Decoding AI for App Marketers: Understanding Generative, Predictive, and Prescriptive AI

For app marketers, Artificial Intelligence (AI) promises hyper-personalized campaigns and unprecedented efficiency. But beyond the buzzwords, a crucial question remains – how does AI move the needle in app marketing, and how can it be improved without giving in to the AI complexity?

AI in app marketing leverages intelligent tech to optimize user acquisition, ASO, personalized journeys, and automating campaigns for profitable app growth. And understanding how different AI types can support your app business is the first step to benefiting from AI.

Some excel at generating texts and graphics for app store listings, while others can do much more, such as predicting paid campaign outcomes and specifying exact actions that will improve your app’s performance.

This article will show you how different AI types can fuel app growth. For the last few years, we have been actively using these types to develop our own family of AI models at SplitMetrics’ R&D center. We use these models daily to help app marketers improve their KPIs like CPA and ROAS.

As the first article in the series, today, we decode four big AI types that apps should work with: Generative, Conversational, Predictive, and Prescriptive.

How AI shapes the market and what that means for your app

Before we continue with AI types, it is important to highlight that AI is shaping the app market just like many others and the data confirms it.

According to SensorTower, apps mentioning AI in their store listings accounted for 17 billion downloads in 2024, representing approximately 13% of all app downloads globally. 

The same report states that downloads for Generative AI apps surged globally, approaching 1.5 billion in 2024, a 92% increase compared to the previous year.

There are also apps such as Duolingo, whose CEO, Luis von Ahn, explicitly framed the company’s strategy shift around AI, comparing it to their earlier successful bet on mobile.

And the research findings from established sources show that AI is an integral part of everyday business, but not without challenges:

• 78% of organizations report using AI in at least one business function as of early 2025, according to McKinsey research
• 92% of companies plan to increase their AI investments over the next three years, as reported by the McKinsey Superagency report
• Nevertheless, more than half of organizations abandon their AI and Generative AI efforts due to cost-related missteps. Gartner estimates that less than 15% of organizations identify, quantify and measure costs, risks, and value.

The breadth of AI terminology (autonomous AI, deep learning, reinforcement learning, ANI, AGI, etc.) can also be paralyzing. It creates confusion about where to focus your attention and resources, especially if you are a mobile-first business.

Cutting through the terminological mess requires a focus on core functions. While definitions vary, the four AI types explored here offer a practical framework for app marketers.

1. Generative AI: The ASO & ad creative engine that helps you with content for store listings and ad campaigns
2. Conversational AI: The in-app support & onboarding bot that supports user-facing interactions within your app/website
3. Predictive AI: The LTV & ROAS forecaster that connects prediction to key app marketing KPIs
4. Prescriptive AI: The UA campaign optimizer that tells you which actions improve user acquisition

Let’s dive into what each of these does and, more importantly, how you can put them to work for your app.

Generative AI: Your content creation tool

If there’s one type of AI that has captured mainstream attention since 2022, it’s Generative AI. You’ve likely interacted with tools like ChatGPT, Perplexity, Midjourney, or DALL-E.   

At its core, Generative AI focuses on creating new content — text, images, code, audio, video — based on the vast amounts of data and patterns it has learned during its training. When given a prompt, it generates a response designed to match the request.

For many apps positioning themselves as “AI-driven,” Generative AI is the foundational technology to accelerate feature innovation and expand capabilities they deliver to their users.

For app marketers facing relentless demands for fresh content and creative assets, Generative AI offers compelling ways to boost efficiency and overcome common bottlenecks. Here’s how you can leverage it today:

• The ongoing challenge in ASO is drafting compelling app store descriptions. Instead of hours spent generating exact versions, Generative AI can rapidly produce a dozen variations, optimized for various personas using your app. Each variation can be tailored to different keyword strategies or audience segments, freeing the marketing team to focus on testing and refinement. 
• Instead of spending time on imagining and creating visual concepts, you can easily get ideas for concepts for A/B testing app store creatives or ad campaigns. You can quickly brainstorm different styles, layouts, and themes and speed up the production process for creatives.
• Producing first drafts for supporting content takes time, but Generative AI is like a motivated intern. It can produce initial drafts of blog posts, email campaigns, social media updates, or even video scripts related to your app, which your team then refines for brand voice and accuracy.
• Feed Generative AI seed keywords, app descriptions, or competitor information to expand your current keyword research process. It will generate related terms, semantic variations, long-tail keywords, and potential keyword categories to broaden your initial research pool for ASO and paid search.
• Replying to user reviews is work and time-intensive. Apply Generative AI to generate contextually relevant draft responses based on your brand tone of voice. Input the review text, and the AI can propose a reply, saving time while allowing for human oversight to ensure empathy and accuracy.

