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Kochava, a real-time data solutions company for omni-channel attribution and measurement, has acquired Machine Advertising.

London-based Machine Advertising has developed app marketing technology that still works in the post-IDFA world. Apple retired the Identifier for Advertisers (IDFA) in 2021 to emphasize user privacy over targeted ads. When it did that, it made it harder for adtech companies to figure out how effective a particular marketing campaign was because it couldn’t rely on data about individual users anymore.

In the wake of that change, Machine Advertising is one of the companies that focused on “incrementally,” or one of the tools to calculate the effect of a marketing campaign using new kinds of experiments, said Charles Manning, CEO of Kochava, in an interview with GamesBeat. Incrementality is one of the tools that can make attribution, or mobile media measurement, work in a post-IDFA world.

“We’ve been working on this for some time. It’s another one of these bellwether moments where it’s an indicator of things changing and how advertising is measured and looked at,” Manning said. “We’ve got our eyes wide open on how the world is changing. This is an area where I think it’s going to be really pretty valuable to marketers on a go-forward basis. Not as a replacement for attribution but as an augmentation.”


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Kochava is getting into incrementality analysis.

The acquisition of Machine Advertising will enable Sandpoint, Idaho-based Kochava to enhance the company’s measurement solutions with Machine’s Always-on Incremental Measurement (AIM) product and continue to increase its footprint in the Europe, Middle East and Africa region, he said.

Kochava was already working with Machine Advertising on search ads, and so it got to know the company and liked how the team engaged with Kochava.

“Machine’s product focus to deliver always-on incrementality measurement aligns perfectly with our own objectives to add instant value to our clients,” said Manning. “As a company built on customer-driven innovation, we are always looking for new best-in-class solutions to add to our solutions suite.”

Over the past several years, approaches to measure the effectiveness of digital advertising have been undergoing a significant transition. Performance-based and direct-response campaigns have traditionally been reliant on last-click attribution techniques. But that can’t happen so easily anymore based on the new privacy measures. (In fact, the Federal Trade Commission sued Kochava for allegedly violating privacy with its measurement solutions; Kochava denied the claims regarding its data business. It also acquired another privacy-related firm, and Manning said Kochava is still working through the legal process).

Apple’s SKAdNetwork replacement solution — described by others as a feeble candle in the dark — relies on this last-click attribution. It is a framework for measuring mobile ads with obfuscations that protect user privacy and deliver results 24 hours after an ad runs. It takes away the function of measuring the effect of ads from third-party measurement firms. And the data it provides can be misleading.

Explaining incrementality

Kochava and Machine Advertising will make it easier to measure how ad spending is working.

That’s where incrementality comes in. Based on interviews I did in March 2021, I learned that marketing is only truly effective when it is incremental. Incrementality is the lift, in terms of any goal, you get from an ad that is above all other media spending plus organic demand. If certain media spend is cannibalizing organic lift or overlapping with other media, the true value of a particular campaign is murky, as you don’t see what is complicating the picture when it comes to ad spending.

With something like SKAd Network, it’s easy to fall into the trap of relying on the “last touch,” or the last ad viewed in a given time period before a user takes an action like downloading a game. It’s easy to assume that the ad was responsible for the action. But that’s not necessarily true if you’re not measuring things correctly. The only way to get at the truth is through a well-designed experiment.

“At the end of the day, what the difference between attribution and incrementality is really around last-click attribution and incremental lift, irrespective of where the engagement with the ad falls into the customer journey,” Manning said. “At the end of the day, what customers care about is to keep on spending until there are decreasing returns based on lifetime value. That is entirely what incremental measurement is all about.”

The key to a marketing experiment is that you need a control group. If you have two equivalent markets, and you stop spending on ads in one market, you can measure the incremental effect by looking at the difference in downloads between the control market and the one where you stopped spending.

As one such experiment, imagine a marketer has a campaign running in Germany along with several other countries. “Last touch” attribution is assigning about $1,000 in day 7 revenue per day to Germany for that campaign. That is, the last ad measured in Germany gets the credit for creating about $1,000 in revenue seven days into a campaign.

The ideal way to measure incrementality would be to pause Germany on that campaign and compare the decrease in German Game World day 7 revenue with a parallel universe in which there is no spending pause. That parallel universe is called a control. Since we only have one universe, we need to create a clone or control of Germany. How?

