How to Measure Incrementality in Advertising

If you’re not measuring incrementality, you’re flying blind. Clicks and conversions might look good on paper, but how many of them happened because of your ad? Without a clear view into cause and effect, it’s easy to misallocate budget, over-credit channels, and waste spend chasing the wrong metrics. This guide cuts through the noise and shows you how to measure what moves the needle. You’ll learn what incrementality means, why it’s the gold standard for proving impact, and how to build a measurement strategy that surfaces real results.

What Is Incrementality in Advertising?

Incrementality in advertising measures the lift your marketing creates above what would have happened without it. It's about isolating the sales that happened specifically because of your advertising efforts, not just taking credit for conversions that would have occurred anyway.

Traditional attribution tells you which touchpoints a customer encountered before buying. Incrementality in advertising answers a much more valuable question: "Would this customer have bought without our marketing?"

Consider this scenario: You run retargeting ads for your online store. Attribution reports show these ads driving lots of sales. But an incrementality test might reveal that most of these customers would have purchased anyway, even without seeing your ads. The actual lift from your campaign is much smaller than attribution suggests.

Why is Incrementality Important?

When you embrace incrementality in advertising, you get an honest view of what's working. You'll stop wasting money on ineffective tactics that just take credit for sales you'd get anyway. You can show your boss or clients genuine ROI, not misleading numbers.

Your budget will go where it helps your business grow. And you'll be able to adapt to cookie phase-out without relying on individual tracking. The result is smarter marketing that delivers results, based on optimization driven by actual impact rather than attribution fairy tales.

How Incrementality in Advertising Changes the Way You Evaluate Ad Performance

Attribution shows you where conversions happened. Incrementality shows you why. Instead of just splitting credit across touchpoints, incrementality testing reveals which campaigns caused a lift in conversions, so you can separate real growth from credit-taking noise.

This shift matters. Take retargeting: attribution might rank it as a top performer, but incrementality could show that most of those users would’ve converted anyway. That insight lets you reallocate spend to channels that truly drive new customers.

Pixis put this into practice with a global clothing brand, using its AI-powered platform to uncover where true growth was coming from, not just where attribution pointed. While traditional metrics suggested certain campaigns were high performers, incrementality-focused analysis revealed that much of the credited performance was cannibalized or would have occurred organically. 

By reallocating budget based on these insights, the brand reduced cost per incremental conversion by 30% and improved ROAS by 33%. It’s a clear example of how shifting from attribution to incrementality can reshape your entire performance strategy.

Incrementality also flags cannibalization when one campaign steals credit for results another would have delivered organically. By identifying these overlaps, you can eliminate wasted budget and build a media mix that reflects actual impact.

With privacy changes limiting access to user-level data, incrementality offers a path forward;  it relies on clean tests and clear answers.

Proven Ways to Measure Incrementality in Advertising (and When to Use Each One)

Here are the most effective incrementality measurement approaches and when to use each:

A/B Testing with Control Groups

A/B testing directly measures causal impact by comparing behavior between two groups. Start by splitting your audience into two random groups: test (sees ads) and control (no ads). Run your campaign, only showing ads to the test group. Then compare conversion rates between groups to find your incremental lift.

Calculate your results with this formula: Incrementality % = (Conversion Rate Test - Conversion Rate Control) / Conversion Rate Test

For example, with a 10% test group conversion rate and 7% control group conversion rate: (10% - 7%) / 10% = 30% incrementality. This means 30% of your test group conversions happened because of your ads.

A/B testing works because it shows direct cause-and-effect relationships, works at campaign, channel, or creative levels, and uses your first-party data, not platform metrics. However, be aware that it requires large sample sizes for valid results, can be complex to set up across different platforms, and needs proper randomization to avoid bias.

A/B testing shines for digital campaigns with large audiences when you need quick, tactical insights into incrementality in advertising, particularly in areas like PPC bid management.

Geo-Testing: A Low-Lift Option That Works at Scale

Geo-testing (also called geo-lift or market holdout testing) measures incremental impact across geographic regions. The process involves finding similar geographic markets, pausing or changing advertising in test markets while maintaining normal activity in control markets, and comparing performance to determine the true effect of your advertising.

Geo-testing works best when you can't randomize at the individual level, you need to measure channel impact at market level, or you run multi-location businesses. The benefits include measuring the impact of both offline and online campaigns, showing broad market-level effects, and revealing regional differences in effectiveness.

Geo-testing is less granular than user-level tests, external factors (local events, competitive moves) can skew results, and it takes time and resources to implement properly.

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

Media Mix Modeling (MMM) for a Big-Picture View

Media Mix Modeling uses regression analysis to estimate how marketing efforts, sales, and external factors relate over time. By analyzing historical data, it separates baseline sales from those driven by marketing, quantifying the contribution of each channel.

