The app marketing world is still adjusting to the evolving privacy changes on iOS, and we are now on the verge of another round of disruptions with Privacy Sandbox on Android and the new opportunities that it introduces.

It’s only natural to stack iOS and Android against each other, so we decided to examine the similarities and differences between SKAN (soon to be revamped as AdAttributionKit) and Privacy Sandbox. Don’t worry though, we’ll (try to) keep things simple. 

Before we begin

Google’s Privacy Sandbox is a broad initiative. It’s a suite of marketing infrastructure tools, covering targeting, retargeting, SDKs, and of course attribution. 

Since SKAN focuses mostly on attribution, and for the sake of comparing apples to apples (See what I just did there?), we will focus on comparing SKAN to Sandbox’s Attribution Reporting API.

Scope and approach

Doing it the “Apple way”, SKAN is often perceived as a “black box”, naturally specific to iOS only.

With Privacy Sandbox, Google has taken a more collaborative approach and is gradually introducing the different modules for both web (starting with Chrome) and Android, allowing industry leaders such as AppsFlyer to iterate, build innovative solutions using the Sandbox building blocks, provide ongoing feedback, and actively influence the core principles. This approach comes with a cost, as Google often has to reverse decisions – as it did with the recent web cookies deprecation announcement.

[H2] Elimination of device IDs

The primary principle for maintaining user privacy is to obscure persistent user identifiers such as IDFAs or GAIDs, and both SKAN and Privacy Sandbox are designed to minimize the use of identifiers. 

Notably, Apple’s ATT framework requires user consent to access IDFA, while Google hasn’t announced any plans to use an ATT-style opt-in mechanism. 

We’re being asked a lot about the future of GAID (Google Advertising ID) and whether it’s planned to be placed behind an opt-in wall like web cookies are. There’s no formal answer on this one yet. However, bear in mind that none of the Privacy Sandbox APIs use GAID, which can provide a good indication of future direction.

[H2] Data aggregation

Both SKAN and Privacy Sandbox use data aggregation, grouping user data into anonymized cohorts to prevent individual profiling. This is a big deal for advertisers, but it’s an even bigger deal for ad networks and publishers that will no longer be able to track users and will have to find new creative ways to optimize their ad performance. 

You can expect lots of  innovation in this area in the coming months and years. 

[H2] Reporting

SKAN 4 and AdAttributionKit provide three postbacks and dictate aligning to pre-defined time windows of 2, 7 and 35 days. Kind of like a full-service restaurant that serves meals on specific hours only.

In contrast, the Sandbox Attribution API is like a 30-day buffet. You cannot get wild and eat all that you want due to some “budget” limitations (more on that later), but you’re not tied to strict time windows. When the month ends, it’s time to put the forks down!

While Sandbox’s flexible reporting windows are great news for advertisers, the 30-day limitation might disappoint some marketing managers looking for longer revenue metrics. 

[H2] Random delays

With privacy-first attribution, data is never received in real-time in order to prevent the option to correlate report data with specific individuals. This forces advertisers to make longer experiments and leads to slower decision-making and reduced agility. 

SKAN postbacks arrive with significant delays: 24-48 hours delay for the first postback and 24-144 hours for the 2nd and 3rd postbacks. 

Privacy Sandbox incorporates two delay mechanisms to enhance privacy:

Event-level reports (designed for campaign optimization) are delayed by at least 1 day following an ad click and 1-30 days after an ad view—Talk about being late for the party! 

Conversely, aggregatable reports (designed for analyzing campaign performance), are available within hours — certainly  a relief for advertisers.

[H2] Privacy thresholds

There’s a joke about a fan telling a little-known artist, “I bought your album!” and the artist responds, “Oh, it was you!”

This kind of sums up the challenge with privacy-focused ad measurement. When the numbers are small, pinpointing individuals becomes too easy, which isn’t great for privacy.

Both SKAN and Privacy Sandbox address that issue using privacy thresholds, but the implementation is different. 

In low volumes, SKAN simply doesn’t give you information on post-install activity (as well as campaign information). That’s the main reason why we at AppsFlyer developed a null modeling solution for SKAN to compensate for that signal loss. Privacy Sandbox uses the notion of “noise”. It adds fake data to mask the real numbers. The noise can be significantly deceiving when data volumes are low, but as volume grows the relative noise size reduces and becomes negligible.

Tip: For both SKAN and Sandbox, low data volumes can pose challenges for advertisers. It’s best to allocate adequate budgets to each media source and avoid splitting campaigns excessively. This strategy ensures data volumes are sufficient to bypass privacy thresholds, providing more reliable reports. The more focused your budget, the more reliable your data.

[H2] Bit-based attribution

Both SKAN and Sandbox use “bits” to represent conversion or campaign data. Values represented by bits are more challenging to use but they ensure data is controlled, standardized, and more importantly: no direct identifiable information (like names, IDs, or even specific actions) is transmitted. 

SKAN uses postbacks to share conversion data with advertisers and ad networks. Privacy Sandbox provides APIs available for ad tech companies to access the attribution reports.

To help you get the most out of those bits, MMPs like AppsFlyer offer standardized methods for converting and interpreting bits into human-friendly metrics and actionable insights.

[H2] Data granularity

SKAN allocates 2-4 digits for source identifiers (depending on the number of installs) and permits only six bits for post-install conversion data (equivalent to 64 possible conversion values). This significantly limits the granularity with which marketers can analyze campaign performance and post-install activities.

In contrast, Privacy Sandbox offers 128 bits for the campaign sources and post-install events. I’ll save you the calculations and the weird numbers but trust me, you’ll get over a billion times more data with Sandbox than you do with SKAN.

Keep in mind, though, that in the Privacy Sandbox each source event, like an ad click, has a maximum number of ‘values’ it can use, known as the contribution budget. Sandbox limits you to certain contribution budgets and adds random noise. This means that even though the data can be pretty granular, you’ll need to plan your budgets carefully and prioritize upfront.

[H2] Technical complexity

SKAN is like a black box managed by Apple, delivering just the essentials like winning campaigns and conversion counts.

In contrast, Privacy Sandbox is designed as a set of building blocks—enabling industry players like AppsFlyer to build their own solutions on top of it. These solutions must comply with strict cloud infrastructure guidelines to maintain privacy. 

In addition, Sandbox offers a cross-network last-click attribution through the presence of a neutral player such as an MMP. That means AppsFlyer and other MMPs will have a significant role in the Sandbox attribution data flow providing insights on cross-network last-click attribution. 

This key difference makes the Sandbox a more complex technology but also opens up opportunities for innovation and disruption in the industry.

[H2] Presenting the full picture

Both SKAN and Privacy Sandbox are tailored to their respective operating systems (iOS and Android), making it hard for advertisers to see the full picture.

Add the fact that some ad networks haven’t fully adopted SKAN yet (which also doesn’t support Apple Search Ads), along with the fact that Privacy Sandbox is just getting started, and you’re left with a reality split across several ‘buckets of truth’—far from ideal for analyzing global campaign performance and make informed decisions.

In such a fragmented landscape, having a single source of truth is crucial. AppsFlyer continues to bridge this gap with its pioneering Single Source of Truth (SSOT), which consolidates all campaign data across media sources and platforms into one centralized view.

[H2] Summary

In a privacy-first era, advertisers need to adapt to a new reality in which personal information is never revealed, measurement is aggregated and anonymous, the reporting intervals are long, and data is delayed and obscured. 

While SKAN keeps attribution simple but very limited, Privacy Sandbox offers a much wider range of capabilities and data granularity, but requires a much heavier technological effort and the creation of complementary solutions. 

AppsFlyer is already developing its next-generation products that will support Privacy Sandbox, enabling advertisers to seamlessly adopt these new technologies, protect user privacy, and enhance their marketing strategies.

