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An In-Depth Guide to Incrementality Measurement

To know the actual impact of your marketing campaign and optimize your advertising spending, you must measure critical key performance indicators. Incrementality is one of the vital marketing metrics every savvy marketer must measure. 

Incrementality shows you the true impact of your advertising efforts by comparing the outcome of your marketing activity to the outcome of your native demand — the results you would naturally achieve without advertising. The desired outcome of your marketing efforts may be increasing sales, profitability, awareness, web visits, or other metrics. 

Incrementality measurement compares the conversions of a control group (an audience not targeted by your marketing campaign) to that of a treatment group (an audience targeted by your marketing campaign). The difference in outcome is called incremental lift. Here’s the formula:

Incremental lift = Conversions in Treatment Group – Conversions in Control Group

Incremental lift helps you distinguish the business outcomes that are a direct by-product of your marketing activity from the results that you would have achieved organically without marketing or those driven by pre-existing consumer behavior. 

By analyzing the incremental lift, you can pinpoint and quantify the success directly attributable to your marketing campaigns. It’ll also guide you to make data-driven decisions to further enhance the overall outcome of your marketing strategies. Incrementality measurement should be one of the go-to performance marketing strategies in your arsenal.

Why Is Incrementality Measurement Essential?

These are the four top reasons why digital marketers should measure incrementality. 

Provides a Roadmap for Marketing Resources. 

Incrementality measurement helps you establish the actual value derived from your marketing campaigns, channels, tactics, and audience. Calculating the incrementality lift lets you know how much value you would attract without using the same strategies.

Incrementality measurement ensures you’re not getting short-changed by allocating your marketing resources to target an already-sold audience while you would get better business outcomes elsewhere. With such insight, you can create a more streamlined marketing resource allocation and distribution roadmap. 

Remember that your marketing resources include the budget, staff, and time. While other metrics like return on ad spend (ROAS) and marketing attribution may only help you optimize your marketing budget, incrementality measurement gives you a more detailed multi-dimensional view of marketing resource distribution.

Allows for Data-Driven Decision-Making

Incrementality measurement gives you a unique set of data you cannot get from tracking other metrics. Without incrementality testing, it’s hard to differentiate organic conversions from those driven by your marketing campaigns. You’ll miss out on data points that can significantly impact decision-making. Here’s a case in point.

Say you’re selling Ford trucks to audience A of sworn Ford enthusiasts. Say 100 of these prospective customers search for the keyword Ford-150 trucks for sale online, land on your Ford-150 paid ad, and proceed to purchase the truck. If you only measure your ad conversion rate, you’d be delighted because in this case, it would be 100%. Following your conversion rate data, it would make sense to double down on the Ford-150 paid ads. 

However, you would miss the critical factor: that all these buyers are Ford loyalists, and most would have bought the Ford-150 anyway by clicking your organic link if they hadn’t seen your paid ad link. You could save your ad money and still get most of the conversions. This is where incrementality measurement comes in. 

Suppose you do incrementality testing on another audience B of people who love trucks but aren’t dedicated Ford loyalists. For one month, you run paid ads with the keyword Ford-150 trucks for sale on audience A and zero ads on audience B. 

If you get 50 conversions from audience B, your incremental conversions would be 50, i.e. (100 audience A conversions – 50 audience B conversions). This would suggest that about 50 conversions on audience A could have happened organically. 

These data insights would guide you to make more informed decisions. For instance, you could slash your advertising spend on audience A and use the money to market to audience B more extensively. This way, you would increase your total conversions without snowballing your ad spend. 

Helps Maximize Return on Investment

Every marketer wants maximum return on investment (ROI) from their total ad spend. However, getting maximum ROI from your marketing investment without measuring incrementality can be a long shot. The best way to measure the true performance of your marketing campaign ROI is by tracking incremental ROI

Measuring incremental ROI gives you a more holistic view of your ad performance than just tracking return on ad spend (ROAS). With ROAS, you only factor in the revenues from your paid media-attributed results and divide them by your total ad spend. However, with incremental ROI, you factor in the total income from all your marketing channels and divide it by your total ad spend:

Incremental ROI = Total Revenues (across all channels) / Total Ad Spend 

Calculating incremental ROI lets you know the incremental revenue each marketing channel generates. This helps you distribute your marketing investments more strategically, enabling you to maximize total ROI. 

For instance, you may find that while one media channel generates new traffic, your total ROI is decreasing. Calculating incremental ROI establishes whether your marketing campaign is driving new sales or just fanning pre-existing market behavior molded by previous marketing campaigns.

