Incrementality measurement has been praised as the holy grail of advertising measurement. Some say that if you want to improve your measurement - you should get a grip on incrementality.

Incrementality is the north star of marketing, as whatever is incremental is by definition - Adding Value.

This article can help you understand the following:

- Measuring Impact: The Beginner’s Guide to Incrementality,

- What questions can I answer with incrementality measurement?

- Why do advertisers need incrementality for proper measurement?

- How to measure incrementality?

Regardless of your business or budget size, you need to understand the concept of incrementality.

What is Incrementality ? 

Incrementality measures the true effectiveness of Advertising activities irregardless of tracking.

The goal of paid advertising is to create incremental revenues. Whether if it is to establish a stronger brand equity, or to push people to download an app and use it.

Incrementality testing requires Advertising to create various scenarios to isolate conversions data. Tests use changes in the marketing activities to compare how a change in activity influenced campaigns performance over time.

The goal for marketing in any organization is to drive growth by driving customers and prospects through the marketing funnel. Awareness > Interest > Desire > Action

Advertising efficiency is reached when the Advertising budget spend produces results that would not have happened if it was not for the Advertising activities.

Why Do Advertisers Need Incrementality ? 

Measuring incrementality is the best way to ensure that the sales results (attributed to paid marketing) are results that would not happen if it was not for the advertising activities. 

Without incrementality measurement, advertisers could be spending advertising budget continuously, believing that their advertising activities are producing incremental sales, while the reality could be that the activities are actually cannibalizing sales that would already happen without advertising. 

What is Incrementality In Marketing ?

Incremental Sales Lift has been the goal of marketing for decades, and Incrementality measurement has always been the goal of measurement.

During Summer of 2020, we set out to evolve digital marketing from the measurement of traffic to the measurement of value with INCRMNTAL.

Incrementality measurement until recently focused on segmenting audiences into a control group and showing those audiences with PSA or Ghost Ads, comparing the results of a campaign shown to the control group vs. the result of the general campaign. 

This approach usually produced biased or inconclusive results, as there was no ability to know if the control group was “clean” and unaffected by other campaigns running.

Various other attempts to test incrementality were done by blacking out advertising all together for a period of time - but this approach had such high opportunity costs and only provided conclusive results for the time the test was performed - that most advertisers abandoned the idea of performing such tests.

Our challenge at INCRMNTAL was: How would we know if a user was going to perform an action, even if they were not advertised to? 

The answer: we don’t

True Attribution Focuses on Incrementality

Our initial idea was: we will build “better attribution”. We wanted to build an attribution solution based on 1st party data, and apply machine learning to understanding the multiple touch points a user has with ads. 

But this was a moot point - multi-touch is practically impossible in the mobile app ecosystem, as user data is becoming obsolete. 

We also figured that attempting to help developers by offering a new measurement SDK is not helping the developers. No one wants to integrate another SDK.

Our research, had us understand that developers are not in need of “better attribution” - attribution as it is - is ok. But attribution can lead to terrible outcomes.

Once we established a few ground rules, we had our direction

• We do not challenge attribution data
• We are not offering to replace attribution
• Incrementality testing is done in retrospect
• Incrementality measurement does not happen for a single user

Causal Inference, Different in Difference

Once we established our ground rules, the answer was found in data science and statistics with Causal Inference and Difference in Difference. 

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed.The science of why things occur is called etiology. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Difference in differences is a statistical technique used in econometrics and quantitative research in the social sciences that attempts to mimic an experimental research design using observational study data, by studying the differential effect of a treatment on a 'treatment group' versus a 'control group' in a natural experiment. It calculates the effect of a treatment (i.e., an explanatory variable or an independent variable) on an outcome (i.e., a response variable or dependent variable) by comparing the average change over time in the outcome variable for the treatment group, compared to the average change over time for the control group. Although it is intended to mitigate the effects of extraneous factors and selection bias, depending on how the treatment group is chosen, this method may still be subject to certain biases 

Incrementality Testing in Advertising

Applying causal inference into Advertising was the real challenge. Advertising, and specifically, multi-platform, high throughput, high scale, global, competitive and highly volatile, environment with no constant makes approaching causal inference an extremely challenging task.

You may say that we had an apple fall on our heads when we found our “how”. A simple, yet obvious, constant in every market research call we had with Advertisers across the globe and across various verticals. 

From here on, it was an “easy” task, spending the next year running data experiments, developing anomaly detection, developing statistical models and algorithms, and developing a an AI brain that can interpret the algorithmic outputs to simple outputs: “New Vendor has no incrementality to your activity. We recommend that you stop the campaigns launched with New Vendor” 

What Incrementality Isn't ?

Incrementality is not a replacement of attribution, nor a method to track clicks, impressions or conversions. Some ad networks offer incrementality tests, showing the marketer that their own incremental test prooves that they produce incremental ROAS - but Return Over Ad Spend is not the correct measuremnet of Incrementality. 

True Incrementality is measured in total ROI - Return Over Investment. Making sure that the advertising activities are producing additional value. 

What Questions Can Be Answered with Incrementality Measurement ?

Incrementality measurement can help answer very important business questions, such as:

• What is the real value of the advertising spend ?
• Are the advertising spend in media vendor X producing incremental sales ?
• Are the advertising activities in media vendor X cannibalizing the organic sales results ?
• Why did the performance of a certain campaign change without doing anything ?
• Did a price increase or a budget increase produce incremental sales ?
• Which campaigns are generating an incremental lift with other paid channels ?

The Use Cases That Matter

We spoke with many growth teams from various industries and needs to come up with the 6 use cases our platform provides answers about:

• Why did the performance of a certain campaign change without doing anything ?
• Did a price increase or a budget increase produce incremental sales ?
• Which campaigns are generating an incremental lift with other paid channels ?

The Use Cases That Matter

We spoke with many growth teams from various industries and needs to come up with the 6 use cases our platform provides answers about: