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.

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.

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.

AI is transforming marketing at lightning fast speed and the potential is mind boggling. But with all the hype, it’s time for a reality check. The reality is that most marketing AI isn’t delivering on the promise. 

The question is why?

Because AI doesn’t create data, it interprets it. And when you’re working with fragmented, unstructured, or poorly documented data, even the most sophisticated AI models will give you unreliable answers. So AI isn’t just about the model, it’s about the data.

This blog explores why models must be based on large-scale, contextual, and governed data to deliver reliable insights , and how fragmented or shallow inputs can lead to failure. Learn what AI-ready data really means and how to assess if your foundation is built for it.

What could possibly go wrong?…

The shift toward AI is real, and without accurate data that your AI models can read from, and without context, AI doesn’t just slow down. It can deliver misleading insights, setting you on a near-certain path to failure.

Teams are exploring agents, LLM interfaces, and predictive models across every aspect of mobile marketing. But all too often, AI is deployed on data that was never built for this kind of intelligent consumption. And when the most important layer is not even seen or ready, it’s easy to hide behind false promises. 

1. You miss the full picture

Your AI can’t “see” what it doesn’t have. Missing data from key partners, channels, or post-install events means your AI works with only part of the user journey. That leads to flawed attribution, unreliable predictions, and optimizations based on guesswork. This is especially critical to support fraud protection initiatives, where pattern recognition depends on complete, full-funnel visibility.

2. You get inconsistent logic

Each platform and ad network defines key metrics differently; what counts as a conversion, an install, or a session often varies. When your AI is working with conflicting definitions, it can’t compare or interpret performance accurately.

And when each partner brings its own logic to the table, combining everything into a single view becomes a real challenge. Fragmented data rules create blind spots, mismatches, and gaps that make your overall data picture harder to trust, and even harder to act on.

As a result, segments break down, ROAS gets distorted, and automation fires on mismatched or misleading criteria.

Also, efficient fraud prevention depends on data consistency — when event definitions and data structures vary across sources, it becomes harder for models to learn patterns, identify anomalies, and act with confidence.

3. Your data lacks clarity

Field names like ‘event_purchase’ or ‘open_time’ are meaningless without documentation. Without semantic clarity and consistent formatting, AI agents (and humans) struggle to interpret data which leads to incorrect answers, misaligned KPIs, and broken insights. AI models can’t compare apples to apples.

4. You can’t move in real time

AI needs live, governed access to data. If your system depends on batch ETLs or manual stitching, agents can’t respond fast enough. This delays anomaly detection, slows optimization, and renders real-time automation ineffective.

5. You lose governance and traceability

In a privacy-first world, AI systems must be able to prove where data came from, whether consent was granted, and how the data has been transformed. Fragmented systems make this nearly impossible, exposing your team to compliance risk. You’d want to rely on clean, traceable data pipelines to also maintain fraud protection efficacy without compromising regulatory compliance.

Why it matters 

AI doesn’t know when your data is wrong. It just scales whatever it’s given, fast. That’s how flawed inputs quietly evolve into high-speed, high-stakes failure. 

What AI-ready data really means (and what to look for)

AI-ready data isn’t just clean — it’s designed for intelligent systems. Here are the principles to evaluate:

Pro tip: Look for data systems that treat information not just as storage but as a product designed for intelligent consumption.

Scale and context : Why richer data performs better

Data isn’t just about quantity, it’s about coverage and context. This is why data scale and quality are the most strategic investments you can make for long-term AI performance.  The most effective marketing AI is built on data that:

• Reflects real-world complexity: Complete user journeys across multiple touchpoints and platforms
• Provides clear attribution context: Which campaign, channel, or creative actually influenced the outcome
• Maintains consistent identity resolution: The same user recognized across different devices and sessions

Systems that integrate across multiple partners and channels offer better ground for AI because they bring a more complete, contextually rich picture of user behavior.

Pro tip: When evaluating AI readiness, ask how comprehensive and context-rich your data inputs really are.Incomplete data leads to incomplete insights.

The role of governance and privacy in AI

In the world of AI, governance isn’t a feature. It’s the foundation. If you can’t trace your inputs or confirm consent, your AI outputs aren’t defensible.

Ask yourself:

• Can you prove where your data came from?
• Can you explain how your AI reached its conclusions?
• Can you show that every signal used was consented?

This is known as AI explainability, and it’s now a regulatory (and operational) requirement. Clean lineage, strong identity frameworks, and privacy-aware infrastructure aren’t just for compliance. They also boost fraud protection, optimize performance, and reduce business risk.

Key privacy and governance considerations:

• User consent that travels with your data
• Clear data lineage and auditability for every AI decision
• Infrastructure that respects user identities across platforms and partners

Pro tip: AI outputs are only defensible if the data behind them is governed, compliant, and traceable from insight to source.

Questions every marketer should ask before scaling AI

Evaluate your AI readiness by answering the following:

• Can I explain how my data is structured and what each field represents?
• Do I know which events are governed by user consent?
• Are my business metrics (LTV, churn, ROAS) clearly defined across all data sources?
• Do I have consistency between what my teams see and what AI systems access?
• Can AI tools operate autonomously on my current data without constant human correction?
• Does my data reflect complete user journeys, or just fragments from individual channels?

