What Is Marketing Incrementality? Definition, Types & Benefits

Understanding the true impact of each campaign is crucial, yet increasingly complex.

With multiple channels and touchpoints influencing consumer behavior, traditional measurement methods often fall short in isolating what truly moves the needle.

This is where incremental analysis comes in. By focusing on the actual lift generated by your marketing efforts—beyond what would have happened organically—incrementality provides a more clear and accurate picture of campaign performance.

This guide offers a detailed overview of incrementality in marketing, including its various types, methodologies, and ways to measure incremental lift.

What Is Incrementality?

Incrementality is the science of figuring out the actual impact of marketing activities. It involves isolating the causal effect of a marketing intervention by comparing the outcomes of a test group exposed to the intervention with a control group that is not exposed.

Incrementality asks: How many of these conversions would have occurred even without any advertising? This question helps in understanding the true value of marketing efforts.

Marketing incrementality measures the direct impact of specific marketing activities on conversions that wouldn't have happened without those activities. — It evaluates the incremental impact of marketing efforts by comparing results between a test group exposed to the campaign and a control group that isn't.

This approach allows marketers to determine the incremental lift generated by their efforts, rather than relying on overall performance metrics that may be influenced by external factors.

Consider a simple scenario: A brand launches a new ad campaign and notices an increase in sales. While it's tempting to credit the entire boost to the new campaign, other factors might be at play.

Perhaps there was a general increase in market demand or maybe another concurrent campaign also influenced the sales. Incrementality seeks to pinpoint the exact contribution of the campaign in question, providing clarity on its true return on investment.

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Incrementality vs. Marketing Attribution vs. Marketing Mix Modeling

Incrementality and marketing attribution are both essential tools for understanding campaign performance, but they serve different purposes.

Incrementality measures the impact of your marketing activities by determining the incremental lift they provide beyond what would happen without any intervention. It’s about understanding the net effect of a campaign.

Attribution models assign credit to various touchpoints in a customer's journey, helping you understand how each channel contributes to conversions. While it’s helpful in tracking paths to purchase, attribution doesn’t always reveal whether a conversion would have occurred without a particular touchpoint.

Marketing mix modeling takes a broader approach by analyzing the impact of all marketing activities, including offline channels, over time. MMM helps in understanding the overall effectiveness of your marketing strategy, but it operates at a higher level and is less granular than incrementality or attribution.

Aspect	Incrementality	Marketing Attribution	Marketing Mix Modeling (MMM)
Purpose	Measures the true causal impact of specific marketing activities	Assigns credit to each touchpoint in a customer’s journey	Analyzes the overall effectiveness of all marketing activities
Focus	Isolates the lift generated by a campaign	Tracks the path to conversion across multiple channels	Provides a high-level view of marketing strategy effectiveness
Granularity	Highly granular, campaign-specific	Granular, touchpoint-specific	High-level, often focusing on long-term trends
Channels Analyzed	Primarily digital, but can include others	Digital and offline (when tracked)	Both digital and offline
Time Frame	Short to medium-term, focused on specific campaigns	Typically short-term, related to customer journey	Long-term, often over months or years
Use Case	Optimizing specific campaigns, testing new tactics	Understanding the contribution of each channel to conversions	Strategic planning, budget allocation across channels

Why Is Incrementality Important?

Understanding the real impact of marketing activities is like having a roadmap for spending a marketing budget. It helps in avoiding the pitfall of throwing ad spend at campaigns that don't genuinely add value.

Incrementality also means:

Optimized Resource Allocation: By understanding incrementality results, marketers can allocate budgets more effectively. This ensures that marketing dollars are spent on strategies that produce the highest ROAS rather than on activities that merely shift existing demand or cannibalize other channels.
Avoiding Wasted Spend: Without incrementality analysis, marketers risk over-investing in strategies that don't actually generate the desired outcome. By identifying non-incremental activities, companies can reduce wasted ad spend.
Customer Behavior Insights: Incrementality studies can uncover insights about customer behavior that are not visible through standard metrics. For example, understanding what truly triggers a purchase can lead to more effective retargeting and personalization strategies, ultimately improving customer engagement and loyalty.

Types of Incrementality

Incrementality can be viewed from various angles, each offering unique insights into marketing performance. Understanding these different methodologies is crucial for anyone looking to make the most of their marketing activities.

Here are some of the most common types of marketing incrementality that are often considered in performance analysis.

