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App Talk by Remerge - Seoul 2026 Highlights

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App Talk by Remerge - Seoul 2026 Highlights

App Talk by Remerge - Seoul 2026

A Glimpse into Asia’s App Growth 

On April 15, 2026, Remerge hosted App Talkin Seoul 2026, an app marketing event centered on one big question: “What does it take to grow an app in Asia today?” The event explored AI‑driven campaigns, retargeting in the privacy‑first era, and effective growth strategies - offering app marketers a practical blueprint for success. 

Through sessions such as AI in Asia 2026, Retargeting Revival in the Age of Privacy, and 2026 Gaming Patch Notes, the program highlighted both challenges and opportunities, painting a clear picture of how apps can thrive in Asia’s quickly-evolving digital landscape. Below is a look at how the day unfolded and what marketers can take away.

Dive in below for video recaps, photos and summaries of the guest talks that took place.

Sensor Tower: AI Trends in Asia 2026

In 2025, AI moved beyond being just a technology trend and reshaped the global ecosystem. With 3.8 billion downloads, $5 billion in revenue, 480 hours of consumer time, and 1 trillion sessions, the sheer scale of these figures demonstrates the explosive growth of AI. What stands out most is the 240% year-over-year increase in sessions, a clear sign of surging user engagement.

Asia has emerged as the driving force behind this momentum, recording growth far above the global average. South Korea alone saw 54 million downloads—double the previous year—and revenue skyrocketed sixfold to $235 million. Japan, Vietnam, China, and other Asian countries also achieved more than double growth, confirming that the center of gravity in the AI ecosystem is shifting toward Asia.

Within this surge, the rapid expansion of AI assistants is particularly noteworthy. ChatGPT and Gemini have evolved far beyond simple assistant roles, continuously expanding their capabilities and securing cross-platform market share across Asia’s top five markets. While web-based access was dominant until 2024, by late 2025 the momentum had clearly shifted toward mobile apps, cementing the importance of mobile-first strategies. This pivot has fueled dramatic increases in both in-app engagement and overall audience size, prompting marketers to shift from traditional SEO-focused tactics to GEO strategies. In places such as South Korea and Vietnam, AI is now woven into everyday life, and advertising has diversified to reflect distinct cultural and national identities. 

The Asian AI market can be defined by three major trends. First, Asia has become the global center of AI growth. Second, AI is expanding beyond workplace assistance into everyday life, reshaping the ecosystem. Third, success now depends on quickly identifying trends and responding with tailored strategies. For companies and marketers, it is no longer enough to simply adopt AI; they must design diversified strategies that reflect Asia’s unique growth patterns and audience characteristics. 

« Asia has become the driving force of global AI growth, with the technology moving beyond workplace tools to become part of everyday life and reshaping the wider ecosystem. In this fast-changing environment, the ability to quickly identify trends and respond strategically is critical. For companies and marketers, simple adoption is no longer enough. They must craft diverse strategies that align with Asia’s unique growth patterns and audience dynamics. »
DJi-sung Yoon, Account Director, Sensor Tower

Stella Yun, Account Director - Sensor Tower

Remerge X AppsFlyer: iOS Retargeting Revival in the Privacy Era 

The mobile marketing industry is entering a new era shaped by privacy. In one session, Hide Cho, Senior Regional Director for Korea & Japan at Remerge spoke to AppsFlyer’s Dae-hoon Jung, Director of Customer Success for Korea & Japan about how these changes are fueling the resurgence of iOS retargeting. They covered macro trends such as antitrust regulations and the halting of Google’s Privacy Sandbox, as well as micro factors like ATT opt-in rates, probabilistic modeling, and probabilistic targeting on iOS. Together,they provided a comprehensive view of the challenges and opportunities that today’s app marketers face.