Understanding Generative AI’s limits

While powerful for scaling content creation, it’s vital to understand where Generative AI falls short. Because it operates based on learned patterns rather than genuine understanding or creativity, it often struggles with the following aspects:

• Brand voice and nuances matter a lot when working with Generative AI. Without guidance and examples, Generative AI can’t identify your brand tone of voice. With some guidance and effort, it will start creating content that matches your style.
• Crafting deeply resonant, emotionally compelling narratives or humor often remains beyond its capabilities. It can mimic styles, but lacks genuine feeling.  
• Generative AI can sometimes produce factually incorrect information (“hallucinate”) or generate content that needs human improvement. Verification is always necessary.
• If you use it to generate feature ideas, it will not be able to fix product-market fit issues—tasks like this are complex and challenging and require a deep understanding of the product, audience, and previous tests.

Conversational AI: Automating user interactions

Next up is Conversational AI, a specialist focused on streamlining communication flows.

This AI category powers the chatbots and virtual assistants you increasingly encounter. It specializes in automating interactions by understanding and responding to human language in real-time. For instance, companies like Intercom use Conversational AI in their customer support bots and interactive FAQs.  

Conversational AI can efficiently handle routine interactions, freeing up your team for more complex tasks:

• Basic support – chatbots in-app or on your website to instantly answer common questions (e.g., password resets, feature explanations), offloading volume from human agents.
• Feedback collection – conversational agents for structured user feedback sessions or simple in-app surveys.
• Guided onboarding – interactive AI guides within the app to walk new users through setup, offer contextual tips, and answer initial questions, improving the crucial first-time user experience.
• Lead engagement – chatbots on landing pages or within support sections to engage potential leads, ask qualifying questions, and direct high-intent users appropriately.

Limitations of Conversational AI

The role of Conversational AI in app marketing is limited — while it has its purpose, it can help you with the operating environment that supports your app but not much with user acquisition.

Conversational AI is generally great at structured, predictable interactions. But it often struggles with highly complex, novel, or emotionally charged support scenarios where human empathy and nuanced understanding are required. Fully automating tasks like review responses across different languages and cultures is still not possible.

Its performance is highly dependent on the quality of its training data and the design of its conversational flows.

Predictive AI: Forecasting your app’s future

Predictive AI offers a glimpse into the future. It understands data to identify patterns, gain insights, and forecast future outcomes.

This AI type analyzes historical data not merely to understand past performance, but to forecast future outcomes. It excels at identifying subtle patterns and correlations to anticipate future events, such as user behavior, churn likelihood, campaign performance, or lifetime value changes.

This is something that app businesses can expect to do already today:

• Predictive AI forecasts user LTV based on acquisition source and early behavior, which allows you to align acquisition spend with long-term app profitability instead of relying solely on historical CPA.
• A common problem apps have is bidding based solely on historical performance and missing opportunities or overspending when conditions change. Predictive AI can optimize ad bidding based on predicted conversion rates, ROAS, or other downstream events.
• Instead of facing uncertainty when planning new marketing initiatives, Predictive AI can forecast campaigns’ likely outcomes (e.g., installs, engagement, potential ROI) before launch. It analyzes planned inputs like audience, creative, budget, and relevant historical data to improve prioritization and resource allocation.
• Generic user flows often lower engagement, but Predictive AI could tackle this by enabling personalized in-app experiences, offers, and onboarding tailored to predicted user needs or intent.

