GUIDE

Leveraging predictive analytics for strategic growth

Crucial in the privacy-first era, add predictive analytics to your marketing approach to optimize campaigns based on accurate predictions of future outcomes.

As mobile marketing measurement continues to evolve, simple data analytics–transforming large volumes of data into actionable insights–is no longer enough. With the advent of the privacy era, the crux of which is mobile marketers gaining access to less user data rather than more, strategic use of next-gen technology is a must. Understanding campaign performance to the fullest extent possible is essential to continual optimization and scalable success.

Predictive analytics harnesses the power of artificial intelligence (AI) and machine learning (ML) to bring strategic data to marketers. Rather than simply presenting patterns and trends gleaned from the raw data it has access to, it uses these patterns and trends to present us with intelligent predictions of future outcomes. Such forecasting is key when it comes to working with aggregated data, something that we’re seeing more of with the introduction of privacy-centric frameworks such as Apple’s SKAdNetwork (SKAN) and its successor, AdAttributionKit.

The importance of predictive analytics and the forecasting capabilities it provides is reflected in its sustained growth in the market. Its revenue forecast for 2025 is $23.9 billion, representing a growth rate of 23.2% since 2019. The competitive nature of the app market makes predictive analytics an essential tool in a marketer’s arsenal. In this guide, we’ll deep dive into what predictive analytics is, its strategic importance in today’s app marketing landscape, some real-world examples, and how Adjust can empower you to make predictive analytics work for you.

What is predictive analytics?

At its most basic, predictive analytics is the process of using historical data to accurately forecast future outcomes. It is known as a “data-driven” approach because this historical data wholly determines the outputs. In mobile marketing, predictive analytics is most often one of a number of tools using data to optimize marketing campaigns, seeking better results and minimizing wasted marketing spend.

Predictive analytics and incrementality

Another data-driven solution in the mobile marketing space is incrementality, a next-generation approach that also predicts outcomes, but does so by presenting the difference between the outcomes of changed and unchanged marketing activities (you could think of this as making predictions on the basis of a hypothetical past).

For example, incrementality will tell you what would have happened if you had not taken a specific marketing action. Say you added $1,000 more budget to a campaign–an incrementality solution will predict the results you would have seen had you not increased your budget in the first place. With this information, you can determine if taking the action was more financially viable than not taking the action.

Adjust’s recent launch in the incrementality space is our InSight solution, using AI and machine learning to uncover the true incremental value of marketing actions, maximizing efficiency and accuracy.

Explore some real-world incrementality use cases.

Predictive analytics, also known as predictive modeling, is powered by AI and machine learning. Using computational statistics to create forecasts, it enlightens marketers in a number of ways, for example predicting future marketing trends, advertising outcomes, and user behavior. Marketers can make all kinds of tweaks and changes as a result of analyzing these predictions, such as allocating budget in a more efficient way, and rethinking advertising creatives.

Marketer risk is mitigated by predictive analytics because it makes it possible to see the potential impact of campaign changes without actually implementing these changes, therefore giving marketers the opportunity to avoid actions that might impact negatively.

How does predictive analytics work?

The core of the predictive analytics process is the machine learning algorithms that digest the vast swathes of historical data provided and, with the use of AI, present marketers with trends that can be used to predict future patterns and behaviors.

Machine learning and AI work autonomously, meaning that the burden for a marketer of running predictive modeling is at a minimum.

Strategy and benefits

The strategic importance of predictive analytics

All marketing strategies are driven and measured by the use of key performance indicators (KPIs). Predictive analytics can not only help marketers to set these KPIs, keep them within realistic parameters, and precisely measure performance against them–its true value is in supporting marketing teams to meet and exceed them. The clear picture painted by predictive analytics of how marketing performance can be improved leads directly to increased return on investment (ROI).

Spotlight on gaming

Across every industry, predictive analytics involves analyzing and predicting user behavior. When it comes to mobile gaming, player behavior (for example, likeliness of churning, likeliness of in-app purchasing) is key, with a focus on player engagement and retention. Pattern analysis proves very reliable when it comes to gaming predictions, offering marketers the valuable insights that form the basis of targeted marketing strategies.

Read more about mobile gaming analytics.

As we’ve mentioned, predictive analytics doesn’t usually stand on its own as a single data-driven solution. More often than not, it’s just one element in a marketer’s next-generation tech arsenal, alongside solutions like incrementality (see above) and media mix modeling (MMM). A multi-solution tech stack proves key for today’s mobile marketers working against the constraints of aggregated data. More on that later!