If the answer is “no” to any of these, your data foundation may not be AI-ready yet.

The bottom line: Smart AI starts with better data

You don’t need to fear AI but you do need to prepare for it properly. Clean data is not enough. You need governed, structured, contextual, comprehensive, and consent-aware data to power effective marketing AI.

The teams that succeed with AI aren’t necessarily those with the most advanced models, they’re the ones with the most reliable, complete data foundations.

Start with the foundation. Then scale with true confidence.

The post Your AI is making marketing decisions on bad data – here’s how to tell appeared first on AppsFlyer.

Imagine turning any marketing question into a decision instantly. Without using dashboards, no waiting on data teams, and no technical roadblocks. That’s exactly what AppsFlyer’s Model Context Protocol (MCP) delivers. Starting today, marketers can leverage AI agents that access real-time insights across their entire stack, and accessible through their preferred Large Language Model (LLM).

AppsFlyer MCP is not just a new feature – it’s a paradigm shift in how marketers interact with data. As part of AppsFlyer’s broader vision to superpower marketing teams with AI, MCP helps transform everyday work into AI-powered workflows. By unifying clean, trustworthy data with conversational access and intelligent orchestration, MCP enables both humans and AI agents to act on insights in real time. 

Ready to change how your team works? Let’s dive in

A Smarter Way to Work: What Is MCP?

AppsFlyer’s Model Context Protocol (MCP) is a powerful new way to access and act on your marketing data directly through natural language. Whether you’re exploring campaign performance, managing audiences, or troubleshooting deep links, MCP connects your AppsFlyer data to your preferred LLM interface.

What makes MCP special is its ability to connect to AI agents that can query the database based on your preferences – or empower anyone, regardless of technical background, to get the exact data they need, instantly. In both cases there’s no waiting, no dependencies. Whether you’re asking questions or enabling AI agents to take action, MCP delivers clarity and execution – instantly.

How It Works: Built on AppsFlyer’s API Foundation

MCP acts as a bridge between your preferred LLM – like Claude, ChatGPT, Gemini, and more – and AppsFlyer’s comprehensive suite of APIs, including attribution, analytics, audiences, OneLink, and more. When you enter a prompt, MCP converts that natural language input into structured API calls, fetches the relevant data, and returns it alongside contextual metadata for interpretation – whether by a human or any AI agent built on top of it.

“What makes MCP truly powerful isn’t just the interface – it’s the data behind it. At AppsFlyer, we provide one of the richest, most accurate, fraud-protected, and privacy-centric datasets in the industry, trusted by over 7,000 leading brands. By sitting on top of this foundation, MCP enables marketers to instantly unlock insights and turn data into real-time, high-impact decisions.”

Barak Witkowski, CPO of AppsFlyer

Because MCP operates through an open protocol, our customers can also build AI agents or custom workflows directly on top of it. Whether you’re training a media mix optimization agent, building an autonomous audience manager, or embedding MCP into internal tools, the framework provides the flexibility and scalability needed to power intelligent orchestration at any level, with minimal setup.

Why MCP Matters: Turning AI Into a Marketing Co-Pilot

1. Instant Insights, No Engineering Required
Need a breakdown of ROAS by channel? Want to know which campaigns are driving the highest LTV? Just ask. MCP can be connected to your AI agents, or translates your natural-language prompt into an API query, retrieves the data, and returns it in a contextualized format, right in your chat window.

2. Unlock AI at Scale
MCP supports both human-triggered and autonomous agent queries, making it ideal for scalable workflows. Whether you’re part of a growth, CRM, product, or martech team, anyone can tap into rich insights with zero setup, no engineering lift required.

3. Privacy and Precision, Built-In
Powered by AppsFlyer’s industry-leading attribution infrastructure, MCP ensures every query is grounded in high-quality, privacy-compliant data- encrypted, secure, and compliant by design.

Key Use Cases at Launch

• Marketing Performance Analytics: Access real-time performance data to analyze campaign results, track spend trends, and compare ROI – whether through a direct chat prompt or by deploying autonomous agents that monitor performance, surface optimization opportunities, and even autonomously execute actions.

• Audience Management: Gain full visibility into how audiences are defined, segmented, and activated across your organization. Use AI chat to query audience structures or dig into performance trends in real time – or build intelligent agents that detect overlaps, recommend optimizations, and sync updates across channels – with the ability to act automatically when permissions allow.

• Link Governance: Audit OneLink templates and link behavior with conversational ease or leverage agents to continuously monitor link hygiene, flag inconsistencies, and ensure every campaign follows best practices – automatically.

• App Configuration & Help Center Assistance: Quickly retrieve app setup details or implementation guidance on demand – or empower agents to detect misconfigurations, suggest fixes, or surface documentation contextually when issues arise.