The post SKAN vs. Sandbox: what advertisers need to know appeared first on AppsFlyer.

Great marketing starts with great measurement, but on iOS, accurate attribution is easier said than done.

As the gap between user actions and what can actually be measured continues to widen, marketers face a growing challenge: how to make informed, data-driven decisions in a privacy-centric world.

To address this, AppsFlyer and Google have joined forces to enhance the iOS measurement framework by introducing a joint attribution solution that enables advertisers to fully capitalize on iOS.

AppsFlyer now supports richer iOS attribution with Google

AppsFlyer now leverages Google’s Integrated Conversion Measurement (ICM), bringing meaningful innovation to iOS measurement. 

This solution is now available to all advertisers in beta. Any advertiser is able to implement it and those who do can expect an increase in iOS conversions attributed to Google Ads — particularly from users who were previously unattributable

This means greater visibility, more accurate attribution, and smarter optimization decisions backed by more complete data.

And it’s not just iOS. Even on Android, privacy shifts and regulatory changes are starting to impact app measurement, especially when it relies on user-specific identifiers. This is paving the way for privacy-preserving solutions like aggregated or modeled attribution, which are becoming increasingly important across the ecosystem to streamline compliance and maintain measurement accuracy.

“The real-world impact advertisers are seeing after adopting our Integrated Conversion Measurement solution is remarkable. By delivering more complete and accurate attribution, we’re empowering businesses with the insights needed for smarter decisions and meaningful growth. We’re truly excited to see more examples of how Google and AppsFlyer’s measurement innovations will help even more advertisers thrive.”

Lee Jones, Managing Director, Global App Ads, Google

This is where the collaboration truly shines: By combining AppsFlyer’s attribution solution together with validation, you gain signal enrichment and a robust reporting infrastructure — together transforming the integration into a fully actionable, privacy-compliant measurement solution.

Key pillars of the joint solution

• Coverage: Recover critical signals to ensure all user actions across the funnel are measured and accounted for.
• Privacy-centric: Deliver insights through privacy-preserving measurement that maintains trust.
• Validation: Use AppsFlyer’s visibility into the mobile ecosystem to verify and continuously improve modeling accuracy.
• Signals: Capture conversions and deliver optimization signals, even on devices without user identifiers, so your campaigns perform seamlessly. That way, your brand doesn’t miss out on valuable iOS users.

With Google joining AppsFlyer’s Single Source of Truth (SSOT) solution — established as the industry standard — advertisers gain access to deduplicated, real-time, and granular attribution. This powerful integration unifies SKAN and AppsFlyer attribution into a cohesive measurement framework, unlocking deeper, richer insights that empower smarter, faster decision-making.

Better accuracy through validation

Validation is a cornerstone of our unbiased approach — and a unique added value of how AppsFlyer customers can leverage Google’s ICM, ensuring data integrity and trust at every step.

Together with Google, we’ve developed a dedicated mechanism to measure and attribute Google Ads using the same industry standards applied to all networks. This process verifies attribution claims before they’re included in your reports — filtering out noise, enhancing accuracy, and boosting confidence in campaign performance.

AppsFlyer applies proprietary algorithms to assess every signal and attribute only trustworthy conversions. This added layer of scrutiny not only improves reporting precision but also ensures marketers can make better decisions, all while respecting user privacy.

Google’s data now appears in your AppsFlyer reporting dashboard under Google Ads. These conversions are measured using AppsFlyer’s probabilistic attribution mechanism, enabling cross-network measurement and a more complete view of performance that helps you make faster decisions and maximize return on ad spend (ROAS).

Making an impact with probabilistic modeling

Brands that have traditionally relied solely on deterministic attribution are missing out. In today’s privacy-centric world, that approach alone no longer delivers the full picture.

Our data clearly shows that probabilistic modeling is effectively bridging attribution gaps, enabling advertisers to unlock significant value and measurable outcomes from day one, with some iOS apps seeing a 100–150% increase in installs attributed to Google. With the support of additional data signals, marketers across industries and markets are regaining unprecedented visibility into their full-funnel performance — insights that directly impact the bottom line, including a significant increase in iOS installs and drops in CPI.

This renewed visibility empowers brands to create personalized experiences, reach the most relevant audiences, and optimize campaign performance with precision and confidence. To give you a glimpse of what’s possible, here’s how Zutobi, a driver’s education app, successfully leveraged AppsFlyer and Google’s joint measurement solution.

iOS users are a critical growth channel for Zutobi, however, challenges with accurate campaign measurement limited their ability to scale iOS user acquisition. To address this challenge, Zutobi prioritized the steps to implement ICM including activating Google’s on-device conversion measurement using event data solution and integrating their app with the latest version of the AppsFlyer and Google Analytics for Firebase SDK. After implementing ICM, Zutobi saw transformative results in its AppsFlyer reporting, including a 138X increase in install volume, 50X increase in subscriptions, and a 97% reduction in cost per install. This enhanced measurement accuracy gave them the confidence they needed to effectively scale their iOS strategy. 

“Integrated Conversion Measurement led to major improvements because it enabled us to more accurately understand how Google iOS campaigns perform in our AppsFlyer reporting compared to other channels. It also allowed us to scale this channel with greater confidence.”

Dmytro Grebennikov, Chief Marketing Officer, Zutobi

Don’t miss out on attribution

If you’re looking to regain the clarity of the pre-ATT era and harness every available signal to rebuild a comprehensive data picture for your brand, the AppsFlyer and Google integration makes it possible.

Implementing the integration does require development resources — updating both the AppsFlyer and Google Analytics for Firebase SDKs — but the payoff is immediate: a unified, privacy-centric iOS attribution solution for your Google App campaigns, delivering clarity and impact from day one.

• To get started, follow the steps in this Help Center article, which includes a detailed walkthrough video and a complete step-by-step implementation guide.
• Bonus: Catch up on our most recent webinar recording, where we’ll walk you through the key components and best practices to help you successfully set up the integration.

Final words

iOS marketing is entering a new era, one where privacy and performance no longer have to be at odds. With innovative measurement solutions from AppsFlyer and Google, you can reclaim lost signals, make smarter decisions, and confidently invest in iOS. 

The tools are here. The signals are back. It’s time to close the gap for good and benefit from one actionable data reality.

The post Growth unlocked: AppsFlyer and Google uncover the true impact of Google Ads appeared first on AppsFlyer.

TL;DR

• Automate without coding: Build powerful marketing workflows using AI agents and no-code tools, zero engineering support needed.
• Get real-time insights: Connect directly to AppsFlyer data through MCP for instant campaign performance visibility.
• Reclaim your time: Eliminate hours of manual reporting with automated dashboards delivered to your inbox
• Prevent budget disasters: Set up 24/7 monitoring that alerts you before overspend happens, not after.
• Two ready-to-use workflows: Start today with plug-and-play templates for performance reporting and budget monitoring

Stop waiting on developers: Build AI-powered workflows in minutes 

What if your reports could simply write themselves every morning without manual data pulls, dashboards, or delays?

What if your campaign budget caps were monitored 24/7 by a digital assistant who alerted you before overspend, not after?

For marketers constantly balancing dozens of tasks, automation isn’t just a nice-to-have, it’s the only way to keep up. Yet too often, it’s locked behind dev queues, complex tooling, or unclear roadmaps. That’s changing.

Thanks to the rise of generative AI agents, AppsFlyer’s Model Context Protocol (MCP), and no-code platforms like n8n.io, you can now automate critical workflows using real-time AppsFlyer data without writing a single line of code. Think of it as building your own marketing ops team, powered by AI, with zero engineering overhead.

This blog shows you exactly how to get started. Below, you’ll find two plug-and-play workflows that marketers are already using to save time, prevent budget waste, and stay one step ahead, all powered by AI agents and real-time data from AppsFlyer.

