A guide to predictive analytics for app marketers

What is predictive analytics?

Predictive analytics is the practice of using historical data to estimate future outcomes. By identifying patterns in existing datasets, predictive models calculate the probability of a particular event or behavior occurring, allowing marketers to make informed decisions before results have fully materialized.

Unlike traditional analytics, which focuses on interpreting past performance, predictive analytics looks ahead. It doesn't attempt to predict the future with certainty. Instead, it estimates the most likely outcome based on the information available at the time.

For app marketers, these predictions can support decisions throughout the customer lifecycle. Rather than waiting weeks for campaigns to mature, teams can use predictive models to estimate lifetime value (LTV) much earlier and respond while there's still an opportunity to influence performance.

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.

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.

The quality of any prediction ultimately depends on the quality of the data behind it. Well-trained models, reliable measurement, and sufficient historical data all contribute to more accurate forecasts and more confident decision-making.

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 marketing 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

The value of predictive analytics lies in its ability to support better decisions before campaigns have fully matured. Rather than waiting for long-term performance data to accumulate, marketers can use predictive models to identify trends early and respond while there is still an opportunity to influence outcomes.

That capability has practical applications across the customer lifecycle and can improve both marketing performance and operational efficiency. Some of the most common benefits include:

1. 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.
2. 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.
3. 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.
4. 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.
5. 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

The value of predictive analytics extends beyond individual campaigns. Organizations that consistently make better-informed decisions are better equipped to deliver improvements to marketing KPIs.

Rather than reacting to performance after the fact, predictive analytics enables teams to take a more proactive approach to growth. Over time, that ability can compound into a meaningful competitive advantage, helping marketers invest with greater confidence and respond more quickly as new opportunities emerge.

Ericsson reports that by 2029, there will be 6.38 billion smartphone users in the world, along with 8.06 smartphones. 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.

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

Cross-channel measurement

Customer journeys have become increasingly fragmented. A user might first encounter an app through a social campaign, return after seeing a connected TV (CTV) ad, and complete a conversion following a branded search or retargeting ad. Understanding the contribution of each interaction is rarely straightforward.

Predictive analytics helps marketers interpret these journeys by applying consistent modelling across the data available. Rather than viewing each channel as a separate source of performance, predictive models can uncover patterns that emerge across the wider marketing ecosystem. This provides additional context when evaluating campaign results and helps reduce the influence of channel-specific reporting differences.

• 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 (UX) 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

Predictive analytics has become part of many of the digital experiences consumers interact with every day, often without realizing it. Streaming platforms use predictive models to recommend films, TV shows, and music based on listening or viewing habits. E-commerce brands surface products that align with browsing and purchasing behavior. Navigation apps estimate journey times by combining historical traffic patterns with live conditions, while financial institutions use predictive models to detect unusual account activity and reduce fraud.

The same principles apply in mobile marketing. Although these applications serve very different purposes, they all rely on the same principle: using historical data to estimate the most likely outcome and improve the experience for the end user.

Five of the most common predictive analytics models include:

• 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.

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 App Tracking Transparency (ATT) opt-ins on iOS 14.5+. 

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. Screens with pre-permission prompts can also go a long way in improving rates. Predictive analytics is as powerful as the data it has access to, and the more of that the better.

Read up on ATT and the positive opt-in rate trends we’re seeing and the design do’s and dont’s for getting the opt-in.

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 and AI solutions can transform your app marketing? Schedule a demo today.