Predictive marketing for mobile advertisers: How to do more with less
Wouldn’t it be great if your media partners would offer a “Get the results you want or your money back!” kind of deal?
Well, it certainly would, but that’s not going to happen…
Enter the next best thing – predictive marketing. By employing this practice you can get an accurate estimate of how your campaign will perform early on before wasting your budget on an underperforming campaign.
Savvy marketers have been leveraging data to build predictive models for a few years now, but most did not. However, that is changing with the increasing focus on the protection of personal information following Apple’s sweeping privacy policies in iOS 14+.
In this new reality, marketers struggle to effectively measure their campaigns because of limited data – both in granularity and duration. This makes predictive marketing a vital practice to adopt.
In this blog, we’ll dive into what predictive marketing is and how you can better leverage data to optimize campaigns in the privacy age.
What is predictive marketing?
Predictive marketing is the marketing approach that applies data science to forecast which marketing strategies are most likely to succeed. This involves utilizing predictive modeling and analytics to draw behavioral patterns to identify opportunities to optimize your campaigns, which effectively fills in the information gaps left wide open by privacy rules.
Don’t sweat it – we’ll explain what this all means later.
The evolution of predictive marketing
In the early days, data was utilized to paint a picture of basic user behavior to answer the question: “what happened?”
But as data became more available and granular, marketers began to draw conclusions of “why did this happen?” to better understand motivations and triggers that lead to certain actions.
Predictive marketing answers the golden question: “what will happen in the future?” which ultimately helps marketers make better data-driven decisions with the limited data they have on-hand.
Predictive marketing began with consulting firms composed of highly educated data scientists and experts who built out predictive models. It was a luxury only Fortune 500 companies could afford.
As demand grew, technology followed. More predictive analytics and modeling firms began to emerge. And naturally, predictive marketing became more accessible and will continue to be an integral part of any organization’s marketing strategy.
How does predictive marketing actually work?
Traditionally, data scientists use multiple predictive models to come to a conclusion on what hypothesis has the highest probability of being true. Apple’s App Tracking Transparency (ATT) framework requires users to opt-in to share their unique device identifier (IDFA) with advertisers, as opposed to an opt-out option in the past. This means marketers need to do more with less data.
In mobile app marketing in particular, predictive modeling helps marketers make more informed decisions in three main areas:
- Retention: measuring how long a user is expected to use the app
- Engagement: analyzing a user’s level of engagement with the app environment
- Monetization: scoring a user’s revenue-generating potential through ads, purchases, and more
For example, predictive marketing can help separate high-potential users from low-potential ones by detecting certain patterns of behavior. Advertisers can use these insights to turn the dials up on more profitable campaigns and vice versa. Advertisers can also leverage the data to identify under-performing and at-risk users to drive them back to your app.
Let’s say you’re advertising a puzzle game. Your machine learning algorithm learns that users who complete level 10 of your game within the first 24 hours are 70% more likely to make an in-app purchase.
If you’re running an eCommerce brand, your data may show that users who make a purchase in the ‘deals’ section of your app within 24 hours are more likely to make recurring purchases on a monthly basis.
Thus, predictive marketing enables you to target your most profitable audiences based on behavioral patterns that would be extremely difficult to otherwise determine manually.
How predictive modeling is done
- Data is collected, consolidated, cleaned, and analyzed to identify patterns in user behavior that cannot be otherwise found when done manually
- Predictive models based on your organization’s KPIs are built to segment your most profitable users
- The predictive models identify, based on performance, which campaigns are on track to succeed or fail, as well as optimize them to be successful.
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Why should you use predictive marketing?
The privacy-first world poses an even greater challenge for mobile advertisers, which is why the most successful mobile marketers effectively apply predictive analytics to make smarter data-driven marketing decisions. Here are a few more reasons why you should get started today.
No longer a luxury in an ATT world
In the privacy age, predictive marketing is no longer a luxury, but a necessity.
ATT has caused a sharp decrease in the data available to advertisers and the best way to effectively optimize your campaigns is by utilizing predictive marketing. You can leverage machine learning to identify patterns of behavior that are impossible to identify manually, and pinpoint your most profitable users.
Get ahead of your competitors
Proactive marketing approach ensures advertisers to identify problems much faster and improve user retention. It equips advertisers with foresight into expected performance slumps or peaks to make key decisions ahead of time – which your competitors may not have the capacity to do.
Trim budget waste and quickly optimize
Most advertisers invest heavily in data science and business intelligence systems targeting users with the highest lifetime value (LTV). Traditional BI analysis takes up to two weeks to gather data and identify meaningful patterns. And campaigns during this learning period can’t be changed in order to get clean and reliable data.
While the holy grail would be to measure user activity within 48 hours and accurately correlate that with long-term LTV, getting there can’t be achieved without access to large amounts of data. A substantial user behavior database can allow predictive modelling to identify patterns and cut waste quickly.
Two things to watch out for
I’m sure you’re eager to get started to implement predictive marketing into the mix, but there are two key factors you need to consider before starting.
Building a machine learning algorithm is still very complex
While predictive analytics has become more accessible, developing a machine learning algorithm is still a very complicated process. If you’re not utilizing a done-for-you service or a data partner, configuring data models requires data science expertise in your organization.
Accurate insights require large amounts of data
If you don’t have enough first-party data to work with and are looking to work with a data partner, ensure the data is trustworthy. Be sure to work with a vetted, high-quality firm that does not utilize data from other clients to build your model. The last thing you need is to have your data inform your competitors’ models.
Additionally, when cleaning your data, check for missing values or extremes and remove the outliers. Second, reference our predictive modeling guide to choose the right parameters to accurately shape your data models.
Tips to effectively utilize predictive marketing
If you’re currently utilizing predictive marketing, here are a few tips to ensure you’re getting the most out of your campaigns.
Identify your KPIs
The biggest pitfall for marketers using predictive marketing is being unclear with what defines success. For example, potential revenue is only one metric for success. Whether you measure potential engagement with your app or how often a user may use your app, identify clear measurement goals so you can optimize towards them.
This requires advertisers to take a bird’s eye view of the funnel. If an average user makes a purchase decision after a week, predictive marketing is not best utilized if you’re only measuring sales, because you want to make key campaign decisions early. Instead, you can identify what early funnel actions translate into revenue later on.
There are business KPIs and key in-app events that need to be measured and configured within your app to inform you whether or not your campaign is on track. If unsure, configure multiple events and allow the machine learning algorithm to identify your highest revenue predictors.
Scale with lookalike models
Once your data models begin reaching maturity, scale your program by creating lookalike analytics models to help you find more customers with similar behaviors and attributes like your current customers.
Key takeaways
- Predictive marketing is the application of data science models to predict which advertising campaigns are most likely to succeed
- In a privacy-focused advertising landscape, predictive marketing has shifted from being used by a few marketing experts to being a necessity for all advertisers
- By analyzing in-app behavior, predictive marketing identifies the most valuable users to target from a full-funnel perspective
- For mobile marketers, predictive marketing can help identify patterns of user behavior to help advertisers target the most profitable audiences quickly
- While predictive marketing as a function has become more accessible, building effective machine learning algorithms is still very complex and can be expensive if done in-house.