MMM is especially useful for evaluating long-term and cross-channel impact. It captures interaction effects between channels and works even when individual-level data is limited. That said, it requires large historical datasets, depends heavily on model quality and data accuracy, and typically produces results on a quarterly or annual basis, not in real time.

MMM is best suited for strategic planning when you need a holistic view of channel effectiveness. It helps you understand how much of your sales would have happened without marketing and where campaigns are driving incremental growth.

To get the most out of MMM, combine it with attribution for short-term, touchpoint-level insights and incrementality testing for causal validation.

Tip: Use A/B tests to guide tactics, geo-testing for offline impact, and MMM to shape your overarching strategy. Together, they form a well-rounded framework for media measurement.

Making Sense of Your Results (and What to Do Next)

After running an incrementality test, your results will usually fall into one of three categories. Here’s how to interpret each, and what to do next.

Positive Lift: Scale What Works

If your campaign is driving conversions that wouldn’t have happened otherwise, you’re on the right track. First, confirm statistical significance: make sure the lift isn’t due to chance. Then dig into what’s working: which creatives, audiences, or placements delivered the most impact?

From there, scale gradually while continuing to monitor results. Test similar tactics in other channels to expand your success.

Neutral Lift: Dig Deeper

No harm, but no real gain either. Start by reviewing your test design to rule out setup errors. Break down the results by audience or placement; there may be small wins hidden in the details.

Try new creatives, targeting strategies, or bidding tactics. If performance doesn’t improve, shift the budget toward campaigns with proven impact.

Negative Lift: Course Correct

If your campaign is dragging down performance, pause or cut your spend immediately. Then analyze what went wrong. Are you cannibalizing other channels? Reaching the wrong audience? Use these learnings to adjust your broader strategy and reallocate budget to better-performing areas.

Final Notes

• Always check for statistical significance and aim for at least 95% confidence.
• Account for external factors like seasonality or competitor activity, which can skew results.
• Use control groups and advanced methods to isolate the true impact.

Incrementality testing isn’t one-and-done. Markets shift, behaviors evolve. The value lies in making it a habit and acting on what you find.

Don't Fall Into These Traps: Common Mistakes to Avoid

Incrementality testing is powerful—but only if executed correctly. Here are the missteps that often undermine results, and how to sidestep them:

1. Misreading the Results

A lift doesn’t always mean success. Always check for statistical significance—small gains can be noise. Look at confidence intervals: the wider they are, the less reliable your findings.
 Also, remember: incrementality tells you what happened, not why. Supplement quantitative results with qualitative insights to guide next steps.

2. Weak Control Group Design

Your control group needs to mirror your test group as closely as possible. Poor matching leads to unreliable results.

Use randomization to eliminate bias, and make sure your groups are large enough for valid comparisons. When true randomization isn’t feasible, synthetic controls can help, but be mindful of their limitations.

3. Overlooking External Factors

Consumer behavior doesn’t happen in a vacuum. Seasonality, competitive activity, and major events can distort your results.

Run tests long enough to smooth out short-term noise, use geos or segments with similar conditions, and document external variables that may influence outcomes. Advanced methods like difference-in-differences can help isolate your campaign’s true effect.

Avoiding these mistakes keeps your measurement clean and your decisions grounded in reality.

Real-World Wins: Brands Using Incrementality in Advertising to Drive Smarter Growth

Podscale® & Podscribe: Proving Podcast Advertising Value

Podscale struggled with a common challenge: measuring the true impact of its podcast advertising. Using Podscribe's always-on incrementality model, the brand discovered 58% of podcast-driven conversions were truly incremental. This translated to 2.56x incremental ROAS and $3.9M in incremental revenue directly from podcast advertising. The real-time measurement allowed quick budget adjustments and showed podcasting's value for acquiring new customers.

E-commerce Brand on Meta Ads: Unmasking Attribution Inflation

An e-commerce brand spending €3,000 monthly on Meta ads suspected the platform was over-attributing conversions. Incrementality testing comparing ad-exposed and non-exposed users confirmed their suspicions: Meta claimed more conversions than it actually caused. This insight led to more accurate reporting and smarter spending decisions.

Final Thoughts

Incrementality in advertising gives you the clearest picture of what’s truly working. By testing and learning what drives real growth, you can spend smarter, improve performance, and avoid wasting budget on tactics that only look successful. Whether you use A/B testing, geo-testing, or media mix modeling, the key is to keep measuring and optimizing over time. No single method fits all, so choose what aligns with your goals, resources, and channels, and be willing to challenge assumptions. Platforms like Pixis can help you move beyond surface-level metrics and uncover the campaigns that genuinely drive impact.