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The future of digital advertising will not be based on an ID

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The future of digital advertising will not be based on an ID

The original version of this article was first published in German on onlinemarketing.de by Niklas Lewanczik

What will advertising, and particularly targeting on mobile, look like when Apple's mandatory IDFA tracking changes take effect? (and if Google follows suit?) Pan Katsukis from Remerge gives us the answers.

A world without advertising IDs is difficult to imagine at the moment. And yet, many companies had already started to prepare for this scenario before Apple’s 2020 announcement that users will soon have to opt-in to app tracking on iOS14. The announcement of this change caused a stir among advertisers, developers, and advertising platforms such as Facebook. The latter publicly denounced Apple for feared losses in advertising revenues and a drastic reduction in tracking options.

After Apple postponed the tracking opt-in to spring 2021 - it should be mandatory from version iOS 14.5 - an update was written into the App Store guidelines. By now, those who will be affected by the upcoming change should be looking into ways of informing potential app users about the opt-in process and exploring alternatives to tracking and targeting. After all, Google could soon come up with a similar feature for Android. Google is one of the companies that responded quickly to Apple's news and adapted its own advertising SDK with an update at the beginning of September 2020. They also provided app developers with tips on how to deal with the mandatory opt-in changes.

"At the end of 2019 we already aligned our strategy towards a no-ID future"

Companies in the app marketing industry have been grappling with what an advertising future could look like without a personal identifier long before Apple’s big announcement. Among these international companies is Remerge, a programmatic app marketing platform that specializes in solutions for app retargeting, user re-engagement, and incrementality measurement and uplift optimization. That's exactly why we spoke to Remerge CEO and co-founder Pan Katsukis. We wanted to gain a deeper insight into the status quo of the mobile marketing scene and the expectations of professionals regarding the future of the industry.

The interview

Mandatory opt-in for mobile app tracking

OnlineMarketing.de: Apple's announcement regarding the mandatory user opt-in for tracking apps in the App Store has created a shockwave in the marketing landscape. Were you surprised by the development at Remerge?

Pan Katsukis: Anyone who has followed Apple's development over the past few years knows that the topic of privacy has been the focus of its corporate strategy for some time. It was clear to us early on that this would also affect marketing. That is why we decided to align our strategy towards a no-ID future at the end of 2019, and our team committed to it. At the beginning of 2020, before Apple's announcement of the new privacy framework, we started working on new products that can work in a world without an ID. When Apple announced its concrete plans for a stronger privacy-driven app environment in the summer of last year, we were well prepared and were able to quickly and effectively set new priorities.

Estimating revenue losses

In this context, is it possible to estimate how much revenue losses an average SME could actually face?

There are many factors that will play a role in the future: How will advertisers behave? Are marketing providers able to quickly provide them with good alternative products that also work without an ID? In order to estimate the consequences in terms of sales, one can of course draw a comparison with similarly drastic developments in the past, for example when Apple rolled out ITP for Safari in the web area. Without cross-site tracking, advertising revenues fell by 40 percent. However, Safari plays a relatively small role on the web, very different to the iOS operating system for app developers. The need to reach iOS users through advertising will definitely remain high, so more will be invested in alternative solutions. Accordingly, there are estimates of sales losses that are only in the mid to high single-digit range (four to eight percent). In the first few weeks there will certainly be a shock and the losses will be higher, but over time this should recover once the uncertainty has subsided.

The personalization of marketing activities

Facebook has created a dedicated website to point out the dangers that arise for SMEs in light of the IDFA update. In your experience, are these companies currently afraid that their marketing efforts might be less personalized?

I doubt it. In relation to this website campaign, it looks more like Facebook is waging a proxy war against Apple. Targeting, for example by city, will still be possible. What will change is the measurability of campaign performance, and as a result Facebook will no longer be able to simply attribute itself to itself, as in the past. Advertisers will rely on Apple's new SKAdNetwork for attribution - where there are 40 percent fewer visible conversions. If the SMEs see poorer performance, then they will also be more cautious with the budgets or shift them to other channels.

The impact on mobile attribution

Which areas would actually be affected? Is this only about targeting and retargeting or also about attribution models for the apps?