When Apple introduced ATT, many believed the data had disappeared. In fact, conversions remained, but visibility became blurred. While Android leads in user volume, iOS delivers far greater value per user. Retargeting conversion rates highlight this disparity: Android increased by 31%, while iOS surged by 381%. As a result, marketers are once again expanding iOS budgets, combining deterministic matching via user IDFAs and deep links with probabilistic matching powered by contextual signals to achieve more precise targeting. 

Probabilistic modeling has emerged not as a replacement but as a complement -bridging the gaps where deterministic signals are missing. Tests have demonstrated strong reliability, with 75% coverage and 98% accuracy. In live campaigns, this approach translated into a 110% lift in conversion rates and a 15% improvement in cost efficiency. 

The revival of iOS retargeting can be explained through three pillars: the gradual rise in ATT opt-in rates, the advancement of targeting technologies, and the complementary role of probabilistic modeling. AppsFlyer recovers lost signals, Remerge optimizes and scales them, and together these solutions form a new growth formula for iOS marketing. 

Dae-hoon Jung, Director of Customer Success for Korea & Japan - AppsFlyer
Hide Cho, Senior Regional Director, JP & KR - Remerge

AppsFlyer: From MMP to AI Marketing Cloud - the New Vision 

AppsFlyer is evolving beyond its role as a Mobile Measurement Partner (MMP) to become a comprehensive AI Marketing Cloud. Building on its strengths in data accuracy and mobile optimization, the company is now extending these capabilities to the web, enabling major platforms to directly optimize for web conversions. With a unified dashboard across apps, web, and consoles, AppsFlyer delivers three core values:

1. Real-time postbacks
2. Media optimization
3. Campaign performance improvement

All of this helps to create an environment in which marketers can more effectively drive results. 

What stands out most are the AI-powered updates. On the audience side, AI learns and optimizes to build more precise targeting segments. On the creative side, the Creative Analytics solution automatically organizes and analyzes vast volumes of ad assets, predicting performance outcomes. High-performing creatives can be instantly synced with Meta accounts, enabling AI to manage large-scale analysis and optimization tasks that would be impossible for humans to handle manually. 

Privacy remains central to this transformation. Through its Privacy Cloud and Signal Hub, AppsFlyer enables secure collaboration without compromising personal data. Partnerships with global players like Mastercard are already underway. LLM-powered conversational dashboards, a suite of agents currently in beta, and interfaces directly connected to the agent builder tool further enhance this ecosystem, allowing marketers to focus on creativity and strategy rather than routine tasks. AppsFlyer is redefining itself as a modern marketing cloud, embedding AI across platforms, audiences, creatives, and signals to drive transformation at speed.

« AppsFlyer is no longer just an MMP but a modern marketing cloud. With cross platform expansion, AI audiences, Signal Hub, AI Chat, and Agent Hub, we are driving transformation faster than ever. This year, we are preparing product updates at an even greater pace than last year, and we invite you to look forward to the changes AppsFlyer will bring as a true marketing cloud. »
Dae-hoon Kang, Senior Account Manager, AppsFlyer

Gojek: Smarter Campaigns with Machine Learning 

Shindu Ramandita, Senior Performance Marketing Manager at Gojek, shared how the company is using machine learning to enhance performance across the entire marketing funnel. As Southeast Asia’s largest on‑demand platform, Gojek operates in Indonesia and Singapore with more than 3.1 million driver partners. Like many in the industry, the team has faced rising media costs, restrictions on data usage, and challenges in measurement. To address these, they defined clear problems and goals at each stage - awareness, consideration, acquisition, and retargeting -while focusing on both efficiency and impact.

At the awareness stage, Gojek adopted a hybrid strategy that combined reservation and auction models to maintain efficient CPMs while expanding reach. For acquisition, the team collaborated on a four‑month project using Firebase’s tROAS functionality, which drove large‑scale new user growth and delivered up to a 40% increase in MAU. In retargeting, experiments with Demand Gen and GDA strengthened conversion‑focused strategies, shifting the emphasis from traffic volume to securing high‑quality users who contribute to real business growth. Shindu emphasized that balancing marketing goals with cost efficiency was the central challenge and shared the diverse approaches tested at each stage. 