Built for Marketers, Loved by Teams

MCP is designed to solve real pain points across performance, retention, CRM, and martech teams:

• Cut decision-making cycles from days to seconds
• Free up analysts and BI engineers by enabling self-serve data access
• Reduce tool-jumping with one unified, intelligent interface

“It’s like having a custom analyst on demand for every team member executed in just a few minutes.”

Elay De Beer, CEO of Buff.game

The bottom line

AppsFlyer MCP marks a pivotal milestone in our broader vision of AI-driven marketing, a vision anchored in empowering marketers with the perfect synergy of human creativity and machine intelligence.

As Barak Witkowski, our Chief Product Officer, outlines in Inside AppsFlyer’s AI Strategy, our approach to AI centers on three key pillars: clean, AI-ready data; assistive tools that act as strategic partners; and autonomous agents that execute at scale. MCP brings these pillars to life by allowing both humans and AI agents to access AppsFlyer data in real time through natural language interfaces.

But this is just the beginning.

At launch, MCP supports high-impact workflows like campaign analysis, audience visibility, and deep link management. Soon, it will power a growing set of capabilities, enabling everything from predictive insights to agent-driven automations. As we continue to evolve MCP, our goal is to support every strategic and operational marketing need, with intelligence built into every interaction.

To explore more:

• Check out the AppsFlyer MCP webpage
• Visit the Knowledge Base for setup and use cases

Ready to experience the future of marketing intelligence?
Join the MCP Beta if you’re an AppsFlyer customer, or Schedule a Demo if you’re new to AppsFlyer.

The post Introducing AppsFlyer MCP: Marketing Intelligence for the AI Era appeared first on AppsFlyer.

AI-first marketers are redefining the marketing game by combining the best of human intuition with the unmatched speed and scale of machines. At AppsFlyer, we’ve witnessed this transformation over the years across thousands of brands, from finance and gaming to e-commerce and travel, all around the globe.

Mastering AI – your competitive edge

Today, the balance between human creativity and machine intelligence reaches its peak, driven by the greatest technological revolution of our lifetime: AI. Now, more than ever, what separates top-performing marketers from the rest is their understanding that human creativity remains their competitive advantage, amplified by AI. However, truly mastering AI involves much more than simply using new tools; it requires strategic integration into every facet of your marketing approach. It is no coincidence that this was the hottest topic among over 2,500 marketers at the industry’s largest conference, MAU Vegas.

At MAU Vegas last week, we presented three essential pillars that transform marketing teams into AI-mastery teams. Based on this framework, shaped together with our customers, we built the AppsFlyer AI strategy, empowering marketers with AI solutions explicitly designed to amplify human creativity, strategic insight, and execution, maximizing their impact:

1. Data – the foundation of AI

AI is only as powerful as the data it’s built upon. AppsFlyer Core AI ensures marketers have accurate, rich, granular, and clean data ready for immediate use. Here are some standout examples:

• Fraud protection: AppsFlyer fraud protection AI layer uses advanced behavioral analysis to instantly detect and block sophisticated fraud tactics like bots, device farms, and fake installs. This new AI layer, built on top of AppsFlyer’s Protect360 anti-fraud suite, safeguards brands’ marketing budgets and ensures every dollar contributes to authentic user engagement, significantly enhancing the ROI.

• Creative optimization: Leveraging AI, AppsFlyer’s Creative suite evaluates all creative assets across campaigns and channels to uncover the highest-performing elements within the creative asset. By identifying successful trends and patterns in real time, these AI-generated insights can seamlessly integrate into your generative AI creative factory, creating an autonomous AI loop. With such a closed loop, Marketers can automatically and autonomously refine and scale winning creative strategies, boosting overall campaign effectiveness. Brands utilizing this complete AI loop have reported a remarkable 200% increase in production success rates.

These are just a few examples of capabilities that leverage accurate, clean, and rich data, enabling marketers to build effective AI models and solutions in harmony with their human expertise.

To fully unlock AI’s potential, data readiness is non-negotiable. At AppsFlyer, we see it as our responsibility to ensure our customers’ data is not only accurate and granular but also inherently AI-ready, enriched with meaningful metadata, and protected by robust governance. This includes strict controls on data quality and access, as well as built-in mechanisms to safeguard privacy and compliance. Clean, trusted, and well-documented data empowers our customers to safely operationalize and scale AI models or tools, from assistive to agentic, unlocking faster innovation and greater impact.

2. Assist – your strategic growth partner

It’s incredible how quickly we all adapted to interacting with AI through conversational, assistant-based interfaces. AppsFlyer Assist takes this experience to the next level, serving as your personal AI copilot that provides instant clarity, insights, and practical assistance to streamline and enhance your marketing strategy. Among other capabilities, Assist can provide:

• Instant insights: Quickly translate complex, raw data into clear, actionable insights, dramatically reducing decision-making time. These insights enable marketers to rapidly adapt strategies based on real-time performance data, ensuring faster and more informed marketing decisions while preventing missed opportunities.

• Efficient setup: AppsFlyer Assist offers guided, step-by-step assistance to effortlessly configure platform settings, partner integrations, and in-app event definitions. By eliminating setup errors and accelerating implementation, marketing teams can quickly leverage new capabilities and best practices.
  

This experience is already becoming our users’ trusted partner for growth, helping them analyze data, set up integrations, discover best practices, troubleshoot day-to-day issues, and much more.

3. Autonomous AI agents – execution powerhouses

Autonomous AI Agents are more than just automation tools; they are essentially new team members deployed to your team. You give them tasks, and they execute effectively. You can even orchestrate multiple agents to collaborate, effectively deploying new teams under your supervision and guidance, unlocking tremendous opportunities. Here are a few examples of our pre-built agents:

• Audience builder: An AI agent that proactively creates and suggests high-value user segments for both user acquisition and retention. These segments are dynamically updated based on performance, ensuring continuous optimization and maximum impact on user engagement and conversion.

• Media mix optimization: An AI-driven media mix agent that analyzes your current advertising performance against industry benchmarks and historical data, then presents tailored recommendations based on your specifications. You retain control to either directly implement these recommendations or deploy other agents to handle these optimizations automatically, ensuring optimal efficiency and maximum return on ad spend.

In addition to these out-of-the-box agents, brands will be able to create custom AI-powered agents and automations via our new Model Context Protocol (MCP) servers. These servers allow for easy connection of AppsFlyer’s APIs to any large language model (LLM), such as ChatGPT or Claude. This agent platform and the MCP servers will enable marketers to swiftly tailor complex workflows and automations, seamlessly integrating AI-powered decision-making into their daily operations.

Your path to AI mastery starts here.

At MAU Vegas, it was clear that almost all brands and advertisers are shaping their AI strategies these days.

We believe that by ensuring you have accurate, clean, and rich AI-ready data, autonomous AI loops, conversational AI copilots, and new AI team members working on your behalf, you are well-positioned to help your marketing team master AI. This approach effectively balances human creativity and expertise with AI power, driving business growth and maximizing strategic potential.

The future of marketing is here. Embrace AI-driven excellence with AppsFlyer. Let’s set the pace together.

The post Unveiling the future: Inside AppsFlyer’s AI strategy appeared first on AppsFlyer.

The famous FORCE so brilliantly depicted in Star Wars is like AI.  

The advancement of Artificial Intelligence is delivering amazing benefits in so many areas. But just like the FORCE, AI is also exploited by a dark side in ways that are harmful to people and businesses, particularly in ad fraud.  

On the one hand, AI can help detect fraud with remarkable precision. Yet at the same time, it gives fraudsters the ability to orchestrate sophisticated scams, putting billions of ad budget dollars at risk.  

The pressing question is: can AI eradicate ad fraud, or is it fueling the problem?

By understanding that AI is a double-edged sword, advertisers can turn it into their greatest defense against evolving threats. With the right strategies, businesses can embrace AI not just as a tool for optimization but as a robust shield against fraud.

The growing threat of AI-driven ad fraud

Ad fraud isn’t new of course, but AI has taken it to new levels of sophistication. Bad actors now use advanced practices to create fake traffic, hijack devices, and mimic human behavior with precision — making them much harder to detect. These methods cost advertisers billions each year, draining budgets and eroding trust.  

In broad terms, ad fraud refers to deceptive practices that manipulate ad systems to divert spending. Common forms include:  

• Fake traffic: Bots imitating humans to inflate impressions or clicks.  
• Botnets: Networks of hijacked devices orchestrating fraudulent activities on a massive scale.  
• Click fraud: Artificially boosting click-through rates, often to exhaust a competitor’s ad budget or generate illicit revenue.  
• Fake users: Fraudsters creating realistic profiles that mimic real user engagement.  

Why AI is making ad fraud worse  

AI has become a catalyst for ad fraud, giving fraudsters more sophisticated, scalable, and effective tools. Open-source AI platforms have lowered the barrier to entry, enabling bad actors to deploy advanced fraud schemes with minimal effort. 

Fraudsters use Generative Adversarial Networks (GANs) to create synthetic content, including deepfake ads and fake users that interact with real campaigns. These AI-generated interactions can convincingly mimic human behavior, making them difficult to detect and flag. For example, fraudsters leverage GANs to generate fake user profiles that seamlessly engage with ads, tricking analytics tools into recording fraudulent engagement as authentic.

AI is also enhancing click farms, making them more difficult to detect. AI-driven algorithms can simulate diverse user behaviors, such as scrolling, dwell time, and varied click patterns, making fraudulent engagement look increasingly realistic. Additionally, AI is making it harder for Click-To-Install-Time (CTIT) detection, improving timing, randomness, and precision in mimicking real users with non-existent flows.

The financial impact is staggering. Statista predicts ad fraud losses will climb from $84 billion in 2023 to as much as $172 billion by 2028. With the accessibility of AI tools, fraudsters are becoming more sophisticated and widespread. 

App marketing is also plagued by ad fraud. According to AppsFlyer estimates, financial exposure to app install ad fraud eclipsed $17 billion in 2024 (this refers to the amount of money that would have been lost to fraud had there not been any fraud detection; in reality, much of this is blocked and therefore not paid for).

Consider CycloneBot, a scheme targeting Connected TV (CTV) platforms. Using AI, it inflates viewing sessions and traffic, costing advertisers millions monthly. Other examples include BeatSting, an audio ad fraud scheme that generates fake audio traffic, siphoning over $1 million per month from advertisers. As well as FM scams, an additional audio scheme where fraudsters blend fake audio traffic that appears to be legitimate user activity across various devices and audio players. 

These fraudulent interactions distort engagement metrics and mislead advertisers into believing they are reaching real audiences. These cases highlight how fraudsters are leveraging AI to scale their operations and evade detection.

Scalper bots have also infiltrated digital campaigns. AI tools are being used to automate the bidding for digital ad placements, artificially inflating costs and leading to wasted ad spend. These AI-powered fraud schemes execute complex multi-click patterns, targeting high-value programmatic campaigns and making detection increasingly challenging

…and there are additional schemes being enacted which none of us have yet to uncover, as fraudsters continue to innovate, leveraging AI in diverse ways to exploit vulnerabilities across the digital advertising ecosystem.

Fighting fire with fire: how AI can combat ad fraud  

While AI enables more sophisticated fraud, it’s also the most powerful tool to fight it. Advanced machine learning models and predictive analytics help advertisers to detect and block fraudulent activities in real time, often before damage is done.  

AI solutions excel in several areas when combating ad fraud:  

• Anomaly detection: Algorithms monitor traffic and flag unusual patterns, such as sudden spikes or inconsistent behaviors.  
• Continuous learning: By analyzing new data, AI evolves to detect emerging fraud tactics and stay ahead of fraudsters.  
• Enhanced accuracy: AI-powered systems distinguish legitimate user activity from fraudulent behavior with high precision, reducing false positives. 

Success stories 

AppsFlyer AI-powered tools prevent billions of dollars in fraudulent transactions by adapting to new threats and providing real-time fraud prevention. Similarly, other solutions employ predictive analytics to anticipate fraud trends, using graph-based methods to detect fraudulent networks and connections.  

Our AI enhances detection, speed, as well as actual deterrence of ad fraud with key benefits including:  

• Faster fraud detection: Identifies fraudulent activity up to 8X faster, helping businesses avoid substantial financial losses and data inaccuracies.  
• Improved deterrence: Fraud attempts are caught and mitigated 14X faster, significantly reducing the window for fraudsters to exploit new bypasses and loopholes.  
• Greater efficacy: Maintains over 90% fraud detection efficacy even after a fraud bypass, with an average of just 9% decline in detection accuracy.  
• Enhanced accuracy: Ensures a 7X improvement in detection accuracy.  
• Real-time detection: Identifies up to 60% more post-attribution fraud in real-time, reducing the number of fake users/installs immensely.  

Challenges in using AI to combat ad fraud

Despite its effectiveness, AI is not a silver bullet. Fraud detection systems often rely heavily on historical data, which can make it difficult to identify completely new fraud tactics. This creates a constant cat-and-mouse dynamic—fraudsters adapt as quickly as detection improves.

Another challenge is explainability. AI-driven fraud detection systems can sometimes produce results that are difficult to interpret, making it harder for advertisers to understand why certain activities are flagged as fraudulent. Ensuring transparency and interpretability remains a crucial factor for AI adoption in fraud prevention.

Privacy and ethical concerns further complicate matters. AI fraud prevention tools must comply with global data protection regulations such as GDPR while still maintaining the effectiveness needed to combat sophisticated fraud tactics. Striking the right balance between user privacy and fraud detection remains an ongoing challenge.

Turning AI into your ad fraud ally 

To stay ahead of fraud, organizations need a comprehensive and proactive approach. AI alone isn’t enough—it must be combined with human expertise and strategic collaboration to be truly effective. Here are the steps businesses should take:  

• Invest in advanced AI solutions: Leading tools offer advanced capabilities for fraud detection and prevention.  
• Blend AI and human expertise: Analysts play a vital role in refining AI outputs, interpreting nuanced patterns, and addressing edge cases that automated systems may miss.  
• Collaborate across platforms: Sharing intelligence with peers and industry stakeholders strengthens collective defenses against sophisticated fraud schemes.  
• Stay adaptive: Regularly update detection models with new data to counteract evolving fraud tactics.  

Future-proofing strategies

Fighting ad fraud requires businesses to think ahead. Federated learning, for instance, enables organizations to collaborate on fraud detection without sharing raw data, ensuring privacy while enhancing results. 

Additionally, fostering a culture of innovation and experimentation—through cross-functional teams and partnerships with technology leaders—helps organizations remain agile and proactive. Building industry alliances can further bolster defenses by pooling insights and resources.

AI innovation has proven to be a great advancement for businesses, especially in recent years, helping many enhance their capabilities, offering, services, and beyond. 

But it is a double-edged sword which can be used for harm just as for good; therefore, businesses must be aware of the pros and cons of such powerful technology, and continue to contribute, innovate, and utilize it to combat ad fraud in order to stay ahead of fraud sophistication. 

