Incrementality measurement has been praised as the holy grail of advertising measurement. Some say that if you want to improve your measurement - you should understand 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:
Regardless of your business or budget size, you need to understand the concept of 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 complete a purchase, download an app or increase their sh
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.
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.
Incremental sales lift has been the goal of marketing for decades, and incrementality measurement has always been the goal of measurement.
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 experimentation. 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
In essence, incrementality does not require experimentation.
Learn More: Incrementality in Marketing
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
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 the cause of the effect variable is changed. The science of why things occur is called etiology. Causal inference is said to provide 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
Applying causal inference to advertising was the real challenge. Advertising, 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”. This 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 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” - see detailed explanation below.
(short answer: no)
The role of advertising within the marketing space is to attract customers, especially new customers, to engage with a brand, leading them within a 4 stage funnel.
Advertising is most effective when it produces incremental results – i.e. sales that would not have happened if it was not for the advertising activities.
During the LUMA Digital Marketing Summit keynote presentation, LUMA presented the following slide saying: “True Attribution Focuses on Incrementality”
Marketing is a broad term describing a company’s activities to promote the sales of a product.
Each element in the 4 Marketing Ps may influence a successful marketing strategy:
In today’s competitive world, advertising activities are a necessary part of almost every product in the market. If you are a mobile app developer, you are competing against thousands of app companies from around the world.
Media vendors have made advertising lucrative by offering performance pricing schemes where advertisers can sit back and pay conversions rather than risk the costs of media which may not lead to any conversions.
While it’s tempting to advertisers – performance pricing has one big caveat – it incentivizes media vendors to optimize their own media strategy towards the low-hanging fruit: Targeting users that may likely would have converted, thus reducing the media inventory that is required to be used.
The following graph shows that an advertiser may launch campaign activities with a new vendor. Based on reporting – the new vendor is providing great value and a positive ROI.
However, zooming out to see the bigger picture, shows that this new vendor does not contribute to the total volume of conversions, but is receiving credit away from the organic traffic.
This vendor may have a high overlap with organic users.
Understanding incrementality can be very simple, but may require a marketer to make a drastic move – stop all advertising activities for a while, to see what happens…. 😬
You may be surprised that large companies who stopped all advertising activities found that up to 80% of their advertising activities were redundant, sometimes including fraudulent publisher activities, and this was while using the latest mobile measurement and analytics technologies available.
Most marketers are unwilling to perform such a drastic step – as while saving is important – most marketers would rather unlock the value of their spend rather than “save it”. Or in other words, cut the right marketing spend.
While the above example shows a clear picture of cannibalization, the reality for most marketing campaigns is more subtle.
An alternative solution to “Stop all Advertising” is available, by utilizing differences in different algorithms and examining the traffic mix on a narrow signal level. Utilizing algorithms allows a platform to continuously compare signals to signals on a combination level and point out opportunities for optimization to increase incrementality while reducing cannibalization. Here you can find a breakdown of the different techniques of incrementality.
Incrementality is not a replacement for attribution, nor a method to track clicks, impressions or conversions. Some ad networks offer incrementality tests, showing the marketer that their own incremental test proves that they produce incremental ROAS - but Return Over Ad Spend is not the correct measurement of Incrementality.
True incrementality is measured in total ROI - Return Over Investment. Making sure that the advertising activities are producing additional value. In other words, incrementality can help you identify where you are overspending and cut the right spend.
Incrementality measurement can help answer very important business questions, such as:
Incrementality measurement can be valuable across all platforms and verticals.
Incrementality is one of the leading measurement metrics for growth marketers and we have seen several teams irrespective of industry find various use cases for incrementality measurement as it lets you measure exactly what you to measure in order to eliminate redundant spend.
Some of the most common uses for the platform have been:
Industries we have seen successfully use incrementality measurement:
More over we have seen different teams use the platform for different uses. Most users fall within three main categories:
If you would like to learn more about the platform first hand, sign up for a free demo.