In fact, I think attribution will be affected even more than targeting. Apple now forces advertisers to use SKAdNetwork for attribution. MMPs and attribution platforms such as Adjust and Appsflyer must also use SKAdNetwork and must stop using fingerprinting or probabilistic attribution models. In comparison to today's models, SKAdNetwork has a relatively rudimentary design; the data is aggregated and only made available after 24 to 72 hours. This will, of course, make the timely campaign optimization more difficult.

Developing a strategy to measure and optimize advertising

What do marketers have to prepare for now - alternative solutions, an app optimization that is as compliant as possible, or both at the same time?

If marketers want to continue reaching iOS users, then they need a good strategy for measuring and optimizing advertising. The top marketers have been testing the no-ID inventory for a few weeks to find the right strategy. As already mentioned, SKAdNetwork measurement on its own is not enough to optimize advertising. I am sure incrementality tests will be used to achieve real-time results and gain scientific insights on the impact of a campaign.

It makes sense to be active in this area from the very start. Right now, prices for no-ID inventory are likely to be very cheap because many marketers will be unsettled and hesitant. Large web platforms such as The Trade Desk have also announced that they will not buy no-ID inventory, which means that competition for advertising space will be less than it is today.

Alternatives to app tracking

Are there any direct alternatives to app tracking that could determine a similar user data set?

There has been a debate over whether fingerprinting or probabilistic methods will be allowed, but Apple made it clear in its updated FAQ that doing so would risk expulsion from the app store. Apple clearly wants to put a stop to the practice of tracking and targeting individual users without their consent. Anonymization alone is not enough. So the basis of alternative methods must be data aggregation, that is, to combine users into groups and populations. This is done, for example, in incrementality tests. Here, the users are divided into a test group and a control group and the performance of the two groups is compared. The test group sees the advertising campaign, the control group does not.

« I am sure incrementality tests will be used to achieve real-time results and gain scientific insights on the impact of a campaign. »
Pan Katsukis

Acquisition and retention in a privacy-centric environment

How can apps continue to thrive in terms of acquisition and retention as we move towards a more privacy-centric environment in iOS?

With SKAdNetwork, Apple offers the basis for measuring campaigns. However, this only provides time-delayed and aggregated results and does not reflect retention values. Additional measurement methods such as incrementality tests are a useful component in order to see real-time data and retention values. The fact that inventory will become cheaper in the short term will also have a positive effect. This will make it possible to spread campaigns and still achieve good results. The focus will be on advertising media and context. If you keep this in mind, it helps with optimization.

The focus on aggregated data

How will user acquisition and retention likely change? How does the lack of data affect user-level targeting, creative personalization and attribution models? Do you have an example scenario for this?

Much of the targeting we see now will also be possible in the future. For example, we continue to use Dynamic Product Ads for personalized advertising. The basis of this, however, is not which specific articles users have previously viewed, but rather aggregated data such as the city in which they are located or the time of day. For example, a delivery service can display a suitable advertisement with a coupon for lunch in the city where the users are located.

Sustaining app growth through programmatic advertising

How are the demands on DSPs changing and what factors will be important in sustaining app growth through programmatic advertising?

We see three main factors: transparency, optimization and scale.

In the future, DSPs must be able to generate more knowledge in order to provide the foundations for making decisions about optimization. Relying solely on SKAdNetwork data will not be enough. Incrementality measurement would be an example of something that supports decision-making around optimization. Contextual data and strong creatives will also play a bigger role in optimization. By scale, I mean how many queries per second (QPS) the DSP processes and how intelligently it can provide insights that help deliver a relevant, high-performance range. More contextual data points on the supply side can therefore help with optimization.

« In the future, DSPs must be able to generate more knowledge in order to provide the foundations for making decisions about optimization. »
Pan Katsukis

Incrementality and true iROAS

What exactly does incrementality mean in this context, what are incrementality tests and how do they show the true iROAS of advertising expenditure?