Your data at your fingertips: The MCP advantage

Model Context Protocol (MCP) is what makes automated workflows possible without technical complexity. Instead of waiting for developers to build custom API integrations or data engineers to write extraction scripts, MCP gives AI agents direct, secure access to your AppsFlyer data through simple, natural language requests.

Getting started is simple and takes only a few seconds to minutes: just input AppsFlyer’s MCP URL and supply your token. When your AI agent needs to check campaign performance or monitor spend thresholds, it simply asks through MCP and gets instant answers. 

For marketers, that means complete autonomy to build and iterate on workflows without waiting on technical teams.

No-code meets AI: Building workflows with n8n.io + AI agents

If you haven’t used n8n.io before, it’s a powerful, drag-and-drop automation platform that makes workflow creation intuitive and code-free. It integrates seamlessly with AI-based agents, making it easy to build intelligent automations using natural language and real-time data.

These examples use n8n.io, but the same principles apply to Make.com, OpenAI AgentKit, Google Opal, Zapier, or whichever automation platform fits your workflow.

Why this matters for marketers

• Faster iteration: Test and launch workflows at your own pace
• Full control: Own your data flows, alerts, and reports from start to finish

Two ready-to-use workflows you can launch today

In the past year, we talked to marketers across the industry to understand their biggest pain points. It is what led us to build  these plug-and-play workflows to solve the real problems app marketers face daily.

Workflow 1: Periodic performance dashboard

The challenge

Marketing teams spend hours every week manually pulling data from AppsFlyer, copying it into spreadsheets, creating charts, and distributing reports to stakeholders. By the time the report lands in inboxes, the data is often outdated. This manual ritual steals time from strategic work and creates a lag between insight and action.

The solution 

This workflow eliminates the entire manual reporting process. An AI agent connects to AppsFlyer via MCP, pulls your specified performance metrics (installs, revenue, ROAS, retention rates, etc.), and automatically generates a formatted visual report. The n8n workflow then delivers it to your inbox (or your team’s) on a pre-determined schedule you choose: daily at 9am, weekly on Monday mornings, or after major campaign launches.

The AI agent doesn’t just dump data, it contextualizes it, highlighting trends, flagging anomalies, and even comparing performance week-over-week or against your benchmarks. The end result: you get  insights alongside the numbers.

Why this matters for marketers

• Saving hours per week on manual reporting
• Getting fresher insights: reports generated with the latest data, not last week’s snapshot
• Focus on strategy, not spreadsheets: spend your time optimizing campaigns, not copying/pasting data
• Never miss a beat:consistent reporting cadence

Ready to set it up? Head to the GitHub repository for complete setup instructions and the workflow template.

Workflow 2: Cost threshold alerts

The challenge

Budget overruns happen silently. You set campaign budgets across multiple media sources, but by the time you check the dashboard, you’ve already blown past your cap. 

Manual budget monitoring means checking AppsFlyer multiple times a day, and even then, you might be too late to prevent waste. For marketers juggling dozens of campaigns across platforms, this reactive approach burns budget and erodes performance.

The solution

This workflow acts as your 24/7 budget watchdog. The AI agent monitors total campaign spend by media source through AppsFlyer’s MCP connection. You set custom cost thresholds for each media source (e.g., “Alert me when Facebook spend hits $5,000” or “Notify me if Google Ads exceeds $10,000”). 

The moment a threshold is crossed, n8n instantly fires an alert to your Slack channel, email, or both, showing you which media source triggered the alert and the current total spend.

Why this matters for marketers

• Agents that are using accurate cost data for accurate results
• Preventing budget overruns before they happen, not after
• Saving thousands in wasted spend by catching issues in real-time
• Offering ease of mind: no need to obsessively check dashboards throughout the day
• Respond instantly: get alerted wherever you work (Slack, email, SMS)
• Media source visibility: measure spend across all your traffic sources in one place

Ready to set it up? Head to the GitHub repository for complete setup instructions and the workflow template. 

Key takeaways

Marketing autonomy accelerates everything: Build workflows in minutes instead of weeks. When you control your own data flows, optimization moves at the speed of decision-making, not engineering queues.

Lead the AI shift in your organization: Pre-made templates get you started in under 30 minutes. Prove the value quickly and become the catalyst for transforming how your team works.

Shift from reactive to strategic: AI-powered automation catches issues before they escalate, freeing you from firefighting to focus on high-impact work.

With generative AI agents, AppsFlyer MCP, and no-code tools like n8n, the modern marketing stack is smarter, faster, and built for autonomy.

Ready to start?

• Go to the Github repository and start with the 2 ready-to-use workflows
• Learn how to customize AI agents with AppsFlyer MCP- Stay tuned for our upcoming webinar series launching in January.

We’re committed to supporting your AI automation journey. To learn more on this opportunity you can read more here and start building your automation strategy, and improving your workflows.

The post NEW! Build AI marketing agents in 30 minutes without writing code appeared first on AppsFlyer.

Every major shift in marketing begins with a revolution.

The first was the mobile revolution. Fourteen years ago, apps changed how people discover, download, and engage with products. Marketers suddenly needed a way to understand what was working, and that’s where we started: building a trusted measurement platform that connected the dots across a fast-growing mobile ecosystem.

Then came the privacy revolution. Overnight, the rules of data collection and sharing were rewritten. Marketers had to adapt, and we evolved with them by reimagining measurement through privacy-enhancing technologies, secure data collaboration, and a single source of truth to connect a fragmented reality. It was a turning point that proved growth and privacy can coexist.

Now we’re in the midst of the AI revolution. It’s faster and more transformative than anything before it. But even the most advanced AI needs a solid foundation: trusted data, privacy-safe collaboration, connected customer experiences, and accurate measurement.

That brings us to today. At our Fall Release event in London, we introduced the next evolution of our journey, the Modern Marketing Cloud, with ten new products across four core suites designed to redefine marketing in the AI era.

What is the Modern Marketing Cloud

The Modern Marketing Cloud is the next evolution of marketing infrastructure: a unified, privacy-first platform built for the AI era. It’s where data, intelligence, and creativity come together to power smarter and more meaningful decisions.

It brings together four integrated suites that work as one:

• Measurement Suite: Delivering a true omnichannel view of performance across every channel and device by combining bottom-up attribution with top-down measurement.
  
• Deep Linking Suite: Creating personalized, contextual experiences that connect users to the right content at the right moment – across devices and journeys.
  
• Data Collaboration Suite: Allowing brands and their partners to collaborate securely and unlock infinite opportunities based on value from their shared data.
  
• Agentic AI Suite: Enabling autonomous marketing through goal-based optimization, intelligent agents, automated workflows, and actionable insights.

Traditional marketing clouds were built around closed ecosystems and media management. They focused on managing spend and audiences inside walled gardens rather than connecting the broader ecosystem. That model made sense when marketing was simpler, but today’s world is far more complex, and with far more opportunities.

The Modern Marketing Cloud takes a different approach: it’s open, neutral, privacy-first, and leverages the power of AI. It is designed for the modern marketer who needs accuracy, insight, and control across every touchpoint.

Measurement You Can Trust

Measurement has always been at the heart of what we do. It’s where our story began and where we continue to lead.

In today’s complex world, consumers move seamlessly between devices and channels, generating more data but also more complexity. The irony is that the more connected the world becomes, the harder it is to connect the dots. Beyond channels and platforms, there’s also a surge in signals, from incrementality and creative performance to fraud and behavioral data. In short, measurement is getting harder.

So how can marketers trust what they measure? 

Today we’re introducing three new products that will take your confidence in measurement to a whole new level:

Cross-Platform Journeys & LTV

For years, marketers have chased the idea of true omnichannel measurement: one view that connects every touchpoint. The explosion of CTV, PC and console, and web engagement is finally making that vision real. Advances in privacy-preserving attribution, identity stitching, and clean-room collaboration now make it possible to connect fragmented journeys into one coherent view.

