Table of Contents


How will targeting audiences in digital advertising be affected?

SKAdNetwork

Attribution and measurement will be explored in more detail – see section 3: How will measurement and attribution be affected?

For where an IDFA is not present, Apple has designed a solution that allows for permissionless (i.e. does not require user consent), privacy-centric, aggregated attribution: SKAdNetwork.

SKAdNetwork is not new. It was released in 2018 to help marketers track users who turned on the Limited Ad Tracking feature. However, the recent updated version has more functionality.

SKAdNetwork will allow marketers to measure attribution of mobile advertising on iOS. It will provide a variety of functions for mobile advertisers:

  • Reliable, but aggregated attribution
  • The potential for less ad fraud on mobile as the sources of conversions are digitally signed by Apple (more details later)
  • Cross-channel/network measurement including the walled gardens platforms like Facebook and Google.

But it isn’t perfect:

  • No user- or ad group-level data
  • Maximum of 100 campaigns per ad network
  • Questionable ability for view-through or multi-touch attribution
  • Restricted to a single conversion value metric per app install
  • Less precise revenue reporting.
How SKAdNetwork works

Step 1

A user clicks on an ad for your app inside another app, such as Twitter.

Step 2

They install and open your app. Postback data is collected (including Ad Network ID, Campaign ID, App ID, Transaction ID, and Conversion Value) and stored on the user’s device. A 24-hour conversion timer is then set live. In that timer, multiple conversion events (such as a registration or in-app purchase) can be recorded, and the timer is reset every time an event of a higher priority than the previous event is recorded. The highest value conversion is then posted back. For more details, see section 3.5 Events and ConversionValue.

Step 3

When the timer expires 24-hours after the last post-install event, the postback data is sent to the ad platform or Mobile Measurement Partner to be reported on.

The advertising performance implications of SKAdNetwork are wide-ranging across targeting, attribution, and reportable performance. These will be discussed in the following chapters of this document.

How will targeting be affected?

Broadly speaking, targeting audiences in digital advertising will become more limited with the absence of IDFAs. It is expected that targeting will become more contextual, rather than based on user attributes and behaviours.

There is a feeling that this might mean advertisers increasingly use digital channels for higher up the marketing funnel. If people can not be determined to be in-market and giving buying signals so easily, then performance marketing at the bottom of the funnel might become harder. By focusing on context, app marketing will be forced into reaching broader audiences who will be less ready to buy.

Contextual targeting

Targeting will be based mainly on data provided by end content providers suggesting details about the intended audience. E.g. app category and genre, audience interests, page content.

There will be severe limitations to other forms of targeting which will detrimentally impact performance marketing and its ability to find high value users and decrease wastage.

Website custom audiences (Retargeting and Lookalikes)

Website custom audiences of most (perhaps all) types are expected to decrease in size as people refuse to give consent to be tracked. The small percent of people who do give consent will be invaluable here, but the absolute number of people might be too small to build effective custom audiences. Where in the past website custom audiences would be split by user behaviour (i.e. build a retargeting audience for each stage of the user journey, or a lookalike audiences for ranges of value conversion), marketers may have to group as many valuable events together as possible to make any one new website custom audience big enough.

People who do not give consent will not be available for retargeting and lookalike audiences using pixel-based approaches. This is because it will not be possible to match a person from your audience on your app or website back to the same person on non-owned apps.

It may still be possible to use CRM data such as email addresses, names, phone numbers and so on, for the purpose of building lookalike audiences. But it would go against Apple’s privacy guidelines, and any app discovered doing it may be punished by removal from the app store. Although it is hard to see how Apple will be able to police it. So do this at your own risk, and speak to an Apple representative for confirmation.

It is important to remember that consent will need to be given by both the advertiser’s website/app and the publisher’s website or app. As such, successful crossover is likely to be very small.

Personalisation of creative

Some ad personalisation will be limited. Personalisation using contextual or publisher-provided (2nd party) data will be largely unaffected. However, personalising creative using 1st party data (say for dynamic retargeting ads) will rely on successfully gaining consent on the brand and publisher apps hosting the adverts.

Sequential targeting

Sequential targeting across different publishers will see very little scale as it would need the user to be opted in to sharing IDFA consent across too many 3rd parties.

Sequential targeting of creative within the same app publisher may be slightly easier because there are fewer times consent is required to be granted. But realistically, sequential targeting will be another tactic with minimal scale.

Location-based targeting

Hyper-localised targeting will be lost without an IDFA. Instead Apple is happy to share an approximate location. How approximate this is remains to be seen. But this shouldn’t impact campaigns and performance too much as hyper-localised campaigns offer less scale and therefore tend to not move the needle quite so much.

A/B Testing

Due to the lack of insights more granular than at a campaign level, the ability to do extensive A/B testing is harmed. Testing the post-click (read: metrics such as conversion rate, and purchases and ROI) impact of small scale factors such as CTA button colours or ads’ colours will not yield a set of comparable results unless separate campaigns are built for each. As such, due to the limit of campaigns allowed per ad network, A/B testing programs won’t be able to manage as many concurrent tests as might be desired.

Lastly, most tests that operate purely within one channel should still be able to yield insights into what drives media metrics (i.e. engagement metrics like click-through rate and cost-per-click).

Tactics used to limit ad exposure to audiences

There are obviously numerous times a marketer will want to purposefully withhold their advertising from certain people. iOS14 will have a detrimental effect on each of these times in those audiences that opt-out of tracking. Not only will this harm performance, efficiency and wastage, but it will also make the user experience of advertising worse.

Frequency Capping

Limiting the number of times an advert is shown to an individual will be harder on many channels. Frequency capping at the device level will become harder (but within the same session will still be fine). Where log-in data would be available, such as on Facebook and Google, Apple is clear that non-IDFA attempts to identify users is also banned without consent. Therefore frequency capping on these platforms will also be hampered hugely.

There are potential workarounds, but are not 100% effective. Sources of knowledge such as IP address might be usable, but they are not as accurate. Alternatively, app developers have their ID for Vendors (IDFV). This is an ID that a publisher can use across its multiple apps. So this might help frequency cap across larger companies such as Google.

Frequency capping at a creative level across multiple channels will not be possible.

As a result, marketers should be ready for audiences to be overexposed to your adverts. Creatives will need to be part of a larger library of variety, changed regularly and used for less time. Ad fatigue is expected to set in quicker (in terms of time, rather than # of impressions).

Exclusion audiences

With the absence of an IDFA, creating accurate exclusion audiences from 1st party data will be harder. So preventing people from seeing ads who are existing users or customers will be impossible using pixel-based methods. It may still be possible and permissible to exclude people using CRM data (matching email address gained from user to the same user in a signed-in environment like Facebook and Google).

Audience overlaps

Creating (and maintaining) distinct audiences where there is minimal overlap of people will be harder. Even platforms with an enforced log-in such as Google and Facebook do not expect to be able to do this because Apple has made it clear that non-IDFA identifiers will not be allowed without consent.

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