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May 27, 2022

How Predictive Marketing will help you optimize your SKAdNetwork campaigns

Predictive marketing has been around for years. 

However, it’s only recently that it became a megatrend in the mobile marketing ecosystem. 

This sudden surge of predictive marketing came as a result of privacy changes that have completely altered the way we do mobile marketing. It all started when Apple rolled out its ATT framework and SKAdNetwork and left marketers with a lack of data.

Over a year into these updates, predictive marketing has proven itself as one of the best ways to navigate the privacy era. 

What will you learn from this article?

  1. What predictive marketing really is
  2. The difference between predictive marketing, predictive modeling, and predictive analytics
  3. Why predictive marketing is so important in a SKAdNetwork context
  4. The benefits of using predictive marketing with SKAdNetwork

And you’ll leave with actionable insights on how to use predictive marketing for your own campaigns.

Predictive Marketing: what is it?

Predictive marketing is a form of marketing, focused on predicting future events. Its main purpose is to give predictions on which marketing strategies are most likely to succeed in the future. 

These predictions don’t come from a crystal ball. 

Predictive marketing is driven by powerful machine learning and/or AI (artificial intelligence). In order to make predictions, predictive solutions utilize different data sources and predictive modeling. 

As a result of all this, marketers gain important campaign insights. 

Specifically, they use these insights to understand user behavior patterns and forecast future campaign performance. This allows them to optimize their campaigns and spend their marketing budgets more wisely – even with a lack of real-time data. 

When it comes to mobile app marketing, in particular,  a predictive strategy can be useful for more than just finding new users. Predictive marketing helps app marketers make the right decisions in these key areas: 

  • User acquisition
  • Engagement
  • Retention
  • Monetization 

Predictive marketing, predictive modeling, and predictive analytics

Many people think predictive marketing, modeling, and analytics are the same thing, but that’s not exactly true. 

Here’s a brief explanation. 

Predictive marketing is the broadest term among the three. This type of marketing utilizes both predictive modeling and predictive analytics. 

The term predictive modeling describes a statistical method used to predict the probability of a certain outcome. Predictive marketing solutions usually use multiple prediction models in order to make accurate predictions. 

Finally, predictive analytics can be defined as a process of applying predictive models to solve business challenges. 

With all of this in mind, let’s move away from definitions to application. 

SKAdNetwork and predictive marketing 

There is no talking about predictive marketing without mentioning what made it so popular in the first place – the limitations of SKAdNetwork (SKAN). 

Since the majority of app users don’t opt-in to ATT, marketers are doomed to rely on the limited, privacy-compliant data sets provided by Apple’s attribution solution – SKAN. 

The problem is that the data received from SKAN is anonymized and aggregated. It only covers the initial 24-hour time frame and comes with a delay. 

As a result, marketers are dealing with a lack of real-time data, granularity, and the loss of LTV and ROI insights. 

The main postback delivered by SKAN is Conversion Value. These data pieces are sent through a six-bit mechanism and can appear in 64 different combinations. Thanks to this, marketers have the possibility of measuring certain post-install activities.

However, relying on Conversion Values alone is not nearly enough to optimize campaigns properly. 

From the data provided by SKAN, it is only possible to find out how users interacted with the app right after the install. This leaves marketers with many questions about how the users used the app and how much revenue they brought to it. 

The process behind predictive mobile marketing 

Predictive marketing helps marketers fill in many voids caused by SKAN limitations. All while being completely privacy-compliant. 

The thing is, predictive marketing solutions don’t try to identify users (like, for example, fingerprinting).  Instead, they aim to recreate attribution windows and predict important campaign KPIs. 

In order to achieve this, predictive solutions need plenty of data. For this reason, they combine different data sets, including: 

  • Historical campaign data 
  • Historical user behavior data
  • Deterministic data from MMPs
  • UA parameters 

How is all this data used in the process? Here’s a rough description. 

Before anything else, machine learning processes historical data and maps it by when and how it occurred. 

The following phase is historical data understanding. This phase aims to identify the main factors that impacted campaign revenues. For this purpose, machine learning analyzes bids and budgets, traffic data, KPIs, etc. 

Upon analyzing all this data, it’s time for calculations. By using predictive modeling, predictive solutions can spot trends and seasonal patterns. This data is then complemented with MMPs data for even higher accuracy.

At the end of this complex process, marketers receive numerous campaign scenarios and predictions. 

The benefits of using predictive marketing within SKAN

As mentioned earlier, SKAN comes with many challenges and limitations. Predictive marketing isn’t a perfect technology either. 

However, combined, these two data sources form a powerful combination. 

Here are some of the main advantages of using predictive marketing within SKAN. 

Saving time 

App marketers constantly analyze user journeys and make conclusions about them – it’s in their job description.

They need to analyze things like how engaged the users will be, which events they prefer, and the amount of revenue they will generate. To find these things out, they usually rely on A/B testing. 

However, for efficient A/B testing, marketers need plenty of data, repetition, but also – a lot of ATT opt-in users. For all of these reasons, relying on this process alone is not the best choice in the privacy era.

Predictive marketing can be a lot of help with this. Most importantly, it can help save time & money spent on numerous A/B tests. 

After all, when it comes to predictions, it’s the AI that does all the work. This leaves app marketers with more time to focus on other important things. For example, finding new advertising markets, finding winning creatives, optimizing Conversion Value strategies, etc.