In incrementality tests, users are divided into a test group and a control group, and the performance of the two groups is then compared. The test group sees the advertising campaign, the control group does not. This measurement offers advantages compared to SKAdNetwork such as real-time data or additional information, such as retention data. The method itself comes from science. For instance, it is used to test the effectiveness of a vaccine. In other words, incrementality tests are rule-based attribution models that offer realistic results.

Gatekeepers in mobile marketing

Google has taken the path of customization, has adapted its advertising SDK and advises app developers to use dialog pop-ups. Is this adjustment inevitable? And doesn't it show too clearly how powerful Apple (and theoretically also Google) are as gatekeepers in mobile marketing?

Every app developer who runs advertisements or shares data with third party providers should show the dialog pop-up, otherwise they risk being kicked out of the App Store. On the one hand, Apple has created a fantastic market with ingenious new business models. Meanwhile, this market, with its many diverse players, is so big that Apple cannot plan such serious changes without involving the industry players. Apple is clearly using its market power here and setting the rules. Given the size of the market, this is not acceptable, and there is no alternative for the iOS app developer.

Proactively tackling transparency

There is a general trend towards data protection-compliant solutions, including cookieless tracking via Chrome (Google has just presented an update on alternative solutions such as Federated Learning Cohorts [FLoC]). Should app developers and marketers therefore rely more proactively on transparency in tracking regardless of requirements? Or simply react when the time comes?

Looking at the development of privacy, one can clearly say: The future of digital advertising will not be based on an ID that is passed back and forth on the web. Apple is pushing ahead here; all browser manufacturers, except Chrome, filter cross-site tracking and Chrome wants to aggregate the user data. So, in the future we will not need any pop-ups for advertising consent, because the basis for advertising will be aggregated data, and these do not need user consent. The technologies for this are in development as the industry already needs solutions for Apple's iOS changes. Perhaps these will lay the foundations for web technologies. As a marketer, it's important to monitor these developments and be at the forefront of data-protection compliant solutions. That will be the future.

The specifics of opting in or out

Do you think that users will read through the specific details for tracking apps that Apple is requesting before they decide for or against opting in to tracking?

The pop-up itself is relatively short and there are intensive tests being done on how to increase the opt-in rates. In the medium term, however, I expect the opt-in rates to decrease because users do not understand the added value of tracking and will associate it negatively. Additionally, you can deactivate the pop-up centrally for all apps so that it no longer shows up and ensures that you, as a user, cannot be tracked. Hardly anyone expects opt-in rates of more than 20 percent.

Providing user consent

Would you always give your consent to tracking apps in the App Store? Are there certain hurdles?

Yes, I personally would give my approval, especially for smaller apps, because I know how much added value I can add to the app that I use for free. Where I wouldn't do it would be on Facebook or Google, as they already have a dominant market position and shouldn't benefit from it any further. The good thing about Apple's Privacy Framework is that, compared to the GDPR, Facebook and Google in particular are “caught” because their data sovereignty is significantly higher than that of independent providers.

[End]

Get the latest insights on the iOS14 changes

We have launched a ID or No ID dashboard to help mobile marketing players with their future planning in a post-IDFA world. This interactive site provides daily updates for the programmatic advertising industry, which will be further impacted by Apple’s privacy-centric iOS 14.5. Be sure to add it to your bookmarks so don't miss a day.

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Retargeting lexicon
Programmatic Advertising

The automated process of buying and selling advertising space through digital platforms.

View-Through Attribution
view-through-attribution

Refer to: Attribution Methodology

Uplift Test
uplift-test

A randomized control trial test conducted by Remerge to measure the incremental impact of one or more campaigns.

See also: Randomized Controlled Trial

Uplift Report
uplift-report

A report by Remerge showing the results of an uplift test. It presents the incremental revenue generated, on top of organic and other marketing-driven conversions. Also contains observed values such as ad spend, group sizes, amount of conversions, converters, and revenues per group, plus other metrics.

SKAdNetwork
skadnetwork

Stands for Store Kit Advertising Network. Apple’s measurement framework for tracking mobile attribution. Introduced in 2018 and widely implemented in 2020 with the iOS 14.5 update.

Segment
segment

A group of users with common attributes such as location, demographics, activity level, value or amount of purchases, and how recently they last opened a specific app.

