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Apple Is Changing How Digital Ads Work. Are Advertisers Prepared? by Julian Runge and Eric Seufert

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Apple Is Changing How Digital Ads Work. Are Advertisers Prepared?

by Julian Runge and Eric Seufert Published on HBR.org / April 26, 2021 / Reprint H06BYT

Normform/Getty Images

Apple is turning the privacy settings of its mobile ecosystem upside

down. When it releases its app tracking transparency (ATT) framework

with iOS 14.5 on April 26, it will shut off a stream of data that app

developers, measurement companies, and advertisers have used to link

users’ behavior across apps and mobile websites — a move that could

reshape the digital advertising industry. With the update, the “identifier

for advertisers” (IDFA), which has been activated by default on Apple

devices and provides access to user-level data to app publishers, will be

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switched off and users will need to grant apps explicit permission to access

it. With in-app prompts asking users, “Allow [app name] to track your

activity across other companies’ apps and websites?” opt-in rates will

likely be low.

We anticipate that Apple’s ATT initiative will deliver a major blow to

targeted advertising, which is crucial to the business models of publishers

of online content such as Facebook, Google, and many news outlets. But

while large digital content providers will feel the effects of ATT, the large

proprietary datasets they’ve amassed may protect them in the long term.

Smaller companies, such as e-commerce operations that rely on targeted

advertising to reach customers, and mobile measurement providers, which

collect and organize app data, will likely find it harder going — a point

Facebook has tried to bring home in a campaign responding to Apple’s

policy changes.

Through the rollout of ATT, Apple is re-imagining the role that advertising

plays within its ecosystem. The move will allow the company to more

tightly control users’ app experiences and content curation. It will also

allow Apple to push adoption of its own target advertising solution — its

in-house ad tracking services use friendlier language than what is required

of third-party apps and it recently introduced new ad spots on the App

Store. Establishing itself as a leader in privacy can serve to strengthen its

brand and have lasting positive effects on its hardware sales to boot.

While ATT might be the most impactful change to the digital advertising

ecosystem to date, more restrictions around user privacy are in the offing.

Developments such as private click measurement (PCM), Google’s

Federated Learning of Cohorts (FLoC), the end of third-party cookies in

Chrome, and governmental privacy regulations such as GDPR and CCPA

all point to a new privacy-centric era on the horizon. That means that

advertisers and advertising firms need to learn how to play by a new set of

rules — and fast. Here’s a primer on how you can be prepared to navigate

the changes.

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For the exclusive use of J. Li, 2022.

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What ATT Changes

Apple’s new approach to privacy presents a clear problem for advertisers

who rely on targeted advertising — in other words, most digital advertisers

— in that it will make it much harder to meaningfully link user behavior

across apps and mobile websites in the iOS ecosystem. Depending on opt-

in rates (which, again, are expected to be low), this presents a major

challenge for advertising targeting algorithms that achieve their current

good performance by observing not only what ads users view and click on,

but also who then proceeds to take relevant actions on the website or in

the app of the advertiser.

Overall, ATT can be expected to make ads substantially less relevant for

consumers and to make them perform substantially less well for

advertisers — except for ads delivered by Apple’s own personalized ads

system. It also reduces the precision of advertising measurement across

iOS apps and mobile websites. Many industry insiders expect Google to

make a similar change in the Android ecosystem at some point in the

future, effectively rendering digital advertising less relevant across the

board and its measurement much less granular and precise.* These

changes in the digital measurement landscape roll back some of the

innovations that became possible through digitization, namely precise

measurement through user-level attribution and advertising experiments.

To aid advertisers in navigating the limitation in data availability

introduced by ATT, Apple is offering a measurement solution called

SKAdNetwork (SKAN) that makes performance data available at the

campaign level. However, not only is there a limit on the number of

available campaign slots per advertiser, SKAN also adds a random time

delay on the observation of performance events such as purchases or cart-

adds and restricts how and how many of such events can be observed per

campaign.

SKAN falls within the sphere of differential privacy, an approach to

marketing measurement that uses statistical methods to make it

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impossible to infer any individual user’s behavior while still allowing

linking of behavior across different digital properties. Differential privacy

is likely to become more prevalent. Other tech companies, such as Google,

are investing significantly into such technologies as well but there may be

a long way to go before wide acceptance and adoption as a new privacy-

safe measurement approach.

In the meantime, more traditional measurement solutions that are

privacy-safe by default will likely stand to gain in relevance. For example,

marketing mix models (MMMs) were developed on and for aggregate

advertising and sales data observed over time and do not require any

linking of lower-level tracking data. They make use of natural variation in

a firm’s marketing mix or, where possible, of explicitly induced

randomization over time and/or geographies to measure advertising

effects. Bearing testament to the likely renaissance of MMMs in marketing

measurement, Facebook published an open-source computational package

that allows advertisers to implement MMMs in a guided manner.