The new Cross-Platform Journeys and Lifetime Value (LTV) capability brings data together across mobile, web, CTV, PC, and beyond to tell one unified user story. By focusing on the user rather than the device or install, it analyzes every interaction across platforms, measures events and revenue, and attributes it back to the campaign that started the journey.

The result is a clear, connected picture of performance. Marketers can uncover hidden revenue once labeled “organic,” increase attributed LTV (early results show a 27%-65% lift), boost ROAS (up to 50%), and make smarter, more confident decisions across every channel.

Incrementality for User Acquisition

The new Incrementality for UA (on top of the successful Incrementality for Remarketing product)  answers the question every marketer asks: Would these users have converted without marketing? It isolates the true impact of your campaigns by filtering out external noise like seasonality or PR.

Incrementality complements attribution. Attribution shows what happened; incrementality reveals why. Together, they give marketers the full picture.

Built with 50 design partners and dozens of networks, Incrementality for UA turns complex modeling into a few clicks. During the beta we’ve seen impressive results: 18% of campaigns showed no incremental impact at all, while others drove up to 10x more conversions than attribution alone. Fubo, the sports-first streaming platform, saw a 35% lift in one campaign and three times higher performance in another.

Enhanced Attribution Model

Click flooding has long distorted results across the industry. Our new model uses real-time AI and per-device logic to ensure every attribution decision reflects true user intent. Customers adopting it have seen up to 100% uplift in key KPIs such as first-time deposits. For years we’ve known that cleaner data drives better performance; now there’s a model that incorporates it per device, per use case.

Autonomous Marketing: From Manual to Intelligent

In almost every conversation with marketing leaders about AI, one theme stands out: curiosity mixed with caution. AI isn’t here to replace human intuition; it’s here to expand it. The real opportunity lies in balancing creativity, context, and strategy with the power of intelligent machines.

For the first time since the mobile revolution, the bottleneck in marketing has shifted. Building is no longer the challenge; end-user attention is. To earn it, marketers must use AI as a natural extension of their creativity.

That’s where Agentic AI comes in. It turns data into insight and insight into action. And because your AI is only as good as your data, we’ve made sure your AppsFlyer data is AI-ready: accurate, connected, and privacy-safe.

AI Assistant & My Dashboards

The new AI Assistant lives inside My Dashboards, turning questions like “Which campaigns delivered the best ROAS this week?” into instant answers. It’s your daily copilot that surfaces insights, summarizes results, and suggests what to look at next all in natural language.

Within My Dashboards, AI, data, and insights come together in one unified view. The assistant adapts in real time, highlighting trends and anomalies so teams can act faster. Dashboards can be customized and shared across teams, turning data into collaboration and insight into action.

It’s no surprise that 70% of our customers have already adopted this new experience, and we’ve seen how it’s boosted their efficiency, especially with the help of the AI Assistant.

MCP: Model Context Protocol

Our Model Context Protocol (MCP) securely connects your data to language models like ChatGPT, Claude and Gemini. It translates questions into structured queries and delivers insights with full context. Our MCP now also powers our new AI-to-AI collaborations with partners such as Amplitude and Braze, combining attribution, analytics, and engagement into one intelligent workflow.

Creative Management

Ad impressions rose nearly 40% in 2025, and many marketers now juggle up to 10,000 creative variations. The problem? Creativity has scaled faster than the systems meant to manage it. In the last couple of years, we tackled this challenge with the industry’s first automated Creative Optimization solution, now optimizing $5B in spend.

But, there was still a missing piece in this autonomous creative puzzle. Now we’ve filled the gap: Creative Management automatically syncs and organizes assets, scoring creatives with AI pre-flight analysis, and distributing top performers across networks in one click. What used to take hours now happens in minutes.

Agent Hub

Part of the new AppsFlyer Agentic AI Suite, the Agent Hub turns autonomous marketing execution into reality. It’s home to AppsFlyer’s AI agents, intelligent assistants built on privacy-safe, AI-ready data that automate key workflows like reporting, anomaly detection, and optimization within a secure environment.

Launched with six ready-to-use agents, the Agent Hub helps marketers move from insight to action automatically – surfacing opportunities, catching issues early, and freeing teams to focus on strategy and creativity. As the execution layer of the Modern Marketing Cloud, it connects with MCP (Model Context Protocol), enabling brands to build or integrate their own agents. Together, they power a new era of trusted, autonomous marketing built for speed, clarity, and growth.

Data Collaboration: From Isolation to Signal Richness

Data Collaboration is the third foundation of the Modern Marketing Cloud. For years, marketers have relied on fragmented, siloed data and long legal processes to access insights. Valuable information was there all along, just out of reach.

But, in the AI era, untapped data potential is no longer acceptable.

Introducing Signal Hub

That’s changing with Signal Hub, a curated, privacy-safe data marketplace within the AppsFlyer Data Collaboration Suite. With Mastercard as our first global partner, and more to come, brands can now enrich, analyze, and activate data responsibly in governed clean rooms.

Signal Hub simplifies collaboration. Data owners list once, and brands can subscribe and activate securely. Signals are aggregated, verified for freshness and compliance, and matched to your data without any personal information changing hands. It’s not a warehouse of raw data but a curated library of quality signals that marketers can trust.

The next chapter of growth won’t come from bigger budgets or new identifiers but from better signals that capture intent, reflect real behavior, and power smarter targeting, sharper measurement, and AI that truly understands people.

The Final Word

We’re living in extraordinary times. Every generation believes its era is revolutionary, but this one truly is. The AI revolution is reshaping how we build, connect, and grow, and it’s moving faster than anyone imagined. None of us can predict where it will lead, but one thing is clear: we’re only at the beginning.

Progress happens when we balance what’s new with what’s proven, embracing innovation while staying grounded in what’s known: data, measurement, privacy, collaboration, AI, innovation, and community. These are our constants and our compass.

As we enter this new chapter, we do it together—as builders, innovators, and partners shaping the next decade of marketing. The Modern Marketing Cloud is only the beginning. We’re still just 1% done.

Want to dive deep into our new products? Dive in! 

The post AppsFlyer’s Next Chapter for the AI Era: From Mobile Measurement to the Modern Marketing Cloud appeared first on AppsFlyer.

As ChatGPT, Gemini, Claude, and other Large Language Models (LLMs) increasingly become the go-to source for answers, marketers are entering a new organic (and potentially non-organic) frontier, one where traffic originates from AI responses, not search engine result pages.

LLMs are not only transforming how users discover apps and content but also impacting consumer behavior. Recent research shows that users who engage via LLMs have higher intent and monetize better than search users. The conversational format feels like a soft recommendation rather than a sales pitch, driving stronger user intent.

However, it also surfaces new challenges: How do you influence, measure, and optimize discovery in these environments?

In this blog, we unpack how GenAI and LLMs like ChatGPT, Gemini, and Claude are reshaping discovery — and what it means for marketers. You’ll see which industries are leading the shift, why attribution is breaking, and how to turn AI-driven traffic into measurable growth.

AI-First industries: Who’s leading the shift?

LLMs are already delivering meaningful traffic to brands, but often without marketers realizing it because the traffic goes unattributed.

Based on these market trends, some industries are seeing a deeper impact:

• Legal & Financial Services: Users ask complex, trust-intensive questions that LLMs are best equipped to answer.
• Online shopping: LLMs drive traffic to retail and e-Commerce sites based on product page information. In fact, OpenAI has just announced its on-site checkout process which may reduce their website traffic and allow in-chat experience for completing the transaction.
• Healthcare & Insurance: AI chat becomes the first line of inquiry for symptoms, treatments, and coverage.
• SMB & SaaS: App discovery, product comparisons, and usage walkthroughs often show up in AI answers.
• Consumer Tech: Users rely on conversational AI for product reviews and recommendations.

