AI and the next-generation of mobile marketing personalization

Personalization is now a baseline expectation for mobile app users. According to industry research, 71% of consumers expect tailored experiences, and 76% feel frustrated when they don’t receive them. Whether users are opening an app, viewing a mobile ad, or receiving a push notification, they expect speed, context, and relevance.

As touchpoints multiply, delivering consistent personalization has become more complex. Traditional segmentation methods, such as static cohorts or rules-based logic, lack the agility to adapt in real time to dynamic user behavior across channels.

Artificial intelligence (AI) helps marketers meet this challenge by automating and predicting aspects of targeting and optimization. While machine learning has long powered recommendations and audience segmentation, generative AI (GenAI) introduces new capabilities, enabling teams to personalize creatives, messaging, and onboarding flows more quickly. 

This article explores how mobile marketers can apply AI-powered personalization effectively, including what’s working today, how to get started, and what to prepare for next.

AI enhances four core areas of the mobile marketing stack. Each plays a distinct role in delivering personalized, measurable experiences.

Audience intelligence

Machine learning models analyze behavioral signals, such as in-app activity, session frequency, and customer relationship management (CRM) data, to predict outcomes like lifetime value (LTV), churn risk, and purchase intent. These forecasts enable marketers to allocate spend efficiently, prioritize high-value users, and tailor campaigns based on conversion likelihood.

Platforms like Meta and Google already use predictive and lookalike modeling to target high-performing segments. Many brands apply similar methods. Starbucks, for example, uses AI to recommend products based on time of day, purchase history, and weather, while also optimizing store-level inventory according to local demand.

In the examples below, Revolut and Monopoly Go! show how campaigns can speak to specific segments, such as pet owners or wellness enthusiasts. AI enables marketers to detect these kinds of affinities and deliver content that feels genuinely personalized, without relying on broad demographic assumptions.

The previous section outlined where AI fits into the mobile marketing stack. Now let’s look at how those capabilities come together in real-world scenarios: 

Personalizing ad creatives

Building on the DCO principles outlined earlier, user acquisition teams at gaming companies are using AI to tailor creatives to player preferences in real time. For instance, a casual puzzle player might see a colorful, lighthearted ad, while a strategy gamer receives a darker, more tactical version, each designed to reflect the user’s interests and gameplay style. This approach helps reduce ad fatigue, sustain engagement, and improve campaign performance by making ads that feel relevant from the first impression.

One notable example comes from SparkLabs, which produced ad creatives for the game Project Makeover. The campaign featured visual assets, like multiple stylized renditions of the Mona Lisa, produced using GenAI tools such as image-to-image generation. This enabled rapid experimentation that would have been much more time consuming to scale manually.

Personalizing across the user lifecycle

Apps that monetize through subscriptions can use AI to personalize engagement across the entire lifecycle, from onboarding and activation to long-term retention. For example, Spotify uses AI to deliver personalized push notifications and email campaigns based on listening behavior, time of day, and engagement trends. A user who skips multiple playlists may receive a tailored mix or podcast recommendation at their usual listening hour. Similarly, Netflix uses viewing history to personalize prompts, such as content reminders or new-release alerts, to re-engage users. 

Localizing onboarding with GenAI

Marketplace apps operating across diverse markets must localize content to meet user expectations. GenAI allows these apps to scale localization without duplicating production efforts, crafting onboarding flows and mobile messaging that adapt automatically to region, language, and device.

Zalando, one of Europe’s largest fashion marketplaces, uses GenAI to create region-specific onboarding content and promotional assets. Its Trend Spotter feature analyzes regional signals like cart behavior and search queries to tailor messaging by city. As a result, new users in Berlin, Madrid, or Paris receive onboarding experiences aligned with local trends and languages, without requiring separate manual workflows for each market. This approach improves activation and retention by making the app feel contextually relevant from the first session.

AI-powered personalization offers strong potential, but it also introduces real risks. Mobile marketers need to understand these challenges to use AI effectively.

