incrementality testing
incrementality cannibalization

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How Do We Measure Incrementality

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Each of these use cases works in reverse as well. Which means that if you as a marketer lowered a bid and ROI increased, or if you paused a campaign and organics went up - the INCRMNTAL platform will provide you with these strategic outputs so that you are always unlocking the value in your marketing spend.


There’s a lot more that you can learn about INCRMNTAL and incrementality testing.


INCRMNTAL is an incrementality measurement platform providing Advertisers with , incrementality and cannibalization scores over their campaigns, ad networks and any marketing activity to unlock the full value of their marketing budget. 

If you want to learn more, visit INCRMNTAL or book a demo today!

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” 


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: