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Remerge and Fyber discuss app marketing trends for 2022

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Remerge and Fyber discuss app marketing trends for 2022

The impact of the App Tracking Transparency (ATT) framework has almost reached its ceiling, with the global adoption rate for iOS 14.5+ now at over 82 percent. The number of iOS bid requests for identifiable users has decreased since Apple's privacy changes took effect but as of November 2021, the volume of IDFA inventory still accounts for over 50 percent of the programmatic market. This amount is higher than many experts initially anticipated and enough for app marketers to advertise at scale on ID-based traffic.

A considerable portion of app marketers is still figuring out the best way to run iOS campaigns with SKAdNetwork. Meanwhile, others have shifted budgets to invest more heavily in Android and several players are turning to incrementality measurement to gain performance insights on no-ID traffic.

In the third part of our ID or No ID content series, we asked Güven Soydan, VP Product at Remerge, and Yoni Markovizky, GM Marketplace at Fyber, for their thoughts on what's happening in the programmatic advertising industry as we head into 2022.

Download the full version of the report to access all the insights.

Android and iOS traffic with ID has become the battlefield

Güven Soydan, VP Product, Remerge

Güven, what are your overall thoughts on the industry post-ATT?

What's interesting to me is that the players who have been the most vocal and proactive in embracing the privacy changes and implementing technologies that Apple has provided have not been the ones that have seen all of this as an opportunity.

There have been short-term rewards for those that have interpreted the policies as mere suggestions and did not rush into adopting them. Apple, for instance, stated that there would be absolutely no fingerprinting — yet 8 months into the "new world", SKAdNetwork continues to be the least preferred way of running user acquisition campaigns on no-ID traffic. This is not surprising as SKAdNetwork is just very different from what marketers are used to. To make data-based decisions in the short term, industry players will get whatever they can, be it numbers that make sense or numbers that are connected to some form of data structure. The confusion that surrounds the inability to easily create "human-data-insights-machine systems" with SKAdNetwork data has pushed the industry to keep using what works — until it doesn't (if that day ever comes).

On the other hand, technology teams still want to know the true value of an impression, and ID traffic on Android and iOS has become the battlefield. The competition is fierce for traffic that can be measured and optimized.

Across the board, we have seen companies focus on building out their creative technologies to better scale and optimize their campaigns (since powerful creatives will always bring benefits, regardless of user identities). And, of course, many have begun to work on contextual signals, an "old school" optimization strategy.

« App marketers must look at what they can do with the available traffic to provide value for their customers. »
Güven Soydan, VP Product, Remerge

What does this mean for marketers running retargeting campaigns?

Our post-IDFA dashboard shows that more than half of iOS inventory still has an IDFA. The bad news here is it might mean that even fewer customers are available to marketers, as the overlap of the people who have given consent in their apps may not be the same people we see on our internal dashboards. Therefore, app marketers planning a re-engagement strategy must find partners that can get as close as possible to covering all types of audiences out there. With the capability of processing 3.3 billion queries per second, I would say Remerge is perfectly positioned to be that partner.

App marketers must look at what they can do with the available traffic to provide value for their customers. They should spend some of their budget on increasing their footprint in the competitive marketplace while optimizing ruthlessly. The optimization decisions that didn’t matter back in the day (because of the abundance of available inventory) are now essential. This includes the personalized treatment of users, starting from the moment of impression all the way to the checkout. The customer-obsessed approach to advertising will be the new game.

What's the biggest challenge for advertisers at the moment?

While advertisers continue grabbing on to what they can understand and change, such as fingerprinting and contextual optimization, the biggest challenge within the programmatic space will be comparing results and using the analysis to make budget allocation decisions and set the right goals.

Although SKAdNetwork seems to be a reliable framework for measuring a fraction of what was possible, programmatic partners that do not have the SDK footprint of players such as Google and Facebook will not be able to measure their own impact.

The consolidation of partners that advertisers work with is inevitable. Not because of the operational benefits this brings, but because it provides more reliable measurement. This is especially true for methodologies such as Causal Impact, which benefit from more data per partner. I would love to see the prevalence of scientific and transparent approaches that create and nurture trust between partners. Advertisers that demand trust should succeed in the long run.

However, with the millions of moving pieces and mechanics that drive the market, we can only wait and see if it's logical decisions or the aggressive tactics of market leaders that define the future of the industry.

Privacy is a bigger priority than ever before

Yoni Markovizky, GM Marketplace Fyber, A Digital Turbine Company

Yoni, what's your take on the state of programmatic right now?

One thing is certain: our industry expected a catastrophe, but that didn't happen. Predictions ranged from a complete crash of some segments to a more conservative dip-followed-by-recovery — but it is evident now that many players were smart, well prepared, and quick to adjust, which enabled a smoother transition.

In approximately three to four months, app publishers tested and learned. They looked at challenges on the user acquisition (UA) front and went to the channels that work better post-IDFA. They rethought their monetization strategies — from picking the right vendors to setting up their waterfall and auctions optimally — and tested until they reached the strategies that work for them.

Moreover, there was a minor dip, and then an uptake. Fyber recorded similar results post-IDFA for publishers and in some cases, we delivered stronger results. The industry is strong and mobile advertising remains on the rise. The way we've dealt with the changes says a lot about the resilience of industry players, as well as their willingness to adapt to the new privacy era mobile users demanded.

Privacy is a bigger priority than ever for consumers, and the direction is clear. Apple made a significant step and the industry is following. Google's announcement of steps towards further restrictions of developer access to the Google Advertising ID (GAID), as well as additional privacy-aware policies likely to be rolled out in 2022, is yet another confirmation of this direction.

« If you want to win on iOS, you have to know how to work with users who don't share their IDFA. »
Yoni Markovizky, GM Marketplace, Fyber

What impact does this have for iOS?

If you want to win on iOS, you have to know how to work with users who don't share their IDFA. A strategy that relies on users who do is not scalable. The top DSPs that buy iOS on our platform at scale are those who achieve higher ROAS and conversions without having to rely on IDFAs.

The 50 percent of inventory on iOS that still has IDFA doesn't give us the full picture. In reality, while some users choose to share their IDFA with certain apps, it's not always enough when running effective targeting campaigns on iOS. Even if a user downloads an app after seeing an ad, that same user has to give consent to the new app they downloaded — there has to be a double opt-in move to enable effective targeting and attribution. So even if one out of two users gives the opt-in after downloading the app, it's still 25 percent, not 50 percent. And the actual numbers are lower; the US overall is currently at a 30 percent opt-in rate and other countries are lower, so the probability of having a double opt-in is effectively under 15 percent.

What market developments do you expect to see?

Consolidation will enable cross-promotion at scale. I expect privacy changes to drive even more consolidation in our market, among other causes. Companies that understand the importance of cross-promotion, where attribution is still possible, will be in the new privacy reality and will strive to consolidate with other parts of the value chain. They understand how this works and will look to scale up these campaigns under their umbrella.

What else will be important in 2022?

As individual user data isn't available anymore, audience segmentation will become useful for advertisers. This will require smart, extensive data work that looks at first-party data and the utilization of contextual targeting strategies. Advertisers can use contextual signals such as CTR and video completion to inform bid requests and only pay more for users who are likely to pay from a session.

Globally, users want more privacy, and their mobile is one of the main arenas for this discussion. Since the ATT rollout, we have learned that adapting to current solutions, testing more than ever before, and innovating are the keys to success in mobile marketing.

More insights from Remerge and Fyber

Access the full analysis by downloading the third edition of the ID or No ID series.

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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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