ROAS doesn’t factor in this bias as it attributes all new revenue to the marketing campaign you’re currently measuring. Thus, you may keep investing money in a marketing campaign that gives you average returns, while you could get higher returns from a different media channel or campaign. Calculating incremental ROI helps you avoid such ROI pitfalls.

Provides Insights Needed for Strategic Long-Term Planning

One of the persistent marketing challenges most digital marketers grapple with is measuring long-term media effects accurately. The good news is that incrementality testing helps you predict the long-term effects of a marketing campaign thanks to procedures such as geo experiments. 

With geo experiments, you can run marketing campaigns in a treatment location only and gauge how long it takes for a campaign to become impactful. This practice is referred to as the delayed effect or Adstock effect. Assessing the Adstock effect in a treatment location gives you enough data to more accurately predict how long an ad campaign takes to build impact and yield conversions.

Such insights are a crucial guiding light when planning your long-term marketing strategy. They ensure you don’t approach marketing campaigns blindly. Instead, they give you a data-driven approach framework to prepare for your ad campaigns in the long term. This way, when you roll out your ad campaign, you won’t panic if you don’t see immediate results. In addition, your marketing budget won’t fall short before you realize value as you’ll use the data from incrementality testing to guide your budget more precisely. 

What Are the Types of Incrementality in Marketing?

These are the three mainstream types of incrementality that digital marketers use for marketing analysis. 


Channel-silo incrementality measures how an individual marketing channel impacts your advertising efforts. It assesses the effectiveness and conversion value of one channel and also evaluates what percentage of those conversions would have occurred organically. 

Channel-silo incrementality works best when you’ve spread your marketing campaign across multiple channels. It helps you gauge the individual value and effectiveness of each channel.

For instance, if you leverage paid search ads and email marketing, channel-silo incrementality will show you the conversion value of each channel. You’ll use the derived data to guide your marketing strategy and resource allocation so you generate more incremental conversions per channel.


Cross-stack incrementality is the most convenient way to measure incremental conversions when you run marketing campaigns on different ad platforms that share a parent company.

For instance, you can conduct Google stack incrementality testing for the ads you run on Google Display Network (e.g. YouTube and Gmail), Google Search, and Google Search Partners. You can also conduct Facebook stack incrementality for your Instagram and Facebook ads. 

With cross-stack incrementality, the secret is leveraging matched-market testing, which entails concurrently measuring cross-stack behavior in a single test group and in a treatment group. This gives you enough data to determine which ad stack package brings more incremental value. You can then optimize budget allocation per ad stack accordingly. 


Marketing-portfolio incrementality focuses on measuring incremental conversions across all your marketing channels. It’s an extensive incrementality measuring method that analyzes the impact of your marketing activity on all the media channels you’ve leveraged. Marketing-portfolio incrementality also weighs how much organic incremental value your marketing campaign could have earned across all channels. However, it takes a lot of analysis work to prove marketing-portfolio incrementality, especially when running massive marketing campaigns. 

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Types of Tests and Best Practices To Measure Incremental Impact

When measuring incremental impact, you want to establish the causal event of conversion and tie it to a marketing campaign. More importantly, incrementality testing helps you assess how many conversions would have happened organically. Luckily, there are several reliable techniques for measuring incrementality. These are:

A/B Testing

This popular incrementality testing approach randomly assigns your audience to two groups: 

  • Group A: The control group that isn’t exposed to your ad campaign
  • Group B: The test group that is exposed to your ad campaign

A/B testing compares the two groups’ conversion rates to establish your ad campaign’s effectiveness. If Group A records a significantly higher conversion rate than Group B, it’s evidence that your ad campaign is effective.

Holdout Groups

This involves isolating a portion of your audience, called a holdout group, and not showing them your marketing ads. You expose the remaining part of your audience to your marketing message and compare the responses of the two groups. If the conversion rate of the holdout group is higher or close to the non-targeted portion, it indicates that the incremental value of your marketing campaign is low or average. 

Matched Market Testing and Geo Experiments

Matched market incrementality testing happens in two markets that are similar in size and performance by metrics such as cost per click (CPC) and conversion rate (CVR). After identifying a matching market, segment it into control and test groups and compare the incremental conversion after a certain period. 

With geo experiments, you establish a matched market in two locations and segment it into test and control groups. These locations may be two different cities, states, countries, or whatever geographical area you decide. Geo experiments are the go-to incrementality testing technique when advertising countrywide or globally. 

Time Series Analysis

This incrementality measurement technique entails running your marketing campaign during different time periods to identify trends and patterns. You then compare the recorded trends and patterns to those recorded after or before your time series analysis. The variance in trends and patterns shows your marketing campaign’s incremental impact or lack thereof.