Privacy and data consent

Data is the foundation of effective AI personalization, but how it’s collected, stored, and used is extremely sensitive. As privacy regulations tighten and platform changes limit tracking, marketers must prioritize transparency, user consent, and ethical practices. AI makes privacy even more essential, as personalization can only succeed when users trust their data is handled responsibly.

Overpersonalization and algorithm fatigue

Believe it or not, too much personalization can lead to repetitive, predictable, or even intrusive experiences. Users can actually disengage when they see only familiar content or if messages seem overly tailored. This is especially risky in discovery-heavy environments like social feeds or entertainment apps. To reduce fatigue, marketers should monitor user behavior for early signs of overexposure, vary content formats and messaging, and provide clear user controls.

Model bias and hallucinations

AI models learn from training data, and if that data is incomplete or biased, the outputs may misrepresent or exclude certain user groups. This is especially critical in targeting, content creation, and user segmentation.

GenAI introduces additional risk through hallucinations, which are outputs that sound plausible but are factually incorrect or off-brand. These errors can lead brand misrepresentation, poor targeting, and ultimately, broken user trust. To manage these risks, teams should audit model outputs regularly, apply inclusive design standards, and ensure human review of public-facing content.

You don’t need a new tech stack or a team of AI engineers to begin using AI personalization. In fact, you’re likely using AI tools already. What matters is how intentionally and efficiently you apply it. Here’s how to get started in a structured, scalable way.

Start small and run focused experiments

Begin with one or two high-impact areas where AI can clearly improve performance. For example, use a GenAI tool to create multiple ad or push notification variants, then A/B test them against your existing creative. Keep the scope tight: a well-designed test in a single channel is often more effective than a broad, unfocused rollout.

If the results show lift, expand. If not, assess what failed. Was it the prompt quality, the audience segment, or the timing? Use those learnings to refine and try again. An iterative, test-and-learn approach builds internal confidence and minimizes risk.

Focus on user value

Every AI-powered interaction should improve the user experience. You need to ask if this message or feature helps the user discover something they want or simplify their path forward? Transparency is also key. Let users know why they’re seeing certain content, and give them control—whether that’s muting a topic, adjusting preferences, or opting out of personalization altogether.

Collaborate across UA, CRM, and product

AI personalization isn’t owned by one team. Acquisition, CRM, and product must work from shared models, messaging, and data sources to avoid fragmentation. Some companies are now fine-tuning shared large language models (LLMs) to develop consistent content across channels, from ad copy to onboarding flows. Others align through shared taxonomies, user segments, or a unified experimentation framework. 

Even if the tools differ, cross-functional alignment on goals and guardrails ensures a coherent user experience and avoids siloed messaging that can confuse or frustrate users.

AI-powered personalization is evolving rapidly. Here are few trends mobile marketers should start preparing for:

• Emotionally intelligent AI:  Some AI systems are beginning to detect user sentiment from signals like drop-offs, skipped content, or time-of-day patterns. This enables more empathetic messaging (e.g., supportive nudges during inactivity) but requires thoughtful design, clear consent, and guardrails to prevent misinterpretation.
• Omnichannel and experiential personalization: Personalization is expanding to physical and hybrid environments. AI now helps coordinate messaging across mobile apps, web, connected TV (CTV), digital out-of-home (DOOH), and augmented reality (AR), creating more seamless and context-aware brand experiences.
• Branded foundation models and internal AI tools: More companies are developing proprietary AI models trained on their own customer data and brand guidelines. These “AI workbenches” help teams produce creative assets, run tests, and automate workflows faster with a consistent tone-of-voice.

 

While AI accelerates execution, it doesn’t replace human strategy. To gain a competitive edge, marketers must treat personalization as an evolving capability, measuring rigorously, iterating continuously, and adapting responsibly as standards and platforms change.

At Adjust, we’re building solutions to support this evolution. Growth Copilot, our AI assistant, helps marketers move faster by answering  plain-language business questions in real-time, providing actionable insights, eliminating reporting bottlenecks, and streamlining campaign and budget decisions.

Ready to simplify analytics and accelerate growth? Reach out to your Adjust representative or request a demo to learn more.