Limitations: The challenge of predicting the unknown

Predictive AI doesn’t come without limitations. Its effectiveness relies on several factors:

• Historical data dependency – Predictions are primarily based on past patterns. Sudden, unprecedented market shifts, viral trends, or unforeseen external events (‘black swan’ events) can disrupt these patterns and reduce forecast accuracy.
• Model training latency – Predictive AI models require significant time to process and learn from large datasets before generating accurate forecasts, delaying initial deployment or adaptation to new data patterns.
• Data quality & completeness – the quality of predictions heavily depends on the input data’s quality, volume, and relevance. Incomplete or inaccurate data leads to unreliable forecasts.

Prescriptive AI: Guiding and automating optimal actions

Moving beyond understanding the past and forecasting the future (Predictive AI), we arrive at Prescriptive AI, which focuses on determining and often executing the best course of action. 

Prescriptive AI builds upon predictive models. It doesn’t just tell you what might happen; it analyzes potential actions and recommends or directly executes the optimal next steps needed to achieve specific goals.

It aims to answer the critical question: “Given the prediction, what should we do now?”

In the context of the app business, Prescriptive AI automates complex decision-making and aligns actions directly with performance goals.

There are also a couple of powerful use cases of Prescriptive AI for app businesses.

• Prescriptive AI replaces complex, reactive manual bidding by analyzing predictive models and market conditions to recommend or automate optimal, real-time bid adjustments. This ensures campaigns are proactively steered towards achieving your defined ROAS or CPA goals.
• It automates routine campaign management tasks like budget allocation and pacing based on performance against targets to eliminate the need for constant monitoring. For example, it can automatically pause underperforming elements or reallocate budget to high-performers according to your pre-set goals and rules.
• Effectively managing budget spend throughout a campaign without over- or underspending is challenging. Prescriptive AI addresses this by recommending or automating daily/weekly spend adjustments based on predicted performance and campaign goals, and targeting the best opportunities for return.

Limitations: The complexities of automated action

Just like other AI types, Prescriptive AI also comes with limitations:

• The effectiveness of prescriptive actions relies directly on the accuracy of the underlying predictive models – flawed forecasts lead to suboptimal actions. 
• Ambiguous or poorly defined objectives will result in ineffective or even detrimental actions. 
• You need high-quality, comprehensive, and often real-time data to make it function reliably. 
• Building or integrating systems that can predict, evaluate, and execute actions is technically challenging.
• Implementing automated AI actions requires establishing continuous human monitoring and clear intervention protocols. This oversight is crucial in volatile market conditions to protect against unintended negative consequences.

Why human expertise is key to AI-driven app growth

Understanding the individual types of AI is crucial, but the real leverage in app marketing comes from viewing AI not as a replacement, but as an incredibly powerful toolbox.

It’s about balancing these different capabilities within a human-led strategy.

AI, particularly Prescriptive and Predictive types, is a force multiplier. It can automate complex analyses, process data faster than humans, optimize campaigns 24/7, and handle repetitive tasks tirelessly. 

However, as our experience shows, AI can lack strategic intuition, deep brand understanding, ethical judgment, and the ability to make nuanced trade-offs based on long-term goals or volatile market context.

This is why your app marketing expertise remains non-negotiable. You guide the AI, set the strategic direction, interpret the outputs within the broader market context, and make the final critical decisions. 

At SplitMetrics, we’ve found the most effective model combines three core pillars: 

• best-in-class AI technology
• comprehensive data
• deep human expertise

Our proprietary Samba and the AI Models Family rely on the following methodology: 

1. The technology leverages sophisticated Predictive and Prescriptive AI models trained on vast mobile marketing datasets of 900M monthly data points to analyze performance and forecast outcomes.
2. These models are fueled by comprehensive data, including over 60 real-time market signals, meaning we adapt them dynamically to changing market conditions.  
3. The AI doesn’t operate in a vacuum. It functions within a constant human feedback loop, guided by our expert mobile growth strategists. They ensure alignment with broader business goals, interpret complex scenarios, and refine strategies based on insights the AI alone might miss.

The result is automated and continuously optimized campaigns designed to hit ambitious client goals like target ROAS or CPA.

Embrace AI, Drive Smarter Growth

As the line between automated intelligence and strategic execution continues to blur, the winners in app marketing will be those who master its orchestration with AI. The tools for execution are widely available and it is the right time to grab the opportunities.

To achieve those, start with applying the right AI – Generative for scaling content, Conversational for automating engagement, Predictive for informing strategy, and Prescriptive for optimizing actions towards your goals.