Econometric Models

Econometrics, better known as marketing mix modeling, is the go-to incrementality measuring technique when assessing the impact and effectiveness of complex ad campaigns across multiple media channels. 

Econometrics isolates the incremental uplift of your marketing activity in each media channel so you know which media activity drives your ROI. When you know the incremental value of all your ad activities per channel, you can better allocate your marketing budget and optimize your marketing strategy from an informed point of view. 

General Guidelines To Measure Incremental Impact

Without careful consideration, minor mistakes can leak into your incrementality measurement process and lead to biased results. Follow these three general guidelines to avoid common pitfalls when performing incrementality testing. 

Segment Your Audience To Create Test and Control Groups

Incrementality measurement profoundly impacts digital marketing strategies because it delivers granular insights into almost all moving parts of your ad campaign that influence ROI. This is possible due to audience segmentation. 

The first step is segmenting your audience into test and control groups. When creating these two test groups, consider the following audience segmentation factors:

  • Demographics like age, job type, geographical location, and income levels
  • Purchase behavior
  • Buyer’s journey
  • Device usage

You decide the segmentation factors most relevant to your marketing campaign and include them in your test and control groups. 

Measure Conversion Rates Based on a Desired Outcome

You should set a specific goal for incrementality testing and clearly state your desired outcome. Ask yourself what you want to achieve with the incrementality process. Do you want to increase web visits, sales, revenue, or leads? Your desired outcome will decide the incremental conversions you measure. It’ll also influence the segmentation factors you consider.

If you don’t track the conversion rates specific to your desired outcome, you’ll be all over the place chasing every incremental conversion you can measure. That would leave you with irrelevant conversion data that doesn’t augment your current marketing campaign’s ROI. 

Calculate the Marginal Incremental Contribution of the Channel

To get a granular view of the incremental value of each channel, you must go the extra mile and calculate the marginal incremental contribution. This means filtering your incrementality measurements to specific keywords, targeting methods, and ad groups. For instance, if your desired goal is to increase leads and web visits, you should measure the incremental conversions of all main keywords used in your marketing campaign. Such granular analysis of incremental value shows you the marginal incremental contribution of specific media channels.

The Role of AI in Measuring Incremental Impact

Incrementality testing reveals the actual incremental impact of a marketing campaign. However, other external and internal variables come into play and influence the outcome of incrementality measuring. External variables include:

  • Economic changes
  • Sharp rise in competition
  • Seasonal fluctuations

Internal variables include:

  • Development or updates of product features
  • Changes in product pricing 

This is where AI technology, like predictive modeling and machine learning, comes in. While AI predictive models cannot independently predict external variables like economic changes, they can learn from past incrementality testing data and generate solid suggestions to help optimize your marketing campaigns. 

For instance, AI can learn from your past data how seasonal fluctuations impact your incrementality measurement and predict how future changes will affect your ROI. Machine learning algorithms can also learn customer characteristics such as demographics, purchase history, and engagement levels and predict how they may affect incremental impact. 

With AI, you only need to feed in past incremental measurement data and adjust constant variables like economic changes on the go. After this first instance, the AI will scale with your incremental measuring processes and become a valuable partner that helps you:  

  • Process and analyze large marketing datasets
  • Identify common patterns like seasonality
  • Perform causal inference 
  • Complete granular reporting

Drive Incremental Impact With an AI-Powered Advertising Solution

AI is transforming all aspects of digital marketing, from programmatic advertising to measurement of the incremental impacts of marketing campaigns. While AI is an invaluable tool that every modern digital marketer should be leveraging, the truth is that putting together an AI-powered advertising solution takes significant financial power, expertise, and time. 

A more cost-effective and accessible option is partnering with an AI-enabled digital advertising partner like AUDIENCEX. With our full AUDIENCEX Intelligence (AXi) suite, we provide emergent AI technologies that empower marketers to identify and target ideal audiences, enable real-time automated optimization, and provide transparency into performance while enabling data-driven decisions with predictive forecasting. AXi Explorer provides full visibility into performance metrics, enabling an in-depth understanding of the efficacy of each channel individually and holistically, while providing predictive analytics to enable data-driven campaign decisions and an ideal media mix to achieve specific goals.

Leveraging these tools throughout the digital landscape with seamless omnichannel media access, tech-enabled creative services, holistic custom strategies and deep expertise across verticals, our teams work every day to deliver exceptional results to brands and agencies alike. We are dedicated to providing broader access to the latest in data and technology to help level the playing field, while ensuring that we accurately measure and optimize the performance impact of every campaign.

Reach out today if you’d like to connect with a member of our team who can help you navigate today’s rapidly evolving landscape, and explore how our solutions can help ensure that you get measurable, significant results out of every touchpoint in your customer journey.