Less risk

There is no predictive marketing solution that is 100% accurate, but some of them can reach exceptional accuracy levels. 

For example, the average accuracy of Tempr.’s predictions is 85%. But to make it happen, the machines require a sufficient amount of data. The best predictive tools out there won’t even provide suggestions if they don’t have enough data to deliver precise enough results. 

Thanks to such high accuracy, predictive marketing can significantly reduce the risk of wasting ad budgets on non converting campaigns.

The ability to predict LTVs

Before SKAN, measuring and optimizing user LTVs was simple. Marketers knew exactly who their users were and how much they were worth to the app. 

With SKAN, marketers don’t know who their users are. Even worse, they have no idea what their long-term value is. 

This makes it practically impossible to make quick campaign optimizations. 

However, there is one predictive marketing metric that can help marketers with this – predictive LTV (pLTV). 

This metric doesn’t reveal the value of a particular user. With it, marketers can find out which pLTV cluster their users most likely fall into. This pLTV calculation to be efficient requires a lot of work on your conversion value model to make sure it’s the most representative of your user value. 

With this information, marketers are able to make quick optimizations in the campaign’s earliest days. 

Diverse KPI predictions

At the end of the day, all app marketers want to know if their investments will pay off. For this reason, ROAS (return on ad spend) is one of the most important app campaign KPIs.

And just like LTV, within SKAN, marketers cannot measure real-time ROAS. 

However, what can’t be measured can still be predicted. Predictive marketing allows marketers to predict ROAS on a granular level. 

Since it’s most important to analyze this metric in the first week of the users’ lifetime, predictive solutions usually predict ROAS for the first 7 days upon the install.

Another thing marketers are frequently focused on is making sure their users complete specific events. 

Naturally, they want to achieve this at the lowest possible cost. Predictive marketing solutions can help with this as well – by predicting CPE (cost per event). 

How can marketers apply predictive marketing to their campaigns 

What does predictive marketing mean for mobile marketers in practice? How does it fit into their everyday operations? 

Here are some of its basic applications. 

Identifying success metrics

Before starting with predictive marketing, marketers should have a clear idea of what their KPIs are. 

For instance, it is not enough to just aim for higher revenues or better retention rates. These goals should be measurable through clearly defined KPIs.

Those marketers who are unsure which KPIs to track for predictions should put them to a test. For example, when it comes to predicting profitability, marketers can test ROAS or LTV for different timeframes. 

The key thing to remember here is the purpose of predictive marketing in the SKAN context. 

Predictions shouldn’t and cannot be used for things like measuring sales. Instead, marketers should use them to identify which early actions users make are most likely to result in revenue later on. 

In other words, marketers should use predicted KPIs to make important campaign decisions early enough. 

Optimizing bids, budgets, and targeting 

Thanks to insights provided by prediction AI, marketers can optimize different aspects of their campaigns such as bids, campaign budgets, creatives, placements..

In practice, this is done in different ways. 

With some predictive solutions, marketers need to draw conclusions themselves. For example, they will receive predicted ROAS and CPE for their numerous campaigns. Based on this, they will need to detect the best ones and adjust bids and targeting. 

This can be overwhelming. 

To make things easier for them, marketers can also choose tools that don’t only provide them with insights, but also bring specific tips to leverage these insights.

 Combining predictions with A/B testing

As mentioned earlier, A/B testing can take a lot of time from app marketers. However, this marketing strategy is far from being dead. 

In fact, marketers can combine it with predictive marketing for faster results. 

In this combination, the role of predictions is to direct marketers to the options that are most likely to be successful. 

For example, predictions can show that certain ad creative concepts don’t have the potential to succeed, and should be discarded. 

Without predictions, marketers would give a chance to many concepts, test them all and waste time and money. However, with this information, they can simply A/B test those concepts that are most likely to succeed. 

Optimizing ad creatives

Predictive marketing may be fueled by numbers, but it affects more than just the numbers aspect of mobile marketing campaigns. Among other things, marketers can apply it to their creative optimization process. 

Here’s how. 

Predictive technology doesn’t only view ad creatives as a whole. Instead, it dissects them into different creative elements. Some of these elements include:

  • Ad type (e.g. image or video)
  • Ad dimensions
  • Colors
  • CTA
  • Ad placement
  • Ad copy

Each one of these elements can positively or negatively affect the performance of an ad creative. Machine learning has the power to recognize these effects and tie them to certain ad elements. This is done by collecting data from inactive ads and linking them to their performance results (e.g. CTR). 

Upon receiving these results, marketers can use them to optimize their active campaign creatives. 

This way, they can make sure that their audiences on different channels get ads that suit their preferences. For example, they might find out that their Facebook audience will react to an “install now” CTA better, while a simple “install” is more likely to work on TikTok. 

No matter how small these details may seem, predicting their performance can be very helpful in producing and picking out ad creatives in the privacy era. 

Ready to look into the future? 

Predictive marketing is the future, and every day, more and more marketers come to realize this. Make sure you’re not among the last ones!

Tempr.’s prediction technology will help you increase revenues by at least 10%. How? Our AI-powered prediction models simulate thousands of scenarios with different parameters. From all of them, we’re able to find the best ones and help you make important campaign decisions. 

Do you want to try out this magic right now? You can schedule a demo here.

Daria Jemli
Marketing and Communications at Tempr.

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