How You Can Adapt

So what should advertisers and advertising firms do? We believe that

internalizing the following strategic viewpoints can help businesses

navigate this changing privacy landscape.

1) Embrace privacy preservation methodologies like differential privacy (Apple) and federated learning (Google). These are the primary means by which large platforms are ushering in new privacy protections

for consumers — firms that are planning ahead should build advertising

technology that aligns with them.

When privacy policy changes, the biggest pain point for advertisers is

infrastructure upgrades. This sea change should be seen as an opportunity

to invest in new and innovative technologies that not only comply with

platform regulations but do so in a way that is forward looking. New

restraints on the data that can be used for measurement and analysis can

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create competitive advantage in moments of dramatic change, when

competitors are reticent to invest or adapt.

2) Understand that workarounds to new privacy regulations are not a viable, long-term solution. It may seem relatively cheap or straightforward to build solutions that preserve advertising workflows and

measurement schemas by sneakily contravening platform policies — using

device fingerprinting or server-to-server conversion management — but

taking this approach merely delays the inevitable pain of adaptation. A

firm should make investments into real solutions, not gimmicks that

exploit loopholes or are predicated on rules not being fully enforced,

especially since the privacy landscape is currently mostly dictated by large

platforms that mostly operate according to their own rules.

3) Transition advertising measurement away from deterministic, user-centric models. Instead, use more holistic, macro-level models that look at variations in ad spend and revenue over time to attribute efficiency

to channel-specific ad campaigns. This approach requires sophisticated

data-science expertise, and these types of models can be difficult to tune

properly, but a measurement solution that relies on statistical

sophistication is more robust and durable than one that relies on the

precision of user identity. Tools like MMMs not only provide insight from

data that is readily available and affirmable such as revenue and ad spend,

but they also allow for traditional advertising channels such as television

and out-of-home to be included in the advertising media mix and

accommodated for in measurement.

4) Deepen your understanding of your audience and rely less on niche products. The products that suffer most in the loss of the identifier- based advertising targeting are those that target niche audiences and

depend on very high rates of monetization participation, or very extreme

levels of monetization from a small segment of the customer base. Building

a more broadly appealing product is a strategy for overcoming the

degradation of advertising effectiveness: The more people that are

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For the exclusive use of J. Li, 2022.

This document is authorized for use only by Jia ye Li in MIS 441 - Global E-Commerce-1 taught by Richard Johnson, Washington State University from Jan 2022 to Jun 2022.

receptive to your product, the less targeted your ads must be in order to

reach customers.

5) Get more creative and use it as a means of differentiation. Absent the targeting capabilities that are unlocked with device identifiers and

behavioral histories, advertisers can focus on ad creative as a way to

increase the reception their ads receive with potential customers. Novel,

creative, and attractive ads can’t fully replace the efficiency lost in digital

advertising from the deprecation of advertising identifiers, but it can help

to reach the most relevant segment of an audience by penetrating through

generic, nondescript advertising from competitors. With precision

targeting largely removed from the advertiser’s toolbox, ad creative can be

used as a way to stand out to the most appropriate portions of the broader

audiences to which ads will be exposed.

Apple’s ATT framework may be the most economically impactful and

brazen change to privacy policy in years. It won’t be the only one,

however. As this step is likely the start of a new era rather than an outlier

event, we recommend using the opportunity to brush up on privacy

technologies such as differential privacy and federated learning and to

sustainably revamp your marketing measurement toolkit.

*Correction: An earlier version of this article stated that Google had announced a similar move in the Android ecosystem. Google has not publicly announced this change.

Julian Runge is a behavioral economist and digital marketing researcher. He holds a Ph.D. in Economics and Management Science from Humboldt University Berlin and was a repeat visiting researcher at Stanford University. Julian also worked as a researcher focused on academic collaboration in Facebook’s marketing science research group. He currently advises companies how to strategically use data, science, and experimentation to fuel their growth and customer experiences.

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For the exclusive use of J. Li, 2022.

This document is authorized for use only by Jia ye Li in MIS 441 - Global E-Commerce-1 taught by Richard Johnson, Washington State University from Jan 2022 to Jun 2022.

Eric Seufert is a media strategist, quantitative marketer, and author who has spent his career working for transformative consumer technology and media companies. He is the author of the book Freemium Economics and developed Theseus, an open-source Python library for marketing cohort analysis. Eric now runs Heracles, a strategy consultancy that specializes in marketing science and growth strategy; Mobile Dev Memo, a mobile advertising and freemium monetization trade blog; and QuantMar, a knowledge-sharing platform for quantitative marketers.

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For the exclusive use of J. Li, 2022.

This document is authorized for use only by Jia ye Li in MIS 441 - Global E-Commerce-1 taught by Richard Johnson, Washington State University from Jan 2022 to Jun 2022.