A varied tech stack provides a 360-degree view of marketing efforts and campaign performance. Multiple-campaign management and mobile marketing measurement can seem complicated and daunting, not to mention the sizable budgets at stake. Bringing clarity and reliable insights to the situation yields positive outcomes for marketers who have a clear view of the areas where campaigns should be scaled up and scaled back. Rather than just a nice-to-have, this transparency is the future of mobile marketing measurement and the key to achieving sustained growth as the landscape continues to evolve.

The key benefits

Predictive analytics offers five key benefits to marketers:

Targeting efforts can be enhanced: The more intelligence you hold on a user’s past behaviors and predictive future behaviors, the more strategically you can segment and target specific user groups, presenting them with the ideal advertising (e.g. a discount coupon to encourage continued engagement) at the right time. By segmenting your audience and personalizing the messaging you use, you’ll increase the levels of positive interaction.
User acquisition (UA) rates increase: Following this same principle, if you’ve targeted the optimum user base with messaging that’s most likely to have traction, you’ll see improved user acquisition and conversion rates. This has a positive knock-on effect on revenue and overall profitability. Predictive analytics can also provide insights into user lifetime value (LTV), meaning you can measure and serve the users that offer the most potential value to your business.
Users are more engaged with your app: Once you’ve marketed your app to the ideal user base, it figures that engagement rate increases. Engaged users are more likely to make in-app purchases (IAPs) and spend more time within your app environment. Predictive analytics enables you to more accurately measure how your app is being used (across the whole user journey) and pinpoint optimization opportunities by predicting user needs.
Marketing budget spend is optimized: Leveraging predictive analytics benefits your marketing budget in two main ways. As we’ve mentioned, machine learning and AI work autonomously–so your marketing team resources can focus on other strategic tasks. By targeting a more defined group of users, you’re not committing spend to users unlikely to be receptive to your marketing.
Churn is measured and reduced: Determining when a user might begin to appear inactive (or go “dormant”) is no easy feat. Predictive analytics uses details like frequency of app use and recency of app sessions to produce informed forecasting of potential churn. This allows you to deliver messaging at the right time and with the right focus, aiming to retain users, thereby keeping your app’s retention rate healthy.

A catalyst for competitive advantage

With the above benefits in mind, it’s clear to see how predictive analytics can deliver improvements to marketing KPIs. Looking at things with a wider lens, optimized KPIs lead the way to sustained growth. Leveraging any technology that impacts growth in this way spells out a boost to competitive advantage and market share within your app’s industry or vertical.

In 2024, Ericsson reports that there are 4.88 billion smartphone users in the world, with this number forecast to rise to over 6 billion by 2027. Large user bases mean more and more apps competing for users’ attention and time, so any step to significantly boost competitive advantage is a game changer.

With AI and machine learning techniques constantly developing, maturing, and offering even more accuracy, predictive analytics is only set to become more and more integral to mobile marketing strategy.

Cross-channel measurement

Cross-channel measurement is the process of measuring the marketing performance of a multi-channel campaign, gaining insights into how each individual channel is faring and having the ability to compare, contrast, and optimize.

Predictive analytics brings accuracy to cross-channel measurement, a field that offered up disparate and disjointed data sets in the past. Predictive analytics uses its algorithms to create forecasts pertaining to all channels and presents an unbiased picture of user behavior that may differ from any assumptions a marketing team may have held. When it comes to optimizing and scaling marketing campaigns, allocating budget carefully, and seeking the best ROI, these insights are key.

“Mobile to CTV, PC and console and back to web—regardless of what new channels might become popular over the next few years or new privacy regulations that might be introduced, marketers need to get the best return on investment. So the question advertisers are asking is, “How do we know where we should spend?” In the past, most of the focus on measurement was aggregating data from a few sources and comparing the results to see how they performed to know where to invest in the future.

But machine learning and AI are giving marketers the ability to predict, with 90% accuracy, a user's lifetime value at days 3, 7, 14, and 30. Within the first 24 hours, marketers will be able to identify if a campaign is attracting high-value users so they can invest more or stop the campaign in favor of other channels. This, in turn, will lead to well-spent budgets and higher ROAS.

Predictive analytics and automated workflows, combined with incrementality and media mix modeling (MMM), will give marketers a 360-degree understanding of how and where they should be spending their marketing dollars. This is the future of modern growth marketing and measurement.”

Simon “Bobby” Dussart

CEO, Adjust

Real-world examples

Making predictive analytics work in practice

Appreciating the theory and power of predictive analytics is one thing, but how do app marketers and app developers implement it, apply it to real-world situations, and truly stand to gain from its vast promise?

