Beyond Prompts: Why App Marketers Needs Predictive and Prescriptive AI to Drive Growth

While most app marketers are still crafting ChatGPT prompts for ad copy or metadata development, a few are using AI to predict which keywords will drive the highest return three weeks from now.

For app marketers focused on hitting serious growth targets, relying only on generative AI is far from enough.

Why? Because generative AI falls short when dealing with your app data – from user acquisition funnels and campaign performance to in-app engagement and crucial post-install events like ROAS and LTV. 

This gap in capability is crucial as AI adoption accelerates. We at SplitMetrics believe that mobile apps will significantly increase their spend on AI tools and systems in the following years. So today we demonstrate how predictive and prescriptive AI can unlock the strategic power within your app’s data to move from simply using AI to winning with AI.

Beyond generative AI: from creative prompts to profitable predictions

If your job is to drive and scale your app business, you are most likely asking yourself critical business questions such as:

• Which app marketing channels drive the biggest value?
• How should I combine organic and paid user acquisition?
• Which keywords will drive the highest ROAS? 
• How should I adjust my bids in real-time to beat a competitor? 
• Which creative variant will convert high-LTV users in my target storefront?

Answering these questions requires an AI type that goes beyond general content production and taps into your most valuable asset: your proprietary app marketing data.

This is your unique, hard-won intelligence: the ad spend data, impressions, user funnel metrics from your MMP, and those crucial post-install events that tell the real story. Generative AI can’t analyze data on the level that mobile apps need if their goal is to get recommendations that directly drive revenue and impact profitability.

To build a true competitive advantage, you need AI that’s trained specifically on your business signals and data patterns. 

Unlock predictive power from your unique data mix

The core limitation of generative AI is that it cannot see the complex, multi-layered story your performance data tells. 

Purpose-built AI (the opposite of general-purpose built models like generative AI) delivers deeper insights.

Sophisticated AI that serves app marketing goals, on the other hand, should deal effectively with complex data for three critical reasons:

1. Your app data is a rich, heterogeneous mix. It includes app store traffic sources data, store listing intelligence, paid campaign data (spend, impressions), tracker data from your MMP provider (ROAS, CPA, LTV), and metrics at different levels of granularity — keyword, ad group, campaign, and storefront. A purpose-built AI can learn that a specific keyword, while having a higher CPA, consistently attracts users who make in-app purchases, making it more valuable in the long run. 
2. The app marketing landscape shifts by the hour – while organic rankings are generally more stable, paid campaign bids are very dynamic. Competitor bids and user preferences change by the hour and data velocity is high. Unlike generative models trained on static, months-old data, an effective AI type for mobile apps uses data from the last 24 hours to inform its recommendations. This means the AI can adjust bids and budgets daily or, when needed, multiple times throughout the day.
3. Every app campaign you run adds to your volume of historical performance data. You’re essentially training an AI on your exclusive slice of market reality — something no competitor can access. While your competitors use generic tools to brainstorm five new ad headlines, your AI is running thousands of micro-simulations based on your unique data to predict the spend required to hit your ROAS target.

Here’s how this works in practice for a typical ad campaign: 

• A purpose-built AI connects your live campaign data with your MMP data
• It might find that the keyword “epic puzzle adventure” has a higher-than-average CPA but predicts a 30% higher ROAS because users searching this term are more likely to make in-app purchases.
• Instead of suggesting you pause this seemingly expensive keyword, the AI will recommend a specific bid increase to capture more of this profitable audience, optimizing for long-term value, not just cheap installs.

Now let’s see how you can make this happen.

Predictive vs. prescriptive AI: your new strategic assets

If you can build a strong foundation with your proprietary app data, you unlock a new level of strategic capability. You can move beyond asking AI to create and start using it to calculate and command. 

This is the domain of predictive and prescriptive AI — two distinct but complementary models that transform your data into a clear roadmap for growth.

This predictive and prescriptive model works conceptually similar to how major ad platforms like Google and Meta work. They use vast amounts of user data to predict the best ad to serve in any given moment and then prescribe a certain action in their real-time auctions.

While the scale is different, this is the identical principle behind SplitMetrics Samba, which applies this data-driven decision-making to optimize the performance of specific app campaigns.

What is predictive AI in app marketing? 

Predictive AI in app marketing uses your historical and real-time app data to forecast future outcomes. It’s the engine that analyzes your unique blend of performance signals to make highly educated guesses about what’s next.