In many cases, AI is now a more common entry point than traditional search, with some brands seeing 5–10% of top-of-funnel traffic driven by LLMs, even when not labeled as such in analytics.

The challenges of optimizing for LLMs

Unlike traditional SEO, LLM visibility is harder to crack and even harder to measure.

There are three core challenges:

1. No visibility into rankings: You can’t “rank check” a ChatGPT response. There’s no way to know how often you’re cited.
2. Inconsistent linking: Some models link, some don’t. Others paraphrase your content without attribution.
3. Attribution is broken: Many AI clicks show up as organic traffic, obscuring the true source in analytics tools.

Together, these gaps make AI optimization feel like you’re flying blind.

How to measure the impact of AI 

To overcome these challenges, brands must adapt their content and measurement strategies:

• Write for AI: Prioritize concise, clear answers. Use questions, summaries, and bullets. Repeat keywords multiple times. Treat your content like it might be quoted out of context. Things that work well include pricing tables, integration breakdowns, trial proposals, product walk-throughs, comparison pages, and so on.
• Track proactively with UTMs: Use UTM parameters on URLs likely to be picked up by LLMs, such as forums, docs, partner content, and public knowledge bases.
• Bridge the visibility gap with web-to-app attribution flow: Turn invisible clicks into measurable insights. If you can attribute the users in your app or website to an LLM engagement, you will be able to understand and optimize the user journey based on lower funnel actions.
• Use deep links wherever possible in your owned and earned media: LLMs collect data from social networks and across the web. When you place links in these placements, make sure they’re deep linking into the app so that, if a user has the app, they’ll have a contextualized and seamless experience — resulting in higher engagement and conversions. Examples include your website, social group links, YouTube videos, influencer campaigns, referral programs, affiliate links, bio pages, and more.
  

Note: deep linking can be complex, so make sure you use the right tool for it (more on this in the next section).

• Use website schema markup: Structured data helps LLMs understand and cite your content correctly. For example, adding FAQPage or Product schema in JSON-LD format can boost discoverability in AI-generated answers.

This markup should be placed in the HTML of your site — typically in the <head> or at the bottom of the <body>.

Example:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is the best budgeting app for freelancers?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "BudgetPro helps freelancers manage cash flow, taxes, and savings through smart automation."
    }
  }]
}
</script>

AppFlyer’s OneLink: What is it and how can it help?

OneLink is AppsFlyer’s deep linking and redirection solution. It solves a complex problem, the result of multiple combinations of where a link can be placed on the Internet and what happens when you click on it, which depends on factors such as the platform, OS version, browser, app, and others. 

For example, some browsers open apps directly while others require a fallback. For your app, you might want existing users to open the app directly and new users to go to the App Store. You need to differentiate between these two groups.

OneLink abstracts all this logic and ensures that once a link is clicked, it always works. And on top of that, it measures the parameters you set up on the link upon creation. That is the reason it is a great option to solve the LLM challenges:

• Deep link as many users as possible to the app.
• Measuring the results of web-to-app user journeys.

Deep link users into the app using OneLink EVERYWHERE

You want LLM to use your deep links. The main reason is that you get high-quality traffic for free without any extra hop in the user journey. To do so, you should spread your link publicly in any Owned or Earned Media channel you have. Some examples can be your website, social groups links, influencer campaigns, referral programs, and links you share with affiliates, bio pages, and the like.

When you use OneLink in these placements, you’re buying yourself insurance that no matter who the user who clicks on the link is, and where it is placed, the user gets the right behavior. Be it opening the app (optimal), or installing the app, and after navigating to an in-app content based on the deferred deep link value. That is another parameter that the link carries and is being passed back to the app after installation, so that the app knows to tailor the first-time user experience.

Ultimately LLMs prefer OneLink because of its robustness rather than a simple iOS Universal Link or a URI scheme. With OneLink, it understands that it can handle multiple experiences.

Web-to-app with OneLink: Turning AI mentions into measurable conversions

AppsFlyer’s OneLink also solves the attribution problem for AI-generated traffic in web-to-app flows, which are very common in the app industry.

Here’s how it works:

1. User asks an AI assistant for a recommendation. For example, “What is the best personal finance app for freelancers?” The LLM replies with your link.
   

Incoming URL: https://your.website.com?utm_source=chatgpt.com

2. User clicks on the link. OneLink uses a Smart Script or Smart Banner on your site to translate the incoming URL params into an attribution link and places  it behind a OneLink that is agnostic to the platform, browser and OS.

Generated OneLink: https://yourapp.onelink.me?pid=chatgpt

3. Once the OneLink is clicked, it:
   
   • Sends existing users directly into the app to a specific in-app content based on their LLM searches.
     
   • Routes new users to the correct app store by identifying their device, OS, browser, and platform.
     
   • Measures the source that led the user to install or open the app, in this case, the LLM tool.

This data is available in AppsFlyer’s raw data reports for analysis and optimization purposes. Now you can look at your funnel, compare the performance of the LLM with other LLMs or with other owned and earned channels, and invest the resources where it makes sense.

The bottom line

LLMs are fast becoming the new gateway to content, apps, and products. However, they also bring uncertainty in visibility, traffic source, and user intent.

To succeed, marketers must:

• Treat LLMs like a new organic channel
• Use deep links and a deep linking solution to measure their effectiveness
• Use structured content and schema markups
• Tag AI-discoverable links with UTMs

GenAI is wide open. With the right setup, you can stop guessing, start measuring, and optimize a critical part of your business.

The post Measuring the unmeasurable: Attribution in the age of GenAI appeared first on AppsFlyer.

TL;DR

• During Advertising Week New York, AppsFlyer joined leaders from Roku, Tinuiti, GoPuff, and US Soccer to discuss how marketers can measure performance across CTV, mobile, and AI-driven channels.
• President and GM Brian Quinn explored the opportunities of agentic AI and how ChatGPT’s SDK integration could become a new platform for brand engagement.
• During a Flyerside Chat, Roku and Tinuiti emphasized incrementality and data activation as critical for proving the value of connected TV campaigns.
• Flyerside Chat panelists agreed that CTV has turned a corner, becoming easier and more cost-effective to run — creating a window of opportunity for advertisers before prices rise.
• Marketing leaders agreed that unified measurement, not new technology, is the key to aligning brand and performance goals.

At Advertising Week NYC, one theme consistently emerged: marketers want clarity and proof of performance. Whether it’s connected TV, mobile, or emerging AI-driven experiences, advertisers are focused on understanding how each channel contributes to growth and measurable ROI.

AI’s Emerging Role in Marketing

AppsFlyer President and GM of North America Brian Quinn spoke about the rise of agentic AI and its potential to become a new marketing platform. With ChatGPT’s SDK integration, he described how AI could evolve into a space where brands build and distribute experiences directly within conversational ecosystems.

That shift presents both opportunity and challenge for marketers. As Brian noted, many brands are still working to understand how to measure the impact of brand investment and justify spend across channels. As AI-driven discovery blends with traditional media, marketers need unified frameworks that connect visibility, engagement, and outcome.

Check out the (behind a paywall) presentation from Brian here.

CTV and the Push for Incrementality

During the AppsFlyer Flyerside Chat with Roku and Tinuiti, measurement was front and center. 

Dan Lapinski of Roku made the point that “last click isn’t a bad thing, it’s just not something CTV can measure on its own.” Instead, success depends on understanding the incremental impact of campaigns. Harry Brown of Tinuiti added that incrementality has become essential for proving what actually drives results.

The conversation highlighted how brand and performance teams are beginning to work from the same playbook. Roku pointed to continued consolidation of media and measurement and predicted more integrated shopping experiences within CTV. Tinuiti’s Brown emphasized the next phase: turning measurement insights into data activation and audience building.