Key takeaways

AI is both a risk and a solution. Fraudsters exploit it for sophisticated scams, but it also powers the most effective fraud detection tools.

• Ad fraud is growing. Losses are projected to reach $172 billion by 2028, making proactive fraud prevention more critical than ever.

• AI-driven fraud detection works. Solutions like anomaly detection and predictive modeling are key to combating evolving fraud tactics.

• Human expertise is still essential. AI alone isn’t enough—expert oversight ensures more accurate and adaptive fraud prevention.

• Collaboration and innovation are key. Industry-wide cooperation and continuous technological advancements are the best ways to stay ahead of fraudsters.

The post The force of AI in ad fraud: fighting innovation with innovation appeared first on AppsFlyer.

On January 28, AppsFlyer North America kicked off the year for the marketing measurement industry in a big way with our first-ever MAMA San Francisco at the stunning Pier 27. With a breathtaking view of the Bay as our backdrop, we brought together some of the brightest minds in mobile marketing for a day packed with insights, innovation, and industry connections.

For years, MAU has been the premier mobile marketing event of Q2. With MAMA SF, the industry has its must-attend Q1 event, where the most influential leaders gather to set the agenda for the year ahead. This wasn’t just another conference; it was a rallying point for the future of mobile marketing.

A day of energy, optimism, and momentum

From the moment doors opened, the excitement in the room was undeniable. The energy wasn’t just about learning – it was about connection. Attendees were eager to share their plans for the year, swap ideas, and forge new partnerships. The timing of MAMA SF also set the tone for what’s ahead: a year of growth, collaboration, and big moves in mobile marketing.

Guiding us through the day was award-winning tech journalist Jennifer Jolly, who brought her signature expertise and enthusiasm to the stage, along with AppsFlyer’s Bobby Sayers and Karina Paramonova, and App Masters founder, Steve Young. Their engaging moderation kept the conversations dynamic and thought-provoking, ensuring every session on both stages and the livestream was packed with insights and real-world takeaways.

Conversations that will shape the year ahead

MAMA SF wasn’t just about reflecting on where the industry is—it was about defining where it’s headed. Some of the biggest names in the space took the stage to deliver forward-thinking insights, industry-wide recaps, and game-changing strategies.

Some of the hottest sessions from MAMA included:

• Why Do the Best Strategies Often Go Against the Grain?
  Growth expert Adam Miller broke down how counterintuitive strategies, like simplifying measurement and prioritizing scalability, can challenge norms and drive sustainable growth.
• The AI Revolution in Mobile Marketing
  Industry experts like Noelle Russell unpacked how AI is fundamentally transforming personalization, marketing efficiency, and creative automation.
• How Do You Navigate Mobile Marketing in 2025?
  AppsFlyer Chief Product Officer Barak Witkowski explored the key challenges and opportunities shaping mobile marketing in the year ahead.
• How Online Communities Fuel User App Growth
  Reddit’s Ryan Angerami explored leveraging community engagement to unlock sustainable app growth, foster meaningful connections, and amplify your brand’s impact.
• Privacy Sandbox: Is This SKAN All Over Again?
  AppsFlyer’s Eran Dunsky led a deep dive into the Privacy Sandbox, discussing whether it mirrors past industry shifts and how mobile marketers should prepare for what’s next.

MAMA on-demand: The learning continues

Couldn’t make it to MAMA SF? Or want to revisit your favorite session? In just a few weeks, we’ll be releasing MAMA On-Demand, featuring must-watch sessions from the Explore Stage, including insights from:

Noelle Russell, AI expert and thought leader
Adam Miller, growth strategist and mobile veteran
Zynga’s Nebo Radovic, discussing the future of mobile gaming
Eric Seufert, unpacking the latest innovations in measurement and attribution

Stay tuned for details on how to access the full session library—because the conversations we started at MAMA SF are just the beginning.

MAMA SF by the numbers

Nearly 500 attendees came together to kickstart 2025 in style
20+ sessions covering the biggest topics in mobile marketing
18 partner sponsors and 10 exhibitors showcased cutting-edge innovations
First-ever MAMA Live, bringing the MAMA content to viewers at home
MAMA Cares made an impact, raising funds for four charities supporting the Los Angeles community affected by wildfires

A huge thank you!

MAMA San Francisco was about more than just great content—it was a statement from the industry about the enthusiasm that has brought the mobile marketing community together, the energy sparking new ideas, and building the momentum for an exciting year ahead. Thank you to our attendees, speakers, sponsors, and partners for making our first MAMA San Francisco such a success. We can’t wait to do it again!

See you at the next MAMA! 

The post MAMA San Francisco: A bold start to 2025 for mobile marketers appeared first on AppsFlyer.

The integration of AI in App Store Optimization (ASO) marks a significant change, introducing advanced tools to make your app more discoverable and boost engagement. 

In this blog, we’ll explore how AI is redefining traditional ASO practices, from keyword optimization to understanding user behavior patterns. Beyond identifying the opportunities AI presents, we’ll also tackle the challenges and misconceptions that come with it, helping you prepare for both the potential rewards and pitfalls of using AI in ASO.

