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Unlocking the power of predictive analytics: A guide for app marketers


The predictive analytics market is growing at a rapid rate of 23.2% year-over-year and is set to reach almost $11 billion in annual revenue this year globally. As app marketing becomes increasingly competitive, marketers are looking to predictive analytics to enhance their marketing strategies. In the guide, we’ll cover the question, “What is predictive analytics?” and dive into predictive marketing for apps as well as look at predictive analytics examples and models.

What is predictive analytics?

Predictive analytics definition

A type of data analytics that utilizes predictive models powered by artificial intelligence (AI) and machine learning by which marketers get estimates for specific outcomes.

Predictive marketing has risen with the evolution of AI, data mining, and data analytics. Marketers use predictive analytical models to see the potential impact of their actions. By feeding large amounts of data through machine learning algorithms, AI helps map layers of data trends marketers can use to predict users’ future behavior and make strategic decisions accordingly.


Why is predictive analytics important to app marketers?

Recent changes in the digital realm have forced advertisers to rethink predictive marketing. No longer is it a futuristic, nice-to-have tool but a necessity for tactical planning.

Below we cover several factors that dictate the current push for predictive marketing.

  • Competitive advantage

    In 2022, almost 84% of the global population has smartphones, up by roughly 35% from 2016. As smartphone penetration and digital demand increases, app marketers find themselves jockeying to capture users’ attention and buy-in. To stay ahead of the pack, marketers are turning to predictive analytics to understand consumer events, spot trends, and ultimately forecast future consumer behavior.
  • Shifts in privacy

    With the release of iOS 14.5, Apple’s AppTrackingTransparency (ATT) framework was implemented, meaning marketers must get users’ consent to deliver personalized ads. Without access to many users’ Identifier for Advertisers (IDFA), and Google’s Privacy Sandbox for Android not far behind Apple, marketers have had to rethink mobile attribution.
  • Improved predictive models

    Unsurprisingly, as artificial intelligence, machine learning, and data analytics make continual strides in sophistication and accuracy, predictive data analytics have also evolved. As a result, many mobile measurement partners, like Adjust, now offer app marketers predictive analytics models by which they can enhance their campaign and budget planning and optimization.


5 benefits of predictive marketing

At the end of the day, an app marketer’s success is tied to achieving KPIs like retention rate, monthly/daily active users, stickiness, and average revenue per user, to name a few. By leveraging predictive analytics, marketers can grow their app’s user base and bottom line to scale quickly.

1. Driving conversions
Predictive analytics can be used to uncover factors to change to improve your app’s conversion rate, such as creatives, channels, types of users, etc. For example, after determining your most engaged channel, you can then ascertain which creatives will likely drive the highest conversion within that particular channel.

2. Leveraging predictive LTV (pLTV)
A user’s lifetime value (LTV) estimates the total revenue they will generate during their user lifetime in an app. Now marketers can use machine learning and aggregated data of predictive marketing to understand how successful a campaign will be within days of its launch date and optimize accordingly.

At Adjust, we have a pLTV model (currently in beta testing with a number of clients) to help our clients understand the future value of a campaign early on, bypassing the SKAdNetwork waiting period so you can act quickly based on predicted KPIs and decisive insights. To learn more about how you can ensure your campaign delivers a high ROI, talk to us.

3. Growing user engagement
Depending on your app, user engagement can be defined by log-in, registration, in-app purchase, session length, or another type of in-app activity. With predictive analytics, you can review engagement across the user journey to forecast optimization opportunities in onboarding, messaging type, timing of communication, predicting user’s needs, and more.

4. Improving cross-selling and upselling
Using predictive analysis, app marketers can determine will be most likely to purchase another product or upgrade their subscription. Then, they create dynamic audience segments to target these users for cross-selling or upselling.

5. Reducing churn
Examing user action or inaction via predictive app marketing can help you understand the potential of users to go dormant based on their frequency and recency in-app as well as the monetary value of their in-app transactions. With these insights, you can create an effective strategy to reduce churn and improve your app’s retention rate.

What is predictive analysis?

Predictive analysis is the process of modeling data to make predictions about the future. In other words, predictive analytics is the result of a predictive analysis.

Marketers new to predictive analytics may also wonder, “What is the difference between AI and predictive analysis?” While both are interlinked when providing predictive analytics, it may be helpful to simply note that AI is an autonomous tool used in predictive analysis, and predictive analytics is a process that requires human interaction.

We’ll dive into the different types of models for predictive analytics in the next section.


Predictive analytics models

Here are five predictive analytics models to consider implementing in your app marketing.

While each predictive analytics model has its strengths and weaknesses, the algorithms for these models can be reused and tweaked to meet your specific marketing needs.

Therefore, we recommend using these models as springboards from which you can iterate and create new models based on your specific app for more relevant predictions.

Classification model

This model is one all marketers are familiar with as it utilizes the traditional method of reviewing historical data to make predictions about the future. Typically used to answer yes/no questions, the classification model can be used to answer questions like the following:

  • Is this user about to make an in-app purchase?
  • Is this user about to unsubscribe?

Now marketers should use machine learning and AI within a classification model to make predictions about users in real-time rather than in days or weeks.

Time series model

This predictive analytics model is useful for brands to identify and understand patterns over time. Often used to create revealing data visualizations, the time series model offers marketers insights into seasonality or cyclical behavior and can be used to predict potential changes in data.

Marketers tend to use time series models when previous trends might not influence future outcomes. For example, many marketers turned to this model during the uncertainty of the global pandemic when patterns were far from normal.

Cluster model

A cluster model sifts through data to create groups of users based on specific characteristics or attributes. For instance, marketers can set the parameters of cluster model algorithms to previous brand engagement, past purchases, or any opted-in user data.

Cluster models are particularly useful when marketers aren’t sure how to group many new incoming users as the model will utilize predictive analytics to cluster data with similar points.

Outliers model

This model identifies historically uncharacteristic data entries within a dataset. The outliers model can also be utilized to distinguish abnormal data as it relates to other categories or by itself. Verticals like e-commerce shopping and finance find this model is particularly useful in fraud detection.

Forecast model

An extension of the classification model, a forecast model is used to estimate the numeric value of new data based on historical data. Unlike the classification model, a forecast model can manage multiple parameters at a time, making it one of the most popular predictive analytical models. This model type can also generate numerical values even when none are present in historical data.


Predictive analytics examples

If you’re curious about how predictive data analytics will improve your business intelligence, review the following three examples to learn how predictive marketing can help delight your users.


Siri, Apple’s intelligent voice assistant app, relies on predictive analytics to cater to its users. The app’s recommendations are usually tailored to the user’s previous searches and activity. The result? In a recent survey, 81% of Siri users express satisfaction with the app assistant.


Streaming giant Netflix combs large amounts of user data and preferences such as search history, preferred genres, language, etc., to offer viewers personalized suggestions. Netflix viewing recommendations are so successful that the brand reports these recommendations account for over 80% of the content viewers stream.


Since 2016, Spotify users have looked forward to its Wrapped campaign, in which Spotify grants users a snapshot of their listening habits at the end of the year. In doing so, Spotify provides value to the user in exchange for their data.

Tip: Ask for the opt-in

It’s important to mention that in the three predictive analytics examples above, the brands have clearly demonstrated the value users’ will receive if they share their data with these companies. Check out The design do’s and don’ts for getting the user opt-in on iOS 14.5+ for tips on how to enhance your app’s user experience to secure an ideal opt-in rate.

While getting users’ opt-in will ensure granular detail about users, with recent innovations in predictive data analytics, it’s now possible to make excellent predictions with aggregated data.

In short: Don’t miss out on predictive marketing

With over 80% of major businesses using smart analytics, which includes predictive analytics, investing in creating a predictive analysis process for your app marketing is becoming a must.

Making more accurate predictions on your campaigns can increase your user base, user engagement, LTV, and, ultimately, your app’s overall ROI.

As a globally trusted mobile analytics platform, Adjust can help you track and optimize your campaigns as well as offer support in predictive analytics. If you’d like to talk to one of our experts about how we can help you grow your app, and how you can try Adjust for free, book your demo here.

Now that you’ve learned about predictive analytics, discover the other trends in the mobile app industry in our latest report: Mobile app trends 2023. Inside you’ll get insights from the top 2,500 apps across 45 countries, benchmarks for key retention metrics, and statistics on install growth, session trends, and user patterns.

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