Retention Rate
retention-rate

The share of users active in the app within certain time frames after install, reengagement, or other events.

Retargeting
retargeting

A type of marketing channel used by an app owner to engage with their existing users through other channels within the same device. Usually, the aim is to encourage users to complete a particular task e.g. completing a purchase, buying in-game currency, placing a first order. The conventional way of retargeting relies on user IDs, such as AAID and IDFA.

Reshuffle
reshuffle

Reshuffle indicates the randomization and marking of users when they were once part of a test or control group.

In incrementality measurement, reshuffling the group assignment for a specific application fights aggregated bias over time where one group doesn't see any ads while the other group is constantly exposed to them.

Reshuffling is relevant in cases where a test has been running for a long time and/or in campaigns the experience more extensive changes to the campaign setups, segmentation, or creative strategy.

Real-Time Bidding (RTB)
real-time-bidding-rtb

The process by which individual ad placements are bought and sold via programmatic auctions that happen instantaneously. With real-time bidding, ad buyers bid on an ad space, which, if the auction is won, instantly displays the buyer's ad. This lets demand-side players such as advertisers or DSPs optimize the purchase of ad placements from multiple sources.

Randomised Controlled Trial (RCT)
randomised-controlled-trial-rct

A method that randomly separates a specific population into two groups that are as similar to each other as possible, namely the test group and control group.

further reading
Queries Per Second (QPS)
queries-per-second-qps

The number of ad placements a DSP is able to process in order to determine on how to bid on them.

Publisher
publisher

Within the sphere of app marketing, a publisher is an App Developer that gets paid for placing ads within their app. For example, an advertiser wants to reach their users via App Y, so they pay App Y to display their ads.

further reading
Public Service Announcement Ad (PSA Ads)
public-service-announcement-ad-psa-ads

An incrementality testing methodology where devices in the control group are shown PSA ads, like donation drives or road safety reminders. By serving real ads, information on the devices within the control group that would have been exposed can be obtained. Unexposed devices are excluded from the measurement to reduce noise.

Probabilistic Attribution
probabilistic-attribution

Refer to: Attribution Methodology

Organic Behavior
organic-behavior

A user’s behavior not directly attributable to specific marketing efforts.

Multi-Touch Attribution
multi-touch-attribution

Refer to: Attribution Methodology

Mobile Measurement Partner (MMP)
mobile-measurement-partner-mmp

Within the sphere of app marketing, MMPs are a service provider that specializes in measuring activities that are happening within and leading to the app. An app publisher may incorporate an MMP into their app to track activity and events e.g. time spent on a certain screen, sources of incoming traffic, app opening frequencies etc.

Lifetime Value (LTV)
lifetime-value-ltv

The amount of revenue generated by the user for the App Developer during the entire duration of the relationship with the user, beginning with the app install.

Last-Click Attribution
last-click-attribution

Refer to: Attribution Methodology

Key Performance Indicator (KPI)
key-performance-indicator-kpi

The key metrics used to assess the effectiveness of an effort in achieving its objective. In programmatic advertising, the common types of performance indicators depend on the goals and nature of each campaign. These can include ROAS, cost per action, and retention rate.

Intent-to-Treat (ITT)
intent-to-treat-itt

An incrementality testing methodology where no ads from the campaign are shown to devices within the control group. Also known as a ‘holdout test’. Cost-free and easy to implement, but with a relatively high level of noise.

This method compares the behavior of all users in both groups. In the test group, this includes both exposed and unexposed users

Incrementality
incrementality

A method of measuring the impact of a specific activity, on top of organic and other activity.

Incremental Revenue (iRevenue)
incremental-revenue-irevenue

The estimated revenue caused directly by the campaign.

Formula:Revenue from test group – revenue from control group = iRevenue

Incremental Return On Ad Spend (iROAS)
incremental-return-on-ad-spend-iroas

A KPI used in calculating how cost-efficient a campaign is. This is used to evaluate the relationship between incremental revenue and the amount of money spent on the campaign. The figure is typically represented in percentage.

