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How are data privacy changes affecting mobile marketers in Asia?

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How are data privacy changes affecting mobile marketers in Asia?

This content was originally produced by Tech in Asia Studios, which connects brands with Asia’s tech community.

Personalized advertisements are hardly new in the mobile space, but advertisers haven’t had it easy in reaching out to their target users as doing so involves keeping in mind consumer data privacy laws. Then there’s the other hurdle of complying with the regulations meted out by the platforms that advertisers rely on to run – all while creating personalized experiences for consumers, a proven formula for boosting user engagement and in-app purchases.

Tech giant Apple is one case that’s shown it has the ability to influence advertisers’ marketing campaigns – and, in turn, create shifts in the advertising industry. In 2017, the company made the decision to block third-party cookies on its web browser Safari, causing advertising revenues to dip by 40%.

This privacy-centric framework was the prelude to a shift in the in-app advertising regulations space that was about to take place.

An industry-wide upheaval

In April this year, and with conversations surrounding user privacy on the rise, Apple introduced its updated iOS14.5, which removes the ability for advertisers to access user-level information – in turn potentially affecting their bottom lines.

Although Southeast Asia has more Android users, iPhone owners have proven to be more willing to shell out money for in-app purchases, averaging US$12.80 per purchase compared to the US$6.20 that their Android-using counterparts spend.

And Apple’s decision has stirred the advertising industry, since mobile users will now have the power to decide whether to share their data with third-party providers through an in-app dialogue pop-up.

With the tech giant's new privacy framework under its updated iOS14.5, advertisers must rely on the company's SKAdNetwork, an attribution model that shares conversion data with advertisers without revealing specifics. For instance, a user who's made an in-app purchase would have that conversion data shared with advertisers, but without trackers such as their personal ID.

“This could result in sales losses of between 4% and 8%, although the number should recover over time once the uncertainty surrounding these changes subsides,” notes Pan Katsukis, CEO and co-founder of Remerge, a Berlin-based demand-side platform (DSP).

In the face of these changes, marketers will need to experiment with new ways of leveraging tried and tested methods such as creative personalization and attribution to run successful advertising campaigns.

« We've optimized our platform to calculate the optimal bid price for ad space and in a way that won't require user IDs »
Pan Katsukis

Adapting to change

As Southeast Asia and the rest of the world shifts toward a no-ID future, the path ahead for mobile advertising is quickly pushing DSPs to reassess their methods for success – whether by improving their data-processing capabilities, or introducing innovative tools to improve the efficiency of their ad campaigns.

Remerge had already preempted this at the end of 2019, given that it had been closely following Apple's privacy-centric changes. This, alongside its presence in the Asia-Pacific region since 2017, has given the company leverage to stay ahead of changes in the mobile ad industry – in turn helping advertisers worldwide to maintain or grow their bottom lines.

"Anyone who's followed Apple's developments over the past few years knows that the topic of privacy has been the focus of its corporate strategy for some time," shares Katsukis. "It was clear to us early on that this would also affect advertising, which is why we doubled down on developing a strong post-IDFA solution."

By the time Apple announced its plans for a privacy-driven app environment last year, Remerge was well-prepared for the change. Katsukis offers the example of a recently launched product line: an in-app user acquisition campaigns feature, which is cost-efficient and can be run without user IDs. In other words, it’s a win-win for both the advertisers and consumers.

"We've optimized our platform to calculate the optimal bid price for ad space and in a way that won't require user IDs," he says, adding that a DSP's success would now greatly rely on how sophisticated its algorithms are and how quickly it can process bid requests within a programmatic buying or real-time bidding (RTB) environment. Programmatic buying, then, refers to the purchase and sale of digital ads, while RTB allows advertisers to bid for ad space through an auction model.

In Remerge’s case, its platform can process 3.3 million queries on advertising opportunities per second, almost 50% more than the next best competitor.

The no-ID future

To enhance ad campaign performance optimization, Katsukis believes that more advertisers will now turn to incrementality tests to complement Apple's SKAdNetwork.

"Advertisers need guidance in the no-ID world, and the SKAdNetwork may not offer enough insights," the Remerge CEO and co-founder points out, adding that being able to measure the re-engagement impact of an ad campaign is what makes incrementality a necessity for advertisers in the post-IDFA era.

Incrementality tests involve assessing the effectiveness of marketing campaigns by differentiating the given campaign's impact from organic user behaviour, and the result of any other marketing campaign. These results properly indicate the true efficacy of a campaign by looking at install and re-engagement metrics – that is, a user's exposure and engagement with advertisements for products or services that they had previously shown interest in.

One case in point is Remerge’s incrementality solution, which was developed to give advertisers added proof of the value of their ad spend – while allowing them access to real-time data and user retention numbers.

These incrementality results would ultimately feed into the larger goal of helping marketers push out more effective and creative personalization campaigns in the post-iOS14 age.

To this, Katsukis suggests that the future of personalized advertising will be done on a contextual level and draw from aggregated data, such as the city in which a user is located and at what time of day.

"For example, a food delivery company might display a mobile advertisement with a lunch coupon in the city where its potential users are located," he adds.

Given that advertisers have long relied on the tried-and-true formula of programmatic ad buying to deliver hyper-personalized ads to target users, the Remerge co-founder acknowledges that their hesitance to experiment with new campaign alternatives is natural. But that shouldn't serve as a blockade.

"Because of this hesitance from marketers, no-ID inventory will become cheaper in the short-term, making it possible to run more campaigns and still achieve good results," Katsukis says. "As Apple pushes ahead, it's important as a marketer to monitor such developments in order to be at the forefront of data-protection compliant solutions."

About Remerge

Remerge is the leading programmatic platform for high-performing, privacy-compliant app marketing campaigns. It focuses on scientific methodologies and transparent communication to deliver credible results and increase app growth.

Find out more about Remerge by reaching out here.

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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.

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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

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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

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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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