“There’s no better way to understand how someone will spend than by looking at what they’ve already been spending on,” Brown said.

The panel also noted that CTV has made major strides in the past year. Creative production, once expensive and a blocker for many advertisers, is now far more accessible. Running CTV campaigns has become easier, with improved tools and more flexible buying options. Panelists agreed that the channel has reached an inflection point — one that’s likely to lead to explosive growth. Prices remain relatively low for now, offering a window for advertisers to get in while the opportunity is still strong.

Watch more perspectives from advertisers on the future of CTV from the floors of Advertising Week.

Measurement as a Growth Strategy

In a panel with GoPuff, US Soccer, and M&C Saatchi Performance, leaders discussed how clear, consistent measurement aligns marketing and business objectives.

Tyler Stewart, Head of Brand Marketing & Creative Partnerships at Gopuff spoke about turning brand attention into measurable behavior, connecting creative impact to actions that drive growth. This reframes brand marketing as an engine for measurable outcomes, not just awareness. It reinforces that creative storytelling and performance data must work hand in hand.

Ange Morris, VP, Audience Growth, Experience, & Values at the U.S. Soccer Federation highlighted the importance of working with partners who bring consistency and transparency to performance reporting. For large organizations managing multiple campaigns and stakeholders, this level of alignment ensures that marketing activity supports business goals and can be evaluated with confidence across every channel.

As Brian Quinn noted during the panel, “The challenge isn’t the technology. It’s getting organizations to align on what measurement means and how to use it to drive decisions.”

The Path Forward Requires Outcomes

Seeing the advertising industry come together for these tentpole moments always gives a great temperature check on where we are, and where we’re going. The industry is certainly in 

Key Takeaways

• Cross-channel measurement is non-negotiable. Marketers need a unified approach that connects insights across CTV, mobile, and AI environments.
• Incrementality proves real impact. As CTV grows, success depends on understanding lift, not last-click attribution.
• Data activation is the next frontier. With reliable measurement frameworks in place, marketers can focus on audience building and predictive insights.
• Alignment beats automation. Technology supports growth, but real progress comes when teams and partners agree on what success looks like.

The post Advertising Week NYC 2025: The New Age of Measurable Media appeared first on AppsFlyer.

TL;DR

• Discord launched Quests as its first-ever in-app advertising format, powered exclusively by AppsFlyer measurement and attribution.
• Quests’ reward-based format is inherently community-driven, fitting naturally into Discord’s gamified environment where users are primed for shared, reward-first interactions.
• Second Dinner leveraged Quests for Marvel Snap, generating 15M+ impressions, $7 CPI, and a 99% reward redemption rate — achieving a 30% lift in outcomes between campaigns thanks to AppsFlyer insights.
• Together, AppsFlyer and Discord are setting a new standard for in-app advertising: value-driven, community-first, and backed by transparent, actionable measurement.
  

When Discord began exploring its ambitions in in-app advertising (IAA), it was clear that any solution had to feel different from traditional ads. Discord is already home to the world’s most engaged gaming communities, where millions of people come together daily to play, share, and connect. For advertising to work there, it had to respect the community, feel native to the experience, and create genuine value for players. That’s why Discord chose AppsFlyer as its first MMP partner to power the launch of Video Quests on mobile, the platform’s first-ever in-app advertising format.

Discord’s advertising platform features its signature premium rewarded Quests format. Video Quests on Mobile unlock cross platform capability on Discord, and ensure advertisers can engage their audiences wherever they play. By integrating with AppsFlyer’s server-to-server measurement and attribution, Discord is enabling developers to go beyond surface-level engagement, unlocking granular performance insights such as Cost Per Install (CPI) and Return on Ad Spend (ROAS). For mobile marketers, this is a breakthrough: authentic, community-driven discovery combined with the measurement rigor required to scale. 

Second dinner doubles down with Marvel Snap

For Second Dinner, the studio behind Marvel Snap, Discord’s Quests offered a way to extend momentum. Since its launch in 2022, the game has attracted over 22 million downloads and generated more than $200 million in revenue. With new characters and features rolling out in 2024, the studio saw Discord as the ideal environment to engage both new and returning players.

Second Dinner launched two Mobile Video Quests on Discord, featuring fan-favorite heroes like Thor, Venom, and Spider-Man in epic in-game battles. The creative was designed with players in mind, rather than just another ad. The results were striking: more than 15 million impressions, and a nearly unheard-of 99% reward redemption rate. Between the first and second Quest, Discord leveraged AppsFlyer’s mobile measurement integration to refine targeting and optimize performance, creating a 30% reduction in cost per install.

From impressions to impact

Second Dinner’s results underscore the efficacy of in-app ads, when they feel native to the experience, a signal that many are following.  In a world where players are quick to tune out or block traditional ads, marketers need new ways to connect authentically without undermining user trust. 

Discord’s move into in-app advertising, powered by AppsFlyer’s transparent measurement sets a new standard. Performance marketing can go beyond interruptive impressions to create engaging, value-driven experiences that benefit players, developers, communities, and more.

Central to this shift is Discord’s reward-based Quest format. Players complete specific actions in exchange for in-app rewards, making the ad experience inherently participatory and motivating. This dynamic aligns with Discord’s community-driven audience – already primed for gamified, reward-based interactions. When paired with AppsFlyer’s real-time attribution and analytics, it gives marketers the insights to optimize in the moment and prove ROI.

“With partners like AppsFlyer, we’re elevating the sophistication of our measurement capabilities, giving advertisers on Discord the confidence to measure performance with trusted, privacy-safe solutions,” said Adam Bauer, VP of Sales and Ads Solutions at Discord of the partnership. “As our first mobile measurement partner, AppsFlyer has already shown its ability to drive stronger results for Quest campaigns, and we’re excited to unlock even greater impact for advertisers.”

Together, Discord and AppsFlyer are demonstrating what’s possible when advertising aligns with customer behavior. Quests aren’t just ads, they’re invitations. Invitations to discover new worlds, engage with beloved characters, and share experiences with friends. AppsFlyer’s measurement and attribution is enabling developers to finally see the real business impact of those moments.

For years, performance marketing has been about chasing impressions and installs. Discord Quests is proving the efficacy of shifting towards ads that feel like part of the end-user’s experience, and the data proves players are responding. With AppsFlyer’s attribution and analytics powering Discord’s Quests, developers like Second Dinner are already achieving outcomes that go beyond expectations.

The post How Discord Quests Fueled Second Dinner with In-App Ads Measurement Powered by AppsFlyer appeared first on AppsFlyer.

In 2008, Apple re-invented the application world. The App Store imposed rules, offered trust, enforced quality, and created a marketplace that shaped user expectations, developer actions, and revenue models. It standardized how apps are discovered, bought, and updated.

In 2025, OpenAI is introducing a change that may well be just as big – to transform how humans talk to applications. Its recent push into commerce (Instant Checkout) and embedding apps inside ChatGPT (via the new Apps SDK) is not just product expansion. It’s an attempt to turn ChatGPT into a new operating system for human‑app interaction.

In other words, ChatGPT is changing how apps are used. 

What OpenAI is building: Instant Checkout + Apps SDK

Let’s review what OpenAI has publicly launched in the last couple of weeks:

• Instant Checkout: Users can now buy single‑item products entirely within ChatGPT (currently only in the US and only from Etsy sellers, and soon many Shopify merchants). Users see a “Buy” button, confirm shipping & payment, and the order flows to the merchant’s backend. ChatGPT acts as an “agent” between the user and the merchant via the open Agentic Commerce Protocol (ACP), co‑developed with Stripe.
• Agentic Commerce Protocol (ACP): An open standard that lets AI agents, like ChatGPT, interact with merchant systems (payments, order, fulfillment) while preserving merchant control and minimal data sharing. The ACP benefits all participants: businesses remain the merchant of record while keeping control over products, presentation, and fulfillment; AI agents can embed commerce directly into conversations without becoming the merchant themselves; and payment providers handle transactions securely via encrypted tokens. This structure ensures transparency, interoperability, and scalability to pave the way for a true agent‑driven commerce layer.
• Apps (SDK) inside ChatGPT: OpenAI is previewing a system where third‑party services can embed “apps” within ChatGPT. The app gets conversation context, responds with structured output, UI elements, and user flows. Developers will connect via server APIs, handle auth, maintain sessions, etc.