Formula:
Percentage: [IRevenue ÷ ad spend] × 100 = IROAS%
Ratio: IRevenue ÷ ad spend = IROAS

Incremental Cost Per Action (iCPA)
incremental-cost-per-action-icpa

A KPI used to evaluate the cost of incremental conversions.

Formula:Ad spend ÷ [test group actions – control group actions] = iCPA

Incremental Conversions
incremental-conversions

The estimated amount of conversions caused directly by the campaign.

Formula:
Test group conversions – control group conversions (scaled) = Incremental conversions

In-app Event
in-app-event

Actions made by a user within the app, such as log-in, registration, completion of onboarding, or purchases. These events can be tracked with the help of an MMP.

Impression
impression

The deployment of the ad to the ad placement. An impression might not necessarily mean that the ad has been viewed.

Identifier for advertisers (IDFA)
identifier-for-advertisers-idfa

A unique random device identifier Apple generates and assigns to every iOS device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Ghost Ads
ghost-ads

A testing methodology that shows devices in the control group an ad ran by another advertiser on the platform, therefore removing any additional cost for clicks and impressions. The control group behavior is then marked with a ‘ghost impression’, which gives the information on which control group users would have been exposed.

further reading
General Data Protection Regulation (GDPR)
general-data-protection-regulation-gdpr

A regulation under the EU (European Union) law on data protection and privacy within the EU and the EEA (European Economic Area), that grants users control over how their data is stored and used by organizations. To comply with GDPR, programmatic sellers must clearly communicate to users how their data will be stored and used. When a user gives consent to an organization to process their data, it enables targeted advertising.

Exposure Rate
exposure-rate

The percentage of devices within a test group that received at least one ad impression, versus the total number of devices within the test group targeted within a campaign during an uplift test. For example, if 900 out of 1,000 users are shown an ad, the exposure rate is 90%.

See also: Uplift Test

Deterministic Attribution
deterministic-attribution

Refer to: Attribution Methodology

Deep link
deep-link

A link that sends users directly to a specific in-app location, instead of the app marketplace. Deep links bypass the steps needed to go through to reach a conversion point, bringing the user directly to where they can perform the intended action e.g. completing a purchase, buying coins, placing an order.

Test Group
test-group

Within the sphere of app marketing, this refers to the group of devices that may be shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the control group.

Compare with: Control Group

further reading
Control Group
control-group

Within the sphere of app marketing, this refers to the group of devices within the target audience that are not shown ads from a specific campaign in the test. The actions on these devices are then compared to the actions on the devices in the test group.

Compare with: Test Group

further reading
Contextual targeting
contextual-targeting

A type of targeting that works with contextual signals only, such as location data (country, city, postal code), language setting, mobile operating system, device model, as well as publisher information.

California Consumer Privacy Act (CCPA)
california-consumer-privacy-act-ccpa

A bill that enhances privacy rights and consumer protection for residents of California, United States. The CCPA took effect on January 1, 2020.

The CCPA provides these rights to consumers:

- Know what personal data is being collected about them.
- Know whether their personal data is sold or disclosed, and to whom.
- Say no to the sale of personal data.
- Access their personal data.
- Request a business to delete any personal information that was collected from that consumer.
- Equal service and price, even if they exercise their privacy rights.

Attribution Window
attribution-window

A specific time frame that is taken into consideration when determining the source of a user’s action.

Attribution Provider (AP)
attribution-provider-ap

A role played by an MMP to credit the in-app activity of users to the correct media sources.

Attribution Methodology
attribution-methodology

Refers to the process of identifying which conversions belong to which preceding click or impression. Common attribution methodologies include:

  • Click-Through Attribution - Determines the source of a conversion based on the user’s click activity.

  • View-Through Attribution - Determines the source of a conversion based on the ad impression delivered to the user.

  • Deterministic Attribution - A model that establishes the origin of a user’s conversion from a specific click or impression, based on unique device IDs.

  • Probabilistic Attribution - A model that establishes the likelihood of a user’s conversion originating from a specific click or impression, based on the data logged on both occasions, such as device language, timezone, IP address, and OS version.