By using a model to combine many other countries into a weighted average, incrementality companies create a synthetic control that closely matches Germany. The synthetic control, which is the weighted average of Game World day 7 revenue in France, the United Kingdom and Italy, matches quite well with Germany for an extended period of time.

Since you can only observe what actually happened in Germany after the pause, you need a synthetic control to understand what would’ve happened in Germany if you hadn’t paused.

If you pause the spending in Germany but keep it going in the other three countries, you’ll see the difference in the loss of installs. The difference between Germany and the synthetic control after the pause is actually $400 per day instead of $1,000. So that’s the true incremental day 7 revenue. Based on the average daily media spend of $4,000, you may have thought that day 7 return on ad spend was 25% when it was actually only 10%. The day 7 returns are $600 per day less than expected. That’s over $200,000 per year.

Kochava’s view of incrementality

Kochava’s dashboard for ad optimization.

Kochava said the changes in the privacy landscape have resulted in an increase in walled gardens where clicks are not being made available to advertisers and last-click measurement can not be used. For certain platforms, like iOS, a structural change has taken place to rely on alternative measurement mechanisms across the platform like SKAdNetwork.

Privacy changes have also given way to incremental measurement which is experiencing a resurgence in focus as an alternative to last-click attribution for media effectiveness, and it can be used across any media treatment regardless of privacy constructs.

Traditional incrementality measurement has been a protracted process, delivering results 30 to 60 days after a campaign flight. Measurement delays mean a general delay in optimizing campaigns. Today’s marketers have become accustomed to making daily or hourly changes to campaigns to better manage their resources and drive efficient results in real-time.

“After the removal of IDFA, there is still this question of how do we understand the efficacy of my media?” Manning said. “As an advertiser, and also as a publisher, how do I help my advertisers appreciate the value of my media source? And so the premise is really there are a few different models approaches to do incrementality.”

Manning said that this new attribution model “kind of zips together two sides of the jacket, based on these common data points over a period of time.”

The problem is that advertisers still want to understand directionally where they should spend their advertising dollars.

“What’s the incremental lift of a particular channel or a particular creative, where you don’t have insight on the actual device IDs. You simply see a cohort of these devices, and they’re statistically accurate, but they’re not deterministically identifiable. So that has been one of the main seam lines that have thrust incrementality into the forefront, Manning said.”

For the past four years, Kochava has operated a services organization that has built custom incrementality reporting modules for customers. It was a custom effort that takes place over 30 to 60 days, and now it is making an effort to do this in real time so companies can make decisions on a daily or hourly basis.

“Machine Advertising was very keen to work with us” on products like the effectiveness of Apple Search Ads, Manning said. “Our relationship really began to develop pretty meaningfully. And in the end, what we realized was that they were building the very kind of thing that we were after.”

Machine Advertising has about eight people while Kochava has 190.

Throughout 2022, Kochava and Machine Advertising have collaborated to leverage the AIM product by Machine in concert with incremental lift analyses developed by Kochava for the company’s customers. The result is a productized approach to delivering always-on incremental measurement as an augmented view for Kochava Advertisers. The AIM product has been built to work with Kochava as well as other measurement tool sets.

Kochava’s AIM product will help figure out how media spending is working.

Kochava’s AIM solution will work with other mobile measurement partners. As a result, customers won’t have to change their MMP as part of the idea of adopting the machine-learning-based incrementally.

AIM is a marketing mix modeling tool that helps advertisers drive better results by providing real-time, incremental insights, Manning said. With AIM, advertisers can understand the impact of factors such as channel saturation and seasonality. AIM then delivers AI recommendations for optimal budget allocation. In the current times of economic uncertainty, AIM is a critical tool to return on ad spend (ROAS) for CFOs forecasting around a recession environment.

“We are thrilled to join forces with Kochava, a company that shares our commitment to innovation and customer-centric values,” said Gary Danks, CEO of Machine Advertising, in a statement. “Our products integrate seamlessly into Kochava’s already impressive portfolio, delivering unparalleled mobile measurement products and driving even greater success for our clients. Our team will support Kochava’s EMEA expansion plans, and being part of a larger organization allows us to take our vision to the next level and have a greater impact on the industry.”

Manning said that working with iOS is much different now since the IDFA changes, and the same will happen with Google Play.

“The other shoe is going to drop on Android,” he said. “A device ID is not something that can be readily expected. The additional layer of insight and analysis around incremental lift is a difference maker for marketers.”

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