1. Channel-Silo Incrementality: A Closer Look at Individual Channels

Channel-silo incrementality focuses on a single marketing channel to understand its specific incremental impact.

For example, if a business is investing in paid search advertising, this approach will measure how many conversions are directly attributable to that channel.

But it goes a step further. It also considers how many of those conversions might have happened anyway, perhaps due to organic search results or other marketing activities.

This type of incrementality is particularly useful for businesses that invest in multiple marketing channels. It helps to isolate the effectiveness of each channel, making budget allocation easier. For instance, if paid search is found to be less effective than initially thought, resources might be shifted to more productive channels like retargeting or email marketing.

2. Media Incrementality: Evaluating Media Channels and Campaigns

Media incrementality takes a slightly different approach. Instead of focusing on a single channel, it evaluates the effectiveness of various media channels, campaigns, or ad sets. This could include anything from social media campaigns to television ads or even print marketing.

The goal here is to understand which media activities are contributing the most to desired business outcomes, such as increased sales or customer engagement. By doing so, it becomes possible to allocate the media budget more effectively. For example, if a social media campaign is found to have a high level of incrementality, it might make sense to increase investment in that area.

Media incrementality is almost like a micro-level of media mix modeling (MMM). While MMM considers a broader range of factors, including historical data, external variables (such as seasonality), and interactions between media channels, media incrementality specifically measures the additional outcomes.

3. Campaign-Level Incrementality: The Big Picture

Campaign-level incrementality offers the broadest view. It looks at an entire marketing campaign to assess its overall effectiveness. This could include multiple channels and media types, from digital advertising to in-store promotions.

This approach helps identify which elements of a campaign are driving the most value. It can also highlight areas where the campaign might be falling short. For example, if an email marketing component of a broader campaign is found to be particularly effective, future campaigns might include a heavier focus on this channel.

How to Measure Incrementality in Marketing

There are several reliable methods to measure incrementality, each offering its own set of insights. Here's a detailed look at some of the most commonly used techniques.

1. A/B Testing: The Basics and Benefits

A/B testing is one of the most straightforward methods for measuring incrementality.

In this approach, the audience is divided into two groups: a treatment group, or group A, and holdout group, or group B. Group A is exposed to the marketing activity, such as an online ad, while group B is not. By comparing the conversion rates between the two groups, it's possible to determine the incremental lift.

This method is especially useful for online campaigns where tracking is easier. It helps in quickly understanding whether a particular ad or marketing message is effective. If the treatment group, which saw the ad, has a significantly higher conversion rate than the control grop, it's a good indicator that the ad is effective.

2. Conversion Lift Studies: A Deeper Dive into Conversions

Conversion lift studies go beyond basic A/B testing to offer a more nuanced understanding of how marketing activities affect consumer behavior. These studies measure the increase in conversions that can be directly attributed to a specific marketing activity.

For example, if an online store runs a special promotion, a Conversion Lift Study could measure how much that promotion increased sales compared to a period without the promotion.

This method is particularly useful for more complex marketing activities that might have multiple touchpoints with the consumer. It can help marketers understand not just whether an activity is effective, but also how effective it is in comparison to other activities.

3. Randomized Controlled Experiments: The Scientific Approach

Randomized Controlled Experiments are the most rigorous method for measuring incrementality in marketing.

In these experiments, the audience is randomly divided into different groups, and various factors are controlled to ensure the results are as accurate as possible. One group is exposed to the marketing activity, while the other is not, similar to A/B testing. However, these experiments often involve more complex statistical analysis and longer time frames.

The benefit of this method is that it provides highly reliable data. It's especially useful for large-scale campaigns or when the stakes are high. The insights gained from Randomized Controlled Experiments can be invaluable for making informed decisions about future marketing activities.

Choosing the Right Method

The choice of method depends on various factors, including the scale of the campaign, the available resources, and the specific questions that need answering. Here's a table that can help you settle on the right method.