The role of AI in ASO

The integration of AI into ASO is transforming the way app developers and marketers optimize their apps for better visibility and engagement. By automating and improving tasks across various aspects of ASO, AI is setting a new standard for efficiency and effectiveness in in-app marketing strategies. Let’s take a look at how:

1. Providing data-driven insights

AI helps transform raw data into actionable insights. For ASO, this means analyzing performance metrics from across the app stores to identify what works and what doesn’t. AI algorithms can quickly process variations in user engagement and app performance, providing a granular analysis that helps in fine-tuning marketing strategies. 

Here are some key ways you can use AI to get data-driven marketing insights in ASO:

• Performance analysis: AI excels in identifying app performance metrics, from download figures and user retention rates to ratings and review sentiments. This analysis sheds light on an app’s strengths and weaknesses, guiding developers on where improvements are needed.

• User engagement insights: By evaluating how users interact with apps, AI identifies patterns that contribute to higher engagement levels. 

• Metadata optimization: AI-powered tools analyze the effectiveness of app titles, descriptions, keywords, and visuals, comparing them against top-performing apps in similar categories. 

• Competitive analysis: AI algorithms can monitor competitors’ performance, offering a clear perspective on market dynamics. Understanding what makes competing apps successful helps you craft strategies that capitalize on market gaps or leverage emerging trends.

2. Identifying high-value keywords

Using AI, developers can identify which keywords are likely to drive the most traffic to their app page. AI tools analyze search frequency, competitiveness, and relevance to suggest high-value keywords. 

When conducting app store keyword research, relying on general AI tools may not provide the specific insights you need due to their broad focus. Advanced AI-powered tools become invaluable here, providing more accurate and effective insights to developers.

For example, you can leverage AppTweak’s AI-generated keyword list to jumpstart your analysis with keywords tailored to your app or game, as well as for competitor apps. With AI-generated keyword clusters – groups of keywords that Apple or Google identifies as closely related to your input – you can also uncover new keyword opportunities that are potentially less competitive than their seed keywords.

3. Optimizing app/game marketing efforts with AI

Google Play’s integration of AI-powered features marks a significant advancement in ASO strategies for game developers. By displaying AI-generated FAQs directly within game listings, Google Play enhances user engagement and provides crucial information at a glance. This not only enriches the user experience but also helps in decision-making and boosts download rates.

Also, Google’s introduction of immersive in-game ads and exclusive offers for Play Pass subscribers and Play Points members demonstrates a nuanced understanding of how you can use AI to create targeted marketing opportunities.

How to incorporate AI in ASO: Practical examples

Integrating AI into ASO makes a lot of sense on paper – but what about the real-world benefits? 

AI tools in the ASO landscape are already making tangible waves, offering developers and marketers a suite of features to enhance their strategies. Let’s explore how.

1. Optimizing text metadata with AI

Traditional keyword research methods often miss the mark by not adapting to changing user interests or identifying emerging trends. 

AI is crucial in addressing the gap in keyword research for app developers and marketers. It revolutionizes how text metadata – like app titles, descriptions, and keyword fields – is created. By providing relevant and high-performing keywords, AI ensures apps are more visible and rank higher in app store searches. 

Sophisticated AI tools go beyond the standard ones like ChatGPT, enabling you to optimize your app metadata and ensure that every piece of content is precisely optimized for maximum impact. For example, with AppTweak’s homegrown Atlas AI, it’s easy to not only identify current popular keywords but also predict future trends. Content creators and marketers can then incorporate keywords that are poised to gain popularity, and ensure their content remains relevant and highly visible over time. 

AI tools like AppTweak can further recommend the inclusion of certain keywords in titles and meta descriptions, as well as provide tailored insights to accommodate regional and linguistic variations.

While AI can help identify and integrate keywords into your app’s metadata for better visibility, remember the human element. Your app’s title, description, and other metadata should not only include relevant keywords but also engage and appeal to potential users. 

2. AI in app store localization 

AI is reshaping the landscape of app store localization, offering developers and marketers innovative solutions to improve accuracy, speed up processes, and ensure cultural relevance. The integration of AI in localization strategies not only streamlines content adaptation across different languages, but also tailors this content to meet the specific cultural nuances of the target audience. 

Google’s foray into AI-powered translation is perhaps the most recognizable example. Google Translate uses Neural Machine Translation (NMT) technology to provide translations that are not just word-for-word, but contextually accurate and culturally appropriate. This represents a significant leap from traditional translation methods, ensuring that web content and apps resonate with users worldwide by maintaining the intended meaning and tone.

Mobile game localization with AI

Going beyond written translation, some AI models can generate new audio based on the translated text and even optimize visual elements in mobile games to align with the cultural contexts of the target audience. This includes modifying the appearance of characters and settings to be more culturally relevant, ensuring an immersive experience for players from different regions.

