Marketing teams worldwide are facing a new wave of challenges in user acquisition (UA) while navigating the new normal.
Between the rise of customer acquisition costs, Apple’s IDFA problems, Facebook’s deprecation of the AMM program, scalability and profitability concerns, and plenty of other restrictions and limitations, marketers are often left working in the dark.
In this article, I’ll talk about how marketers can use predictive UA to clear some of these hurdles. As the name implies, predictive UA puts predictability into performance marketing. In many ways, you can see it as a crystal ball for growth teams.
PREDICTIVE UA IS ABOUT THE LONG-TERM
UA campaigns that leverage lifetime value (LTV) predictions are all about thinking long-term instead of in seven-day conversion windows. In other words, you’re targeting an untapped audience outside of the attribution window. As a result, you can encounter less competition (giving you a bigger piece of the pie), higher profit margins, and lower cost per acquisition (CPA).
The purpose of LTV predictions is to identify a company’s ideal user from day zero so that you can home in on loyal subscribers.
Predictability and scalability are at the heart of predictive UA solutions. Marketing teams with strong internal data are good candidates for a strong predictive UA campaign.
There are several key metrics to pay attention to, including future lifetime value, conversion, churn, and loyalty likelihood. It goes without saying that the quality of the data is essential, as the information should be complete and in the correct format. The predictions can get even more accurate with the inclusion of ad network data. This will collectively help your team optimize all aspects of your brand’s user acquisition efforts.
It’s important to understand how the optimization element comes into play. First off is the ability to automatically send signals to ad networks. The second way it comes into play is through full predictability of your campaigns’ future performance and ROI. This enables your growth team to make strategic keep/kill decisions.
Your brand will benefit most from a solution that can analyze different data sets depending on their process and/or your needs. Depending on the solution you choose, the data sets the solution combs through can include transactional data, clickstream data, and ad network data. It might also go over previous campaign history and elements of zero-party data, including user profiles and information from registration and onboarding forms/surveys.
The more (complete) data, the merrier. It’s quite literally about both quality and quantity, because greater datasets will lead to more accurate predictions, both at the cohort and at the user level. This will give your brand the ammunition it needs to optimize UA campaigns with LTV-based predictions.
Throughout the course of 2022, UA teams should focus on stepping up their game for the post-pandemic world. Holistic user acquisition strategies should cover various audience segments, each with expected risks and rewards. Teams should also diversify and restructure strategies as business objectives shift.
WHICH COMPANIES SHOULD USE PREDICTIVE UA?
Large businesses that are heavily focused on customer loyalty can benefit most from what predictive UA has to offer. This especially applies to DTC subscription brands and brands that focus on returning customers. It also applies to casual gaming apps, subscription-based apps, and companies in the fintech space, to name a few. Brands with limited data lakes, such as most startups and mid-sized companies, can technically use predictive UA solutions, but I wouldn’t necessarily recommend it because as I said before, the more data, the merrier. The results that stem from limited data sets may not be optimal, so it’s better to wait until the internal data lakes are richer.
We are collectively continuing to navigate through the new year, as well as the new normal. As brands focus on bringing in greater returns, AI-driven solutions can help user acquisition teams optimize their strategies. On one hand, changing times may have led to complications in UA. But on the bright side, technology does exist to guide growth teams toward the path to success.
Ido Wiesenberg is the CEO of Voyantis, a UA platform that helps growth teams activate signals based on LTV data to optimize UA campaigns.