Aspect	A/B Testing	Conversion Lift Studies	Randomized Controlled Experiments (RCEs)
Purpose	Compare the performance of two variants (e.g., ad creatives, landing pages) to determine which performs better.	Measure the incremental impact of a campaign by comparing exposed and non-exposed groups.	Determine the causal effect of an intervention by randomly assigning participants to a test or control group.
Best Use Case	Optimizing specific elements of a campaign, such as creative, messaging, or landing page design.	Assessing the overall effectiveness of a digital campaign, particularly in paid media.	Testing new marketing strategies, product launches, or significant changes to the customer journey.
Granularity	Highly granular, focused on specific elements (e.g., CTA button, headline).	Medium granularity, evaluates the broader impact of a campaign or specific channel.	Broad, can evaluate overall marketing strategies or significant changes in tactics.
Complexity	Relatively simple to design and execute, requiring basic statistical knowledge.	Moderate complexity, involves setting up control and exposed groups and requires understanding of lift analysis.	High complexity, requires robust experimental design and statistical analysis to ensure validity.
Time Frame	Short-term, often completed within days or weeks depending on traffic volume.	Short to medium-term, typically a few weeks to a couple of months.	Medium to long-term, often requiring weeks to months for accurate measurement.
Data Requirements	Requires a sufficient sample size to ensure statistical significance. Data collected in real-time.	Requires baseline and post-exposure data for both control and exposed groups.	Requires robust pre- and post-intervention data collection, with randomization to ensure unbiased results.
Control Group	Yes, the non-exposed variant serves as the control group.	Yes, typically involves a control group that does not see the campaign or intervention.	Yes, participants are randomly assigned to either the control or test group.
Bias Potential	Moderate; results can be skewed if the audience isn't evenly split or if external factors influence one variant more than the other.	Low to moderate; careful setup needed to avoid external influences on control vs. exposed groups.	Low; randomization helps minimize bias, making RCEs one of the most rigorous methods.
Result Interpretation	Direct comparison of metrics (e.g., conversion rates) between two variants.	Analysis of lift (incremental impact) by comparing outcomes between control and exposed groups.	Evaluation of the causal impact of an intervention based on differences between control and test groups.
Resource Intensity	Low to medium; requires basic tools for A/B testing and time to run the test.	Medium; requires more sophisticated tools to set up, track, and analyze conversion lift.	High; requires advanced planning, statistical expertise, and often more significant time and resource investments.

A Common Pitfall for Marketing Incrementality

One of the most significant challenges in measuring marketing incrementality is the collection and management of data.

Data fragmentation across various platforms, inconsistent formatting, and the overwhelming volume of data points can significantly hinder the accuracy of marketing measurement.

Without a streamlined process, the data required for incrementality analysis may be incomplete, improperly aligned, or delayed—leading to flawed conclusions and misguided decisions.

Improvado addresses these critical challenges head-on by providing a comprehensive solution that automates the collection, transformation, and preparation of marketing data.

Designed for enterprise-level needs, Improvado seamlessly integrates with over 500 data sources, ensuring that all relevant data is aggregated into a single, consistent platform.

The platform not only collects data but also applies the necessary transformations to standardize formats and align metrics, making it perfectly suited for any analytics use case, including marketing incrementality. This automation drastically reduces the risk of errors, enhances data integrity, and frees up your team to focus on deriving actionable insights rather than managing data logistics.

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Frequently Asked Questions

What is true incrementality?

True incrementality is the measurement of the incremental lift or impact that a marketing activity generates beyond what would have happened without the intervention. It isolates the direct effect of a campaign or tactic, providing a clear understanding of how much additional value—such as sales, installs, or conversions—is driven solely by the marketing effort.

Why is incrementality important?

Incrementality accurately measures the true impact of marketing activities, distinguishing between actions that genuinely drive additional value and those that merely capture existing demand. By focusing on incremental gains, marketers can optimize budget allocation and make data-driven decisions that enhance overall campaign performance. This leads to more efficient marketing spend, higher ROAS, and better alignment with business growth objectives.

How to calculate incrementality?

In simple terms, you create two groups: a treatment group exposed to the marketing activity and a holdout group that isn't. After running the campaign, you compare the outcomes (such as conversions or installs) between the two groups. The difference in results shows the true lift or impact of the marketing activity, which is your incrementality.

What is incrementality in performance marketing?

In performance marketing, incrementality measures the true impact of digital marketing efforts by determining how much additional value—such as sales or leads—is generated solely by advertising spending. It helps identify whether the marketing activity is driving real growth or just capturing existing demand, providing a clearer picture of ROAS, and insights on budget allocation.

What is the difference between attribution and incrementality?

Attribution models, like last-click and multi-touch attribution, assign credit to various touchpoints in a customer journey based on predefined rules, showing which channels contributed to a conversion. Incrementality, on the other hand, measures additional value generated by a marketing activity beyond what would have happened without it. While attribution tells you where conversions are happening, incrementality reveals whether those conversions are truly driven by your marketing efforts.