The standout innovation was the use of predictive models powered by machine learning. Since 2024, Gojek has been using XGBoost to handle non-linear relationships between variables. This enabled the company to predict performance with up to 90% accuracy at D+1, without waiting for the 7-day attribution window. As a result, budgets could be optimized from day one based on CPB, with flexible investment strategies across channels depending on peak and off‑peak seasons. When prediction accuracy declined, the team intensified monitoring, interpreting larger errors as signals of performance deterioration and scaling back budgets accordingly. 

Collaboration was another key driver of success. By partnering with companies like Remerge, Gojek shared machine learning predictions and target values to improve visibility and ensure alignment. This approach showed how combining predictive modeling with strategic partnerships can amplify marketing results and lay the foundation for sustainable growth.

Shindu Ramandita, Senior Performance Marketing Manager - Gojek

trifa: Japan vs. Taiwan and Growth Marketing Insights

In the following session, Yusuke Oe, Head of Global Business at trifa Inc. - a company providing eSIM services in over 200 countries -shared how user‑experience‑driven strategies and localized marketing approaches helped deliver strong results. He compared growth marketing in Japan and Taiwan, highlighting both the similarities and the unique differences between the two markets. 

In Japan, success was built on several key pillars: search optimization through “Katakana branding,” an app-first strategy that improved UX and boosted LTV in a virtuous cycle, and sophisticated performance management combining cohort analysis with seasonal strategies. Particularly impressive was the use of CPA-based outdoor advertising and influencer measurement, paired with in-app surveys and coupon codes, to create an effective low-tech strategy that delivered measurable impact.

Building on this, trifa expanded to Taiwan, where a similar media ecosystem and high traveler base made it attractive. By engaging micro‑influencers and forming a local team, the company surpassed 30,000 downloads in 18 months.

Across both markets, the winning formula was clear: localized strategies plus data‑driven decision‑making, enabling sustainable app‑centered growth tailored to each market’s unique traits.

Yusuke Oe, Head of Global Business - trifa Inc

Nexon Korea & Devsisters: Game Growth Strategies for 2026


For the final panel on 2026 game marketing strategies, Nexon Korea’s Yeoncheol Kim and Devsisters’ Hongin Kim shared how they are adapting to industry shifts. Nexon emphasized incremental measurement and user value, focusing on acquiring players who stay longer and contribute more. Devsisters highlighted strengthening the Cookie Run IP fandom and embedding AI across the organization to improve analytics, with retention and re‑engagement as key goals.

The past year brought major changes: platform AI automation made creative assets the main differentiator in UA, rising acquisition costs boosted the ROI of retention marketing, and short‑form content trends reshaped the market toward casual gaming. Yeoncheol Kim noted that AI has become a basic tool for everything from creative production to media operations, while rapid game influx from global publishers has intensified competition and made marketers care about performance-driven UA.

Looking ahead, Devsisters plans to expand its IP through cultural collaborations and global growth centered on the U.S., working with local creative studios. Nexon is focusing on funnel management and keeping ad creatives fresh, operating media and creatives with segmented strategies tailored to user characteristics. Both panelists agreed that in the next 12–18 months, the defining skills for top game marketers will be data‑driven decision‑making and effective AI utilization.

App Talk by Remerge in Seoul 2026 went beyond sharing industry updates, establishing itself as a platform to shape the future of app marketing in Asia. By bringing together diverse companies and experts, it sparked new possibilities and practical growth strategies in a fast-changing environment. This collective knowledge will help shape a more resilient and dynamic future for Asia’s app industry.

Hongin Kim, UA Marketing Manager - Devsisters
Yeonchul Kim, Team Lead - Nexon Korea
Jisung Yoon, Account Manager - Remerge

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Event photos
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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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