Put these together, and the lines between search, app, web, and commerce blur.

Just imagine this: You ask your assistant to reorder your regular stock of nutritional supplements, and it instantly finds the right seller, places the order, and tracks the delivery – no app-hopping or manual browsing. Or picture asking it to hunt down that rare sneaker drop in your usual size the moment it becomes available. Repetitive shopping becomes frictionless, and finding rare items turns from a manual hunt into an automated routine.

Why it feels like a new OS 

• UI abstraction: In the world of ChatGPT apps, the “UI” is conversational + structured responses. Branded UI wrappers, splash screens, custom navigation and overall user experience might matter less. What counts is content, value, task accuracy, and responsiveness.
• Protocol & standards: Apple gave us the App Store rules; OpenAI is giving (or will give) ChatGPT apps a specification, SDKs, security rules, and marketplace policies. Just as iOS apps needed to comply with Apple’s guidelines, apps will need to comply with ChatGPT’s.
• Gatekeeper role: Apple once arbitrated the app ecosystem; ChatGPT can now mediate discovery, ranking, monetization, permissions, and access.
• Conversational routing: Rather than users opening a brand’s app, users may name the task and let ChatGPT decide which app to route to. E.g. “Book me a flight to Tokyo” — ChatGPT picks one or more travel apps under the hood.

So, ChatGPT isn’t killing apps —  it’s changing how apps are used; less focus on monolithic apps or brand cosmetics, and more focus on task standardization and getting things done. 

What apps / brands will need to do differently

As ChatGPT evolves into a primary interface for digital interaction, apps and brands will need to rethink how they’re built, accessed, and monetized within this new ecosystem. Here’s what they need to do:

1. Focus on core service, not UI shell: Your app’s “skin” becomes less important than what it does. Whether it’s product search, booking, analytics, or chat support — your core logic, APIs, and data matter most.

2. Build a server‑side API with an AI mindset: You’ll expose endpoints for your app to accept requests from ChatGPT, send structured responses, monitor state across turns, manage errors, and fall back gracefully. You’ll handle authentication, rate limits, versioning, throttling.

3. Comply with ChatGPT app standards: OpenAI will require apps to adhere to safety, privacy, UI guidelines, response formats, error handling, permission flows, rate quotas, etc. (similar to how Apple reviews apps for guidelines, security, UI, performance.)
4. Map deep linking / routing / canonical entry points: Even inside ChatGPT, you’ll want to map tasks to internal flows. E.g. ChatGPT says “Go to product catalog → filter → add to cart → checkout.” Deep links become “intent links” from conversation into your internal logic. You may provide templates for how ChatGPT should “open” a context in your app logic (with parameters). Similar to how we use deep links today (e.g. myapp://product/123?ref=chatgpt).

5. Monetizate, discover & promote: Once apps run natively in ChatGPT, there will be incentives to stand out. Brands will pay for:

• Featured placement or priority ranking
• Promoted suggestions for tasks
• Sponsored content inside ChatGPT UI
• “Upsell” modules exposed inside the app flows

This mirrors what happened in search (Google Ads), and in the App Store (search ads, featured apps). ChatGPT will become a new channel for “in‑chat promotion.”

6. Provide signals for better targeting & personalization: With ChatGPT knowing users’ conversational history, preferences, and context, it has potential to tailor which app services, products, or offers to show. That gives great targeting power to apps embedded in ChatGPT but also raises the bar: you’ll want to optimize for conversion under this “agentic” environment.

Because OpenAI will see user interactions across multiple apps/flows, it could help matching users to offers in smarter ways. Brands will need to provide signals, feedback, and engagement signals to train better recommendation logic.

Measurement & attribution in the age of AI apps

Measurement has always been the backbone of smart growth strategies. That won’t change. What will change is what we measure and how. In a world where users no longer install apps but interact with them inside AI environments, the traditional funnel is replaced by fluid, intent-driven experiences.

Attribution will move beyond counting installs to understanding which prompts, and interactions actually drive outcomes. Instead of measuring downloads, we’ll monitor completed actions. Instead of mapping user journeys across screens, we’ll map them across conversations, agent calls, and tasks.

This future demands attribution models that can follow intent through multi-step, multi-agent paths, connecting upstream triggers to downstream impact with precision. And just as important, it will rely on first-class feedback loops – where conversions, cancellations, or refunds flow back into the ecosystem. These signals will help AI agents rank, recommend, and personalize experiences more effectively, turning every action into intelligence that sharpens the system.

Measurement in this new era will be lightweight, contextual, real-time, and deeply integrated into the fabric of how users and agents interact, driving smarter decisions for both advertisers and platforms.

What’s next? 

This might only be the first step into a new type of software units consumers haven’t seen before: 

1. Smaller apps or app “primitives”: Rather than full monolithic apps, smaller brands might want to penetrate with smaller, micro‑skills apps (e.g. “translate text,” “summarize document,” “book flight leg”) that chain into flows. Brands will compete to have their micro‑skills embedded in many chains, while emerging players might get a chance to penetrate existing markets. 
2. Multi-apps orchestration: Multiple apps might collaborate within one conversation: e.g. booking a trip requires hotels, flights, car rentals. ChatGPT (or agent) may orchestrate across apps. Those who expose APIs that play nicely will gain advantage.
3. Adaptive UIs generated on the fly: Because ChatGPT controls layout, developers might deliver augmented UI snippets (cards, forms) rather than full app UI. Apps will deliver structured data + rendering hints, not fixed screens.
4. “Appless” brands: Some brands may skip a standalone app altogether and live only as ChatGPT apps. Their brand presence is entirely via conversation, but they still need backend systems, analytics, marketing funnels, retention loops, etc.
5. Rich context & memory across apps: OpenAI may maintain a memory layer: user preferences, history, context that flows across app calls, enabling cross‑app personalization, unified profiles, and more intelligent suggestions.
6. Predictive task suggestions: Because ChatGPT sees what users do over time, it could proactively suggest app tasks or offers inside the conversation: “Hey, you often book flights — would you like me to check fares for your upcoming trip?”

The final word

The app era is entering a new chapter. ChatGPT doesn’t mark the end of apps, on the contrary, it represents the next layer of its evolution. The winners will be the brands and developers who treat ChatGPT as a new operating system layer: one that demands clean APIs, conversation‑first logic, measurement integration, and smart monetization strategies.

At AppsFlyer, we see our mission extending: from post‑hoc attribution to being the measurement & insights engine within this ChatGPT OS. We’re already investing in MCP (Model Context Protocol) to enable marketing data to be queried via natural language and AI agents.

If you’re a brand, SDK vendor, or product team, now is the moment to explore:

• What flows of yours should become ChatGPT apps
• How your APIs, events, and measurement will map into that world
• How to surface your brand & offers inside ChatGPT’s discovery & monetization systems

The post When ChatGPT becomes the OS: A new era for apps, measurement & commerce appeared first on AppsFlyer.

The CTV opportunity

In 2025, Connected TV (CTV) is no longer a future trend; it’s where audiences already spend a significant portion of their time. For marketers, this represents one of the fastest-growing and most measurable advertising channels.