For an app marketer, it means developing specific, crucial forecasts:

• Forecasting the potential LTV or ROAS from users acquired through a specific keyword or campaign.
• Forecasting campaign metrics like spend, revenue, or the number of post-install events at the keyword level.
• Predicting the conversion rate of a new set of screenshots before you even push them live.

An example of this is the SplitMetrics AI Bid Simulator, which uses predictive AI to forecast how different bid amounts will impact key metrics like spend, conversion volume, and CPA. For app marketers, that means they can simulate the outcome of bid changes before committing their budget, turning complex data into a clear, actionable forecast.

With predictive AI, you’re not just reacting to market changes — you’re seeing them coming and positioning yourself to win before your competitors even know what hit them.

What is prescriptive AI in app marketing?

Prescriptive AI in app marketing takes the forecasts from predictive AI and recommends specific, optimized actions you should take to achieve your business goals. 

Think of it this way: predictive AI gives you a map of what might happen, while prescriptive AI plots the exact route you should take to get where you want to go.

For app marketers, this translates to actionable directives:

• Instead of just predicting spend, a prescriptive model provides daily bid and budget recommendations designed to hit your targets.
• Recommending you pause a low-performing keyword or move budget to a keyword that is predicted to have a surge in high-value traffic.
• Suggesting the ideal audience segments for a campaign to meet a specific CPA goal.

When combined, predictive and prescriptive AI work in tandem to continuously improve your campaigns. The prediction identifies a future opportunity (e.g., “this keyword will be highly profitable next week”), and the prescription provides the exact action to capture it (e.g., “increase the bid on this keyword by $0.15”). This approach turns complex data into straightforward actions that deliver real, measurable results.

App marketers can turn these actionable directives into reality using automated bid optimization strategies within SplitMetrics Acquire, as shown in the example below.

How predictive and prescriptive AI work together: an automated optimization loop

Your campaigns get smarter with each dollar spent when predictive and prescriptive AI models work together, creating a feedback loop that actually learns from itself. This process doesn’t just analyze data — it tells you exactly what to do next to boost ROI.

1. First, predictive AI forecasts what’s likely to happen. It analyzes your marketing data — from costs to post-install behavior — and projects results for different budget scenarios.
2. Next, prescriptive AI recommends exactly what you should do. It compares predictions against your ROAS or CPA targets and gives specific instructions — typically tweaks to your daily bids or budgets.
3. The results of the implemented action are fed back into the system as new data. This creates a powerful learning loop: the new information makes future predictions more accurate, which in turn leads to even smarter recommendations. It’s a cycle that continuously adapts and improves, driving progressively better campaign performance over time.

This automation works alongside experts rather than replacing them. It handles the grunt work while amplifying what marketers do best. App marketers still call the shots, bringing the strategic vision that no algorithm can replicate. They provide essential oversight, interpreting results and tweaking the strategy based on broader market trends or brand initiatives that fall outside the model’s data.

SplitMetrics Samba: putting predictive and prescriptive AI into practice

Predictive and prescriptive AI sounds impressive in theory, but it only matters when it actually solves real business problems. 

SplitMetrics Samba uses both AI models to constantly refine its approach through an ongoing cycle of analysis and adjustment. This system is designed to focus on your unique business targets, whether that’s hitting a specific CPA or maximizing ROAS, through a three-part process:

• At its foundation, the data layer pulls together insights from 60+ market signals — blending SplitMetrics’ proprietary data with external market intelligence to create a complete picture of the advertising landscape.
• The artificial & human intelligence engine then kicks in, using sophisticated AI models that predict campaign performance and suggest the most effective bid amounts for your specific goals. You’re still in control — expert marketers define what success looks like (whether that’s hitting specific CPA targets or ROAS goals) and can adjust course based on real performance data.
• With automated UA management, the system puts those optimized bids to work — running your campaigns without constant babysitting while consistently hitting your CPA targets or driving higher ROAS than manual management could achieve.

For you as an app marketer or business owner, this cuts out the massive expense and years of development it would take to build a similar AI system yourself. Skip hiring a data science team — plug your app into Samba and tap into models refined across millions of marketing data points.

From experimenting with AI to winning with AI

App marketers are moving past ChatGPT prompts to discovering what AI can actually do for their bottom line. Generative AI has its purpose and value, but using it alone won’t drive your next growth phase. The real winners will build custom AI systems around their unique customer data.

The path forward lies in leveraging predictive and prescriptive AI to turn your unique business data into a decisive competitive advantage.

It’s time to move from experimenting to executing. Start by auditing your data and defining a clear business objective for an AI system, such as a specific ROAS or CPA target. Then use this to build an AI strategy with clear revenue targets that actually moves your business forward.