  • Last-Touch Attribution - A model that establishes a match between the action taken by a user (e.g. app open, purchase) and its corresponding ad click or impression. When a user converts from an ad, the DSP that delivered the respective ad is given full credit for that conversion event.

  • Multi-Touch Attribution - Also known as multi-channel attribution. A model determines the value of every touchpoint on the way to a conversion. Rather than giving full credit to one ad, multi-touch attribution divides the credit among all advertising channels that the user has interacted with, leading to the conversion.
Attribution
attribution

A method of identifying the touchpoints a user has encountered within a specified period before making a conversion.

App Tracking Transparency (ATT)
app-tracking-transparency-att

The privacy framework from Apple that, among other things, manages the process of obtaining user consent before accessing their Identifier for Advertiser (IDFA).

App Monetization
app-monetization

The strategy a publisher employs to earn money from their app. This can be done through in-app advertisements, paid membership, and charging for premium features or an ad-free experience, among others. For example, some gaming apps are free to download and play, but users may need to pay in order to progress to the next level quickly.

Android Advertising identifier (AAID)
android-advertising-identifier-aaid

Also known as Google Advertising Identifier. A unique device identifier that Android generates and assigns to every device. Advertisers can use this to track user activity across apps, show them personalized ads, and attribute ad interactions.

Advertisers
advertisers

The advertiser is a person or legal entity focusing on generating sales and leads through serving ads that convey the right message to the right audience at the right time.

In mobile advertising, the advertiser is on the client-side and is the one interested in promoting an app.

Causal Impact Analysis
causal-impact-analysis

A measurement framework developed by Google that works without device IDs. It measures the incremental uplift of one or more conversion events, removing the influence of other campaigns and organic conversions. Used to assess the effect of ID-less campaigns.

Similar to measuring the effect TV ads have, the principle is based on running campaigns on identifiable sub-markets (test group), while leaving other sub-markets unexposed (control group).

Ghost Bids
ghost-bids

An incrementality testing methodology based on Ghost Ads, adapted for retargeting campaigns. The difference is that it removes all devices that are not seen on ad exchanges, or that would not be bid on, from both test and control groups, to reduce noise. A bid is placed as usual for the test group, while the control group is tracked with ‘ghost bids’ (bids that could have been placed, but weren’t in the end).

Return on Advertising Spend (ROAS)
return-on-advertising-spend-roas

A KPI that measures the relationship between the revenue generated by specific advertising efforts and the money spent on them.

Formula

Percentage: [Revenue ÷ ad spend] × 100 = ROAS%

Ratio: Revenue ÷ ad spend = ROAS

See also: Incremental Return On Ad Spend

Supply-Side Platform (SSP)
supply-side-platform-ssp

A company that works with publishers to sell ad inventory across ad networks.

Demand-Side Platform (DSP)
demand-side-platform-dsp

A company that works with advertisers to purchase ad inventory across ad networks. Their platforms are built to identify a desired ad space and place bids on it.

Compare with: Supply-Side Platform

Open RTB
open-rtb

A digital marketplace where ad inventory from multiple publishers are available for advertisers to bid on in real time.

See also: Real-Time Bidding

Self-Attributing Network
self-attributing-network

An ad network like Meta, Snap, and Twitter, that attributes its traffic internally, without the involvement of third-party MMPs.

Variable Bidding
variable-bidding

The dynamic adjustment of bid prices based on a user's in-app behavioral patterns, contextual information, time of day, and ad placement performance.

Dynamic Product Ad (DPA)
dynamic-product-ad-dpa

Also known as a dynamic ad. It is dynamically assembled based on the user’s behavior and information sourced from a feed. This type of ad delivers a tailored experience for individual users.

Real-Time Audience Segmentation
real-time-audience-segmentation

The division of an audience into distinct segments based on real-time events, thus enabling targeted advertising and alignment with a user's behavioral patterns and preferences.

User Acquisition (UA)
user-acquisition-ua

A mobile marketing effort used to attract new users to an app. Paid UA may refer to ads shown in mobile ad networks or social media channels, while non-paid UA involves app store optimization and promotion on the advertiser’s own channels.

Programmatic Advertising
programmatic-advertising

The automated process of buying and selling advertising space through digital platforms.

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