Acquiring new users is not easy. User acquisition pushes users to the app store, to download and open your app. But this does not mean that you have won. Installing your app, does not mean that users are using it. Remember that to generate ROI from UA, you must have an audience composed by high quality users, who generate post-install events.
When Apple released its proposed change to the IDFA, millions of users have “opt-out” IDFA sharing, impacting advertisers’ ability to identify users of iOS14 operating systems. Apple’s Identifier for Advertisers helped mobile marketers to target, optimize, and attribute campaign performance to the individual. Mobile user acquisition activities do not exist without proper tracking and measurement.
For this reason, in User Acquisition strategies (process), one of the most important levers is machine learning. Machine learning based advertising is crucial now, as it makes sense for both companies and users. The former asks for results, the latter for privacy; machine learning satisfies both. Advertisers need to leverage technology to find meaningful insights, predict outcomes and maximise the efficiency of their investment, by choosing the right channels and budget.
Targeting requires to respects user privacy
Apple did not offer a solution that replaces precise targeting, leaving advertisers alone, searching other ways to show ads to relevant audiences. The majority of mobile user acquisition is done on programmatic exchanges, which work on a cost-per-mile (CPM) pricing model, so their ability of precise targeting is the most valuable benefit for marketers.
But if the advertisers are blind, they will spend money on random users who do not generate conversions. This deadlock is not good for anybody. Of course, the privacy shift was made to keep users’ best interests in mind. It is interesting to notice that on the one hand, users have concerns about the way data is collected and stored, and they end up facing a dilemma. On the other hand, they want personalised experiences. In other words they want the best of both worlds: benefiting from targeted ads and no identity exposure.
Focus on organic but remember that you need to scale
First of all, it must be said that you need to start working on organics way before your app is alive. When you set up your website, collect emails so you can keep your followers updated and let them know when your mobile app launches, and when you have new updates and features. You have to work on awareness. For example, by creating a pre-launch teaser landing page, and by starting a community on social media platforms. Keep in mind that organic installs matter because they often result in the most high-value users. There’s also an important business reason for driving organic installs: cost. Organic installs can save on CPI costs considerably.
Unfortunately, organic is never enough to scale very significantly. You will eventually need to advertise, whether programmatically or on different media, to really scale. You need a combination of elements. You “start” organic, and then you do paid acquisition. Even big companies like E-Commerce giants or social media platforms still do user acquisition on a large base.
Optimize your product for mainstream audiences
For optimizing your product, you need to test it, and for testing it you need to formulate hypotheses. First, you proceed to set off as many sources (e.g search campaigns, App store ads, owned media etc.) as possible, because one of your goals is to start to get clicks and installs. Pay close attention to your early adopters, since they might carry within biases, especially if you have a strong organic foundation. Early adopters’ behavior doesn’t necessarily represent your mainstream audience.
This practice could make running paid UA healthy: to prove your hypotheses and optimize the product towards the mainstream audience. If you have a small budget, focus on organic or different marