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Why app marketers should continue investing in retargeting

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Why app marketers should continue investing in retargeting

Remerge is a demand-side platform (DSP) that helps mobile businesses increase revenue from their existing app users by keeping them engaged with personalized mobile ads. The company has also enhanced its product for today's IDFA-less future, providing clients solutions for user acquisition on no-ID traffic and incrementality measurement.


Pan Katsukis shares his thoughts on the state of the mobile advertising industry, in-app retargeting, and the early days of Remerge. The complete interview is part of Remerge’s Apptivate podcast series which is hosted by Tommy Yannopoulos, Director of Sales, Americas.

Pan, tell us about how you got to where you are today?

I started out in the mobile industry back in 2004, when there was no real smartphone out there. In 2008, I co-founded my first company, madvertise, which is a European ad network. We were one of the first mobile programmatic platforms in the market. I was its Chief Product Officer for 6 years and during that time I learned a great deal about the ad tech industry.

In 2014, I co-founded Remerge. Our aim was to solve the retention problem in the mobile industry; advertisers were spending a lot of money on user acquisition, but not many people were using their apps. We set out to create an easy and scalable product to overcome this issue. Our vision was to leverage the programmatic infrastructure and provide scientific insights into the performance of campaigns with the use of incrementality. These areas are still two of our biggest product offerings.

What inspired you to build a company like Remerge?

Our ultimate goal was to create an environment where everyone loves coming to work. Why is this important? Well, humans are at the heart of good marketing, and how people feel is a huge part of a company's culture. The experience and results we deliver for customers are inherently better when people enjoy their tasks and their place of work. We are very open and transparent at Remerge. For instance, employees always have access to our financial numbers, how we are spending money, what's in the bank, and the reasons behind why we make tough decisions.

When we thought about what we wanted to do as a business, it came down to three main elements. The first was to ensure our tech was a clear differentiator. The second was that we could scale globally. And the third was building a company that could adapt to a fast-growing market.

« The ways app marketers interpret campaign results have changed, so they must test and learn to prepare themselves for the future. »
Pan Katsukis

What changes will marketers face when adapting to life with ATT?

The biggest challenge for app marketers is rethinking their infrastructure and setup with ATT. SKAdNetwork is evolving as a new standard and works differently from other known attribution providers. The ways app marketers interpret campaign results have changed, so they must test and learn to prepare themselves for the future. There is a lot of uncertainty in terms of where to spend budgets, but I think the opportunity is big. Even if marketers have a strong base of iOS users using the newest version of the operating system, there's still an extremely valuable audience to tap into and they cannot simply stop spending on iOS.

Also, there are tools such as incrementality with causal impact that can overcome the limitations of SKAdnetwork. This works on no-ID and marketers can already use it without looking at SKAdNetwork and all the problems attached to it.

What does incrementality provide that SKAdNetwork does not?

Incrementality has been on the rise for several years now and I believe the majority of marketers who have a 6-digit monthly marketing budget have been looking into this methodology. The benefit of this methodology is that it provides performance results on a scientific basis. For example, marketers can discover if a specific campaign really drives incremental value or if customers would have converted without the campaign. This year, we’ve worked with some of our clients to test incrementality measurement on no-ID and have achieved impressive results.

What opportunities are available in the privacy era?

Right now there is a massive pool of valuable users. The price of no-ID inventory is 60% cheaper than ID inventory, so I would strongly advise app marketers to take advantage of this to test new approaches. We launched a post-IDFA dashboard in April and this offers freely available data to the market on important trends for bid requests, CPM changes, iOS version usage, and SKAdNetwork-enabled traffic. Marketers can use this to help plan their programmatic campaigns and ad spend.

What does a winning formula look like for a DSP in the future?

DSPs operating in the privacy-first world must adopt and embrace two things. One is science and technology. In the past, ID traffic was enough. But now, it’s imperative to look at and understand much more data. A DSP needs an intelligent contextual bidding setup and extremely high scale to test and learn everything about each bidding opportunity. Incrementality measurement is also key - this allows a DSP to understand if a marketing campaign is actually creating an impact.

The other element is humanity. Messaging, contextualizing, creating emotions, and engagement are all becoming more important. DSPs must understand a campaign strategy in detail, create the most engaging creatives, and place creatives in the right context for the user.

« The reality is that there is a long way to go before retargeting dies out - it is still a valuable marketing strategy. »
Pan Katsukis

Can you talk about the state of retargeting?

The impact of the iOS 14.5 rollout has not been as dramatic as we anticipated. In fact, at Remerge, we have seen much more business, more conversions, and more opportunities. Our retargeting revenues grew month over month in May, June, July, and August. Our dashboard shows that the number of ID bid requests is stable and has remained high since June. App usage continues to rise around the world and a solid retargeting strategy helps a business easily improve its marketing KPIs.

Why is the perception of retargeting different from reality?

I can understand why some marketers are a little hesitant when investing in retargeting campaigns, particularly with all the talk about the death of ID-based marketing. But the reality is that there is a long way to go before retargeting dies out - it is still a valuable marketing strategy. Marketers are losing out if they do not test in the new environment - this is true for no-ID inventory and for the remaining ID inventory.

Will revenues continue to grow or will ATT have a bigger effect?

We are at an adoption rate of 80% with the ATT framework for iOS. 
It will likely go to 90% or 100%, so we are nearing its peak. However, we are still able to run retargeting at scale
. For those who are maxing out retargeting on iOS, performance is likely to go down - because they cannot scale as easily and reach more users. 
But there is a real opportunity for marketers who are not doing any retargeting on iOS. They can still grow their business with this strategy.

Why do you think there are high levels of market consolidation?

There are several factors driving consolidation. The pandemic has accelerated the growth of app usage and digital advertising, and I think more businesses want to be a part of this trend. A focus on privacy has led to companies bringing more data under one roof and creating their own walled gardens. There is also more money in the market; financial institutions are buying and merging private organizations to create synergies and take them public.

Are there advantages to being an independent player in ad tech?

I think it really comes down to a company's mission and why they are in the market - what problem do they want to solve? What do they want to achieve? The Trade Desk, for instance, is the entry point to programmatic for big agencies. They have been an independent business from day one and are now one of the most valuable ad tech organizations around. They are successful because they defined a very good value proposition that focused on their customers' needs.

Also, it's always been a stretch to tap into both the supply and demand side of the industry. There is a conflict of interest: who should a company serve more? Publishers or advertisers? It's important to find a compromise and balance. It didn't work out for companies like Millennial Media or Rocketfuel, but maybe today's ad networks can address this situation in a better way.

What excites you about the future of Remerge?

This is an exciting phase for us. The topic of privacy is everywhere and it is shaking up the market. We have always been at the forefront of privacy. We are a data processor, which is the highest privacy class under GDPR. We have never mixed customer data or created device graphs by collecting data from different sources. We don’t use or store data on AWS; we have our own bare metal server infrastructure set up all over the world. We put all our resources into building great solutions and results for app marketers.

The state of retargeting in the privacy-first era

Listen to the full conversation with Pan and Tommy by tuning into episode 100 of our Apptivate podcast: The State of Retargeting in the Privacy-First Era

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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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