When determining where predictive analytics might offer the greatest rewards, consider any part of your marketing setup that produces data. Where data is generated, predictive analytics can transform it.

Here are a few use cases to keep in mind:

Marketing automation: Predictive analytics significantly lessens the burden of time- and resource-heavy marketing tasks. As well as anticipating the behaviors and preferences of specific segments of users, the autonomy of AI and machine learning can be leveraged to act on these in a very precise manner, for example by displaying a particular ad at a particular time. Adjust’s Campaign Automation tool does just that–more on this later.

Audience segmentation: As we’ve covered, the critical first step of audience segmentation can be automated using predictive analytics. This approach gives us the ability to segment by predicted attributes as well as proven attributes, for example the predicted cost per action (CPA) of a user.
Retargeting: Efforts to entice inactive users back to a brand are known as retargeting. Again, predictive analytics can identify this pool of users and automatically target them with the right messaging at the right time.
Channel analysis: As we’ve said, taking control of the cross-channel mobile marketing landscape is greatly strengthened by the ability to predict which channels will prove most successful for a particular campaign and therefore offer most value to your business.
User retention: The process of using predictive analytics to anticipate and prevent churn is central to a modern mobile marketing retention strategy.
Income forecasting: Apps that monetize certain aspects of the user experience (for example, through IAPs) thrive on being able to forecast this monetization and accurately report forecast figures ahead of time. Where physical goods are concerned, this accurate forecasting also benefits inventory management.
Optimizing user experience: Insights gained from predictive analytics can pinpoint particular parts of the user experience that present stumbling blocks. For example, perhaps your app’s onboarding process is cumbersome and the predictions show that it will cause many users to become inactive–now that you’re armed with this knowledge, app developers will have the opportunity to make positive changes.
App performance monitoring: Another area of keen interest to app developers is monitoring how an app is performing, for example in terms of load times and server resources. Being able to view performance forecasts leads to timely fixes, stable peak usage times, and optimized ongoing maintenance.

Predictive analytics in everyday life

Some of the most used apps and sites in the world rely on predictive analytics to retain their huge market share. Siri, Apple’s voice assistant app, presents informed responses to user questions by analyzing the previous activity of that specific user or similar users. Similarly, streaming leader Netflix proactively offers viewers their next shows by analyzing what they have spent time watching in the past. This recommendation feature is key to the retention of Netflix’s users in the long term, as well as increased time spent per session. Audio giant Spotify keeps users listening by employing a similar “you might like” recommendation technique.

Best practices and optimization

Leveraging the power of predictive analytics

Appreciating the benefits and strategic importance of predictive analytics, as well as understanding its real-world application, are key first steps to leveraging it as a powerful piece of next-generation marketing tech.

Implementing predictive analytics in the ways that will most benefit your app’s marketing involves choosing the predictive analytics model that best suits your needs.

Choosing the right predictive analytics model

Predictive analytics models vary in how they handle data to provide most efficiency and value. It’s important to remember that all models can be customized to precisely fit your specific use cases and mobile marketing needs. Iteration and testing are key to implementing the ideal model.

Five popular predictive analytics models are:

Classification model: This model typically uses yes/no questions to make predictions of future outcomes based on historical data, e.g. will this user make an in-app purchase? Real-time information like this enables marketers to act, whether through an automated process or manual intervention.
Time series model: Very useful for understanding behavioral patterns over time, the time series model produces data visualizations that give insights into seasonal or cyclical patterns that can be used by savvy marketers to predict future behavior.
Cluster model: This model “clusters” groups of users based on shared attributes. Marketers are in control of the parameters that the model is working within to group users in this way (for example, users who have made purchases in the past) and can treat each cluster as an individual cohort when it comes to strategically marketing.
Outliers model: This model identifies the “outliers” in a dataset, i.e. those that appear uncharacteristic when viewed in the context of historical data. Marketers find this model particularly useful as a method of identifying and combating fraud.
Forecast model: In a similar vein to the classification model, the forecast model uses historical data to predict the numerical value of new data, even when no numerical values exist within the historical data. This model can manage multiple parameters at once, making it more complex than the classification model and a definite favorite among marketers.

Privacy-preserving measurement

Predictive analytics in the privacy era

The last few years have seen predictive analytics become yet more valuable to app marketers, in the context of Apple’s App Tracking Transparency (ATT) framework and Google’s upcoming Privacy Sandbox on Android, alongside numerous global data privacy standards such as Europe’s GDPR and California’s CCPA. These seismic shifts have led to marketers having to rethink mobile attribution and personalized advertising completely.