Let’s dive into some numbers to frame this opportunity: According to Nielsen, as of March 2025, streaming accounts for 44% of all TV time spent in the US. Nielsen research also found that 72% of all TV viewing is ad-supported, with almost half taking place on streaming platforms. Spending is following suit, with global open programmatic CTV ad spend reaching $5 billion in Q1 2025, up 10% year over year. 

This shift isn’t just limited to North America. According to Wurl, in Europe, 86% of consumers use CTV, with the UK’s CTV ad spend projected to reach £2.94 billion by 2028, and Germany seeing a 32% increase in CTV channel volume in 2024. 

AppsFlyer data reinforces this trend: total CTV-to-mobile installs (app downloads attributed to a CTV ad) nearly doubled between August 2024 and January 2025.

For marketers, the message is clear: audiences are moving fluidly across CTV, mobile, and other digital touchpoints, ad spend is following, and accurate measurement is no longer optional.

Complete and accurate measurement is essential for connecting the dots across channels and understanding the full user journey. That’s exactly the opportunity unlocked by AppsFlyer’s new integration with Roku.

The AppsFlyer–Roku integration

Roku, the #1 TV streaming platform in the US, Canada, and Mexico, offers advertisers unmatched reach across its ecosystem.

​​AppsFlyer’s new integration with Roku brings cross-channel attribution and real-time optimization across CTV and mobile. This integration ensures that rich attribution data from Roku Advertising flows directly into AppsFlyer, allowing you to see how streaming campaigns drive app installs, in-app activity, and long-term customer value.

“Together, Roku and AppsFlyer are bringing advertisers the transparency and performance they need in today’s evolving streaming landscape. By combining Roku’s scale as a dominant streaming platform with AppsFlyer’s trusted measurement, we’re helping marketers connect every impression to business outcomes and drive measurable growth.”

Yair Kahan, Director, Partner Development, CTV at AppsFlyer

With this integration, marketers can:

• Run end-to-end attribution across Roku’s in-stream video and native ads, powered by one of the most robust CTV user graphs in the market
• Measure end-to-end user journeys, including CTV-to-CTV, mobile-to-mobile, and CTV-to-mobile flows
• Gain improved visibility into Roku-driven conversions across platforms
• Streamline workflows with one consolidated integration that simplifies campaign management

“Our integration with AppsFlyer is already proving Roku’s ability to drive mobile app performance from CTV. Mobile marketers now measure and optimize directly in Roku Ads Manager, with the confidence and tools they expect from social. Roku’s identity, massive CTV inventory access, and price are now paired with a closed loop of app event signals that feed our performance algorithms. Roku is becoming the next great platform for evergreen advertising.”

Peter Hamilton, Sr. Director, Product Management at Roku

How does it work?

The AppsFlyer–Roku integration enables rich, multi-channel data capture across mobile and CTV, including impressions, clicks, installs, sign-ups, in-app events, revenue, video engagement, and other engagement metrics.

By unifying these datasets, marketers can connect the dots across channels, gaining a complete view of the customer journey — from Roku Ads exposure on the big screen to app engagement and purchases on CTV and mobile devices. 

This integration with Roku gives advertisers a single source of truth across TV and mobile, powering campaign optimization, attribution reporting, audience building, and ultimately driving growth with precision.

These capabilities are especially impactful at Roku’s scale. In early 2025, the platform exceeded 90 million streaming households, giving advertisers access to one of the largest engaged streaming audiences in the world.

Use cases: Turning CTV into business outcomes

The AppsFlyer–Roku integration opens up powerful opportunities for advertisers to drive measurable growth:

• Cross-device attribution: Ads shown on Roku TV streaming can now be tied back to installs and app launches across all devices in the household, helping advertisers maximize ROI by understanding how a single impression influences the entire home — not just one device.
• QR code attribution: With a simple scan from a CTV ad, AppsFlyer attributes the install directly back to the campaign, giving advertisers a frictionless way to bridge the gap between TV and mobile and turn passive viewers into active users in seconds.
• Drive new app installs: Extend your acquisition strategy by reaching fresh audiences in a premium, high-attention environment, enabling advertisers to capture incremental users who may not be reachable through other channels.
• Complement mobile campaigns: Use CTV as an upper-funnel channel to amplify performance marketing, extending the impact of mobile strategies by creating a holistic funnel that drives awareness on TV and conversion on mobile.

Making an impact 

With audiences spending more time streaming than ever before, advertisers need the tools to connect those moments of attention to measurable outcomes. Through the new AppsFlyer & Roku integration, we’re making it easier for brands to understand the impact of their Roku campaigns across channels and turn streaming engagement into business growth.

“It’s been truly exciting to launch a CTV app campaign on Roku Ads Manager, using our integration with AppsFlyer to measure installs and subscriptions. Results on some of our key metrics like CPA, ROAS thus far have been positive, as we continue driving towards our goals and seeing strong engagement from this audience.”

Jeet Niyogi, CMO Fliff Inc.

“Roku and AppsFlyer have been great partners in helping us drive new user growth. Their new integration has made it easy to measure and optimize our CTV campaigns for installs and user acquisition. Performance has been strong and continues to trend upward.”

Jeff Reichelderfer, VP User Acquisition Marketing, Brigit

Seizing the CTV opportunity 

The AppsFlyer–Roku integration empowers marketers to unify TV and mobile strategies, measure outcomes holistically, and optimize spend with transparency.

Getting started with the integration is simple. To take the first step click here.

The post Turn streaming into measurable growth with AppsFlyer and Roku appeared first on AppsFlyer.

With the official launch of iOS 26 on September 15, Apple introduced a significant change to Safari: the operating system version is now frozen in the browser’s user-agent (UA) string. This means Safari (including in-app web views) no longer reveals the current iOS version to websites or measurement tools.

For app developers and measurement teams, this kind of change isn’t just a detail — it signals how the web platform is shifting: fewer easy signals about a device’s OS, more reliance on privacy‑driven behavior, and a growing gap between what was possible and what will be reliable moving forward.

What this means for attribution

The user agent freeze has significant  implications for the mobile marketing ecosystem:

• Attribution platforms and martech tools often rely on granular signals in the UA string, including the OS version, to improve accuracy in attribution and analytics models.
• With the OS version now static, platforms that lean heavily on user-agent signals may face reduced precision when attempting to link installs, re-engagements, and user events.

Clearly, this change could disrupt attribution accuracy for some. But the good news is that it will not impact AppsFlyer customers.

AppsFlyer’s solution: Future-proof attribution

AppsFlyer’s engineering team detected the change early in iOS 26 beta versions and developed a forward-compatible fix to ensure attribution accuracy wouldn’t be compromised.

Here’s how our solution works:

• Frozen UA detection: When Safari presents a static OS version (e.g., “iOS 18.6”), the system identifies it as part of the new behavior introduced with iOS 26.
• Version correction: AppsFlyer’s attribution modeling relies on a combination of device-level and aggregated signals to restore and maintain attribution accuracy.

The bottom line: No impact on attribution. Your measurement continues without disruption, and AppsFlyer’s models remain fully supported and unaffected by the Safari user agent change.

What this means for you as an AppsFlyer customer

If you’re an AppsFlyer customer, there’s nothing you need to do. Our fix is already live and seamlessly working behind the scenes.

Your attribution data will continue to be:

• Reliable
• Consistent
• Unaffected by iOS 26 Safari UA changes

This is a direct reflection of our commitment to staying ahead of platform-level changes and continuously protecting the integrity of your measurement, attribution, and analytics – even in a rapidly evolving privacy landscape.

Key takeaway: you are covered. AppsFlyer Adapted to Safari’s UA Freeze

Apple’s decision to freeze the OS version in Safari’s user agent string marks another step in the industry-wide shift toward privacy-enhanced digital experiences. While this introduces technical challenges for attribution providers, AppsFlyer customers are already covered. 

Our team acted early, delivered a resilient solution, and ensured that your data remains trustworthy – no matter how the ecosystem evolves.

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