For example, the localization efforts for the RPG game The Witcher 3: Wild Hunt make the game feel originally developed for various markets, rather than merely adapted. The game developers used AI-driven tools to adapt the game’s vast narrative and dialogues, ensuring cultural relevance and linguistic accuracy across different languages. Given the role-playing genre of The Witcher 3, the scope of translation work, including voiceovers, was extensive. 

However, the role of human translators, particularly native translators, remains critical in localization. Despite AI’s advancements, it sometimes misses the mark on cultural nuances and may deliver less than precise translations. These inaccuracies highlight the need for human intervention to ensure that the game’s content is not only linguistically accurate, but also culturally appropriate and resonant with the target audience. The goal is to avoid any misinterpretations or cultural insensitivities that could negatively impact the user’s experience.

3. Using AI to refine app store creatives

Beyond text, AI can also help optimize the visual elements of your app store listing. AI revolutionizes this process by analyzing millions of images or videos quickly. It categorizes them by trends such as mood, color, or objects, streamlining the ideation phase. 

This not only speeds up content creation but also infuses your strategy with data-backed decisions, ensuring your content is more aligned with audience preferences and boosting engagement. It can fundamentally transform how developers approach app or game creatives to maximize user engagement and conversion rates. 

• Create app icons using AI: AI tools such as Midjourney simplify the creation of app icons. By submitting clear, specific prompts, you can easily edit current icons or start new designs. This helps you design an app icon that not only catches the eye but also matches your brand identity closely. 

• Analyze visual elements: AI algorithms equipped with image and video recognition capabilities can analyze the performance of visual elements in real-time, identifying patterns and trends that correlate with user engagement. For instance, AI can discern which colors, shapes, or themes in app icons and screenshots are more likely to catch the attention of potential users.  

• Automate A/B testing: Other AI tools revolutionize creative optimization in the gaming industry. For example, by automating A/B testing, PressPlay allows game developers, like Wildlife Studios, to efficiently test different creative options for their games. This means they can quickly find out which images, videos, or metadata work best to attract users. Using AI to automate the creation, deployment, and analysis of these tests saves a lot of time and effort, making the process much more data-driven. 

• Personalize content delivery: AI enables developers to personalize creatives displayed to users based on their preferences, behavior, and previous interactions with the app. This level of personalization ensures that users see visuals that are most appealing and relevant to them, increasing the likelihood of engagement and conversion. 

For example, the popular streaming app Netflix uses AI to personalize thumbnails shown to users. Using AI, the app analyzes individual viewing habits to determine which type of imagery a user is most likely to respond to, and then adjusts the thumbnails of movies and series to match these preferences. 

• Optimize gameplay and boost player engagement: The use of AI by Rovio shows how game developers can use machine learning to significantly improve visual elements and gameplay mechanics. AI’s ability to analyze and optimize game scenes in real-time gives players high-resolution output with improved detail and smoother textures. This not only elevates the gaming experience but makes it stand out in the competitive app stores, attracting more downloads and increasing user retention rates.

Understanding the limitations

While generative AI is an effective tool for content generation, it’s important to recognize its limits — namely, that it falls short when it comes to capturing the emotional depth and cultural nuances that human creators excel at. 

Creativity is all about connecting with people on an emotional level. If we lean too much on AI, we might lose that human touch that makes content resonate with audiences. For app developers and marketers, it’s wise to use generative AI for brainstorming and developing initial ideas. However, final content creation should integrate human insight to ensure it resonates on a deeper level, improving customer satisfaction.

Extracting value from user reviews

AI can also boost your ASO efforts by extracting valuable insights from user reviews. Through the analysis of word frequencies and semantics, AI can uncover key topics within reviews without human intervention. This helps identify and score specific topics found in user feedback, so you can systematically monitor their evolution over time.

Understanding sentiment analysis

Sentiment analysis leverages AI to categorize user feedback into distinct sentiments – positive, neutral, or negative – and further identifies specific themes or issues mentioned in the reviews. This provides a comprehensive overview of user satisfaction and areas requiring attention, which helps guide product development and marketing strategies.

Facing challenges with ChatGPT’s broad approach in extracting meaningful insights from user feedback, AppTweak’s data science team shifted to a more focused strategy. We adopted unsupervised machine learning techniques, aiming to identify key topics and their relevance within user reviews.

This method allowed us to identify word patterns and meanings within user reviews. We were able to find specific topics that frequently arise in user feedback, allowing developers to track how these topics change over time. 

Take Netflix, for example. After the streaming platform removed The Vampire Diaries series from its content library, we saw a visible increase in 1-star and 2-star reviews on the App Store. Our analysis highlighted the frequent mention of the series’ removal as a predominant factor driving user dissatisfaction.

Developers could now use these insights to take corrective actions – whether reintroducing popular content, adjusting content strategies, or communicating more effectively with users about content changes.

The bottom line

AI teaming up with ASO is more of a partnership than a takeover. It’s about combining AI’s power with human creativity and insight. This way, we get an ASO strategy that’s smart, efficient, and really connects with users.

The future of AI in ASO isn’t just crunching numbers – it’s about really getting to know what users want and creating experiences that hit the mark. Successful apps and games will be those that not only get discovered but also truly meet users’ needs and desires.

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