What is the privacy era?

The prioritization of user privacy by big tech is a result of the privacy-preserving measures brought in by global legislative bodies. It represents major disruption in the mobile measurement world, and has led to the coining of the phrases the “privacy era” and “privacy-first era”.

In this era, marketers must commit to next-generation technologies—alongside traditional mobile attribution—to continue to optimize campaigns and scale at speed. Future-proofing in this way will mitigate the risks that are posed by a traditional mobile measurement setup that has access to data at an aggregated level only.

iOS 14.5+ and Apple’s post-IDFA framework

Apple’s introduction of SKAdNetwork (SKAN) as part of iOS 14.5+ and more recent launch of AdAttributionKit mean we no longer have access to IDFA data for iOS users who don’t opt in to share this data. The privacy-centric nature of these frameworks means that our access to campaign activity data is limited to anonymized data.

In a scenario like this, predictive analytics is the ideal (and perhaps the only reliable) method of predicting markers like LTV and using this key information to optimize campaigns. Evaluating LTV in relation to CAC remains key in evaluating campaign effectiveness and making data-driven improvements. SKAN 4 and AdAttributionKit’s measurement windows 2 and 3 provide some ability to gain insight into user LTV, but this can only truly be unlocked with next-gen analytics and predictive analysis.

Read more about AdAttributionKit and SKAN, and how Adjust is overcoming the unique challenges they presented to marketers.

Securing the opt-in

With all of this said, and while adopting a post-IDFA mindset and embracing aggregated data analysis is prudent for today’s marketers, traditional attribution methods via IDFA are still very much in play and work in tandem with next-generation approaches. There is still a lot to be said for putting the groundwork in place to secure Apple’s ATT opt-ins on iOS 14.5+, and preparing to do the same for Android, with Google’s Privacy Sandbox on Android just around the corner.

These opt-ins will likely soon be the exclusive way to access first-party data directly from users. Presenting an opt-in selection to a user in the right way at the right time could be the difference between having access to consented first-party data and not having access. Predictive analytics is as powerful as the data it has access to, and the more of that the better.

Read up on ATT a couple of years on, and the positive opt-in rate trends we’re seeing.

Adjust and predictive analytics

Leading the way in next-gen mobile measurement

With the ongoing evolution of AI and machine learning, predictive analytics is central to campaign optimization and budgeting. Adjust’s predictive models are custom built for each app, based on that app’s SDK data.

Predictive modeling is just one of a number of next-gen technologies and methods Adjust has leveraged to give marketers valuable predictions of long-term outcomes in an increasingly privacy-driven mobile marketing landscape.

Adjust’s campaign automation solutions leverage predictive analytics to adjust budgets and optimize ad bids, streamlining campaigns while freeing up marketing resources to focus on strategic objectives. Using machine learning algorithms, our predictive model transforms large volumes of SDK data into digestible and actionable insights.

Adjust’s Audience Builder tool also uses predictive analytics to segment user bases and refine user targeting, while the raw data we provide can be consumed by any number of predictive models to generate data-driven insights.

Adjust’s pLTV (predictive LTV) measurement forecasts user-specific LTV without the need for IDFAs, which is ideal in the iOS 14.5+ privacy era. Most effective when considered early in a campaign’s lifecycle, pLTV acts as a guiding measure for marketers looking to optimize campaigns and boost revenue.

And we’re not finished yet! Adjust’s commitment to market-leading innovation is evidenced by our continual development of measurement solutions that transform app marketing. We are currently far along in the process of developing an additional next-generation predictions tool that will use real-time user behavior to generate revenue predictions within the first 24 hours. This will significantly cut down the wait time for cohorted metrics to mature, allowing marketers to invest wisely at an early stage.

Used in conjunction with InSight, our incrementality solution that also predicts outcomes, but does so in the context of a hypothetical past, AI and machine learning are working together to build a complete picture of the true value of marketing actions, maximizing efficiency and accuracy.

Make predictive analytics work for you

By committing to the next-generation solutions that empower modern app marketing and following the tips in this guide to configure a predictive analytics solution most suited to your needs, you can realize the competitive advantage to grow your app at pace.

An investment in predictive analytics is an investment in your app’s future success, while failure to adopt a predictions-driven approach limits your ability to optimize campaigns and realize your long-term attribution goals. With improvements to attribution, user engagement, LTV, and ROI at stake, getting predictive modeling right is becoming a strategic must.

Ready to explore how Adjust’s next-generation solutions can transform your app marketing? Schedule a demo today.

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