to measure it effectively, and share real examples of how B2C brands use it to make better marketing decisions.

Breaking Down Incrementality in Marketing

Incrementality in marketing measures the true impact of your marketing activities by isolating the additional value they create beyond what would have happened naturally. In simple terms, incrementality answers the question: "What additional results did we achieve because of this specific marketing effort?"

The core concept of incrementality is lift. That's the measurable difference between what happens when you run a marketing campaign and what would have happened if you hadn't. This approach focuses on establishing a causal relationship between marketing actions and business outcomes rather than just observing correlations.

For example, let’s say your attribution platform shows that your retargeting campaign drove 1,000 conversions. That looks great on the surface. But what if incrementality testing reveals that 700 users would have converted anyway, with or without seeing your ads? That means only 300 conversions were influenced by the campaign. Suddenly, the campaign’s value looks very different. And that insight can fundamentally change how you approach budget allocation.

Unlike traditional metrics that might overstate marketing's contribution, incrementality gives you a clearer picture of your marketing's effectiveness by comparing results with and without your efforts. This helps you understand which activities drive growth rather than simply capturing credit for conversions that would have occurred organically.

Why Is Incrementality Important in Marketing

When your attribution data is unreliable, your entire decision-making process is compromised. You might be pouring budget into channels that aren’t moving the needle because they look good in your reporting tools. This leads to false confidence and inefficient spending, especially when retargeting or branded search campaigns appear to perform well on paper but offer little to no real contribution to incremental growth.

Retargeting is a classic example. Attribution models often credit these campaigns with a high volume of conversions, but incrementality tests frequently show that many of those users were already on track to convert. Similarly, branded search often picks up users who are already familiar with your brand and would have visited your site directly anyway. In both cases, attribution models give you a distorted view of impact, and incrementality helps correct that.

Incrementality has become even more important with growing privacy restrictions. As user-level tracking becomes less reliable due to cookie deprecation, privacy regulations like GDPR and CCPA, and platform changes such as Apple's ATT, traditional attribution models are breaking down, highlighting the importance of adaptation strategies.

In this privacy-first world, incrementality testing and privacy-preserving strategies offer a more accurate picture of campaign impact without depending on cross-platform user tracking.

The Difference Between Incrementality and Attribution in Marketing

While attribution and incrementality both aim to measure marketing effectiveness, they do so in fundamentally different ways. Understanding the difference is important for any performance marketer.

Attribution is about assigning credit for conversions across various touchpoints in the customer journey. It answers, "Which channels and campaigns led to this conversion?" Traditional attribution models like last-click, first-click, linear, and multi-touch attribution distribute conversion credit according to predefined rules.

Incrementality, on the other hand, focuses on causality. It seeks to answer the much more challenging but meaningful question: “Would this customer have converted if they hadn’t been exposed to this marketing campaign?”

Here’s how they compare at a glance:

• Attribution tracks user behavior across touchpoints and assigns conversion credit.
• Incrementality isolates whether your marketing caused the conversion.
• Attribution shows the journey; incrementality shows the impact.
• Attribution helps map paths to purchase; incrementality helps allocate budget wisely.

Used together, attribution and incrementality can offer a more complete view. Attribution maps out customer journeys and helps guide creative and targeting strategy. Incrementality tells you what’s delivering value. If your goal is to optimize budget and drive growth, incrementality should be your north star.

How to Build an Incrementality-First Strategy

Adopting an incrementality-first mindset starts with changing the way you think about measurement. Instead of focusing on the number of conversions your campaigns generate, you need to ask: How many of those conversions were truly caused by the campaign?

The first step is to formulate a clear hypothesis. Before running a test, ask questions like: Is my retargeting campaign driving new conversions, or just taking credit for ones that would have happened anyway? Are my branded search ads delivering value, or simply acting as a branded shortcut for users already planning to buy? Which channels bring in new customers, and which are touching people already in my funnel?

Once you’ve identified your testable question, pick one high-impact campaign or channel to focus on. It’s tempting to try to measure incrementality across your entire marketing mix, but starting small is more effective. Choose an area where you’re spending a large portion of your budget, where results seem suspiciously high, or where you suspect inefficiencies.

Next, align your testing efforts with KPIs that reflect true value, and with a focus on improving ROI. Depending on your goals, that might include:

• Incremental ROAS – revenue generated from conversions that wouldn’t have happened otherwise.
• Incremental CPA – the cost of acquiring truly net-new customers.
• Customer Lifetime Value (LTV) – changes in long-term value from different campaign types.

Kroger Precision Marketing used testing to create control audiences that mirrored their exposed audiences in purchase behaviors, helping them quantify incremental growth in sales and store visits.

With your question, test group, and metrics in place, you can run controlled experiments beyond surface-level reporting.

Setting Up Clean Experiments for Accurate Results

Properly designed experiments are necessary for reliable incrementality testing. The integrity of your test results depends on how well you control for outside variables and isolate the marketing input you're trying to measure.

Start with a clean control group. That needs to be an audience segment similar to the test group, except that they aren’t exposed to your campaign. That means matching for demographics, past behavior, and geography. It also needs to be large enough for statistically significant results, and you need to be sure there’s no contamination from other campaigns.

To create proper control groups, segment your audience randomly in your ad platform or use geographic targeting to compare markets with similar characteristics.

Randomization is also important. Whether you're using your ad platform’s audience segmentation tools or a specialized incrementality partner, make sure group assignment is random and the sample sizes are large enough to support statistically valid insights.

Equally important is defining your success metrics before launching your test. Be clear about what you’ll measure, how much lift you expect to see, and how the results will influence future decisions. That clarity helps prevent cherry-picking favorable outcomes after the fact.

The final piece is culture. Make campaign testing a habit, not a one-off project. Create a cadence, monthly or quarterly, and consistently question the assumptions behind your most visible campaigns.

How to Measure Incrementality in Marketing

Measuring incrementality reveals the true impact of your marketing efforts. Here are the most practical testing methods you can use:

A/B Testing

This straightforward approach creates a holdout group that doesn't see your marketing campaign and compares their behavior to an exposed group that does.

This method works best for:

• Email campaigns where you can easily segment audiences
• Paid social campaigns that allow for audience segmentation
• CRM initiatives where you can control which customers receive communications

You must make sure your groups are statistically similar before the experiment begins, creating a clean comparison that isolates your marketing activity's true impact.

To incorporate A/B testing for incrementality, use your ad platform's audience segmentation tools to split your audience randomly. Make sure your test runs long enough to gather statistically significant data. This typically lasts 2–4 weeks, depending on your traffic volumes.

Geo Testing

Geo testing splits markets by geographic region to isolate campaign impact. You run your marketing campaign in some regions while holding it back in others, then compare results.

This works particularly well for:

• Offline or retail brands with physical locations
• Large-scale campaigns that affect entire markets
• TV or radio advertising where targeting is geographically based

Shinola, a luxury goods retailer, questioned the effectiveness of its Facebook retargeting campaigns. By implementing geo-matched market testing at the zip-code level, they discovered gaps for better budget allocation and pinpointed the audience segments with the most growth potential.

To set up geo testing, identify matched markets with similar demographics and past performance, then run your campaigns in test markets while keeping control markets clean.

Time-Based Testing

Time-based testing compares performance during campaign periods against established baseline periods. This approach doesn't require creating separate audience groups.

Use time-based testing for:

• Promotional campaigns with clear start and end dates
• Product launches where you can measure the lift from pre-launch to post-launch
• Seasonal or holiday campaigns where you can compare to previous periods

The challenge is controlling for other variables that might change between time periods. Factor in seasonality and market conditions in your analysis.

To incorporate time-based testing effectively, establish a clean baseline period, document all other variables that might affect results, and use analytics tools to compare performance across these periods.

Advanced Methods

For more sophisticated measurement, some brands use advanced techniques:

• Marketing Mix Modeling (MMM) uses statistical analysis to determine how different marketing activities contribute to sales.
• Synthetic control methods create AI-generated control groups that predict what would have happened without your marketing.
• Ghost ads allow platforms to show control groups non-campaign content while maintaining ad placement integrity.

These methods require more resources and expertise but provide more robust insights for advanced marketing organizations.

By selecting the right incrementality testing method for your specific marketing activities, you'll see which efforts drive business results.

Challenges and Pitfalls in Measuring Incrementality in Marketing

Measuring incrementality comes with several significant challenges that can undermine your results if not properly addressed:

Small sample sizes often lead to statistically insignificant results, making it difficult to draw reliable conclusions about the true impact of marketing efforts.

External noise from seasonality, macroeconomic trends, or competitor activities can easily skew your incrementality measurements if not controlled for in your experiment design.

When multiple campaigns overlap in timing or audience targeting, they can pollute each other's results, making it impossible to isolate the incremental impact of any single initiative.

Platform limitations are increasingly problematic. Changes to iOS privacy settings and the migration to GA4 have created data gaps that complicate accurate measurement.

Ethical and privacy constraints now restrict how experiments can be designed and user data collected, requiring more creative approaches, such as strategies for improving lower funnel performance.

To improve your incrementality measurement:

• Stagger your tests to avoid overlap by planning a testing calendar with clear windows for each channel
• Isolate variables carefully by documenting all other marketing activities during your test period
• Test one change at a time instead of multiple variables simultaneously
• Use larger sample sizes when possible by extending test duration or expanding test regions
• Account for seasonal factors in your analysis by comparing year-over-year data or using normalized metrics
• Design experiments with privacy regulations in mind by focusing on aggregate data rather than individual tracking

By addressing these challenges proactively, you'll generate more reliable incrementality insights to guide your marketing decisions.

Real-World Examples: Incrementality in Action

Incrementality testing can reveal surprising insights that transform your marketing strategy. Here are three real-world examples of companies that used incrementality to make data-driven decisions:

Podscale's Podcast Advertising Impact Measurement

Podscale®, a digital media agency, aimed to quantify the direct impact of podcast advertising on new customer acquisition for direct-to-consumer brands. By leveraging pixel-based attribution and incrementality testing through its partnership with Podscribe, the agency discovered that 58% of conversions driven by podcast ads were incremental. This insight demonstrated the effectiveness of podcast advertising in reaching and converting new customers.

Large Mattress Retailer's Affiliate Program Optimization

A prominent mattress retailer sought to confirm the incrementality of its affiliate marketing program but lacked a clear definition and measurement approach. Partnering with Acceleration Partners, the retailer developed an incrementality data dashboard to assess affiliate performance. This led to optimized partnerships with affiliates delivering significant incremental value and renegotiated cost structures with others, resulting in a more cost-effective program aligned with its incremental key performance indicators.

Final Thoughts

Incrementality empowers B2C marketers to move beyond surface-level metrics and make smarter, more strategic decisions grounded in real impact. While attribution tells part of the story, incrementality reveals what drives growth and where your marketing dollars are making a difference. By adopting an incrementality-first mindset, testing rigorously, and continuously refining your approach, you can uncover hidden inefficiencies, reallocate budget more effectively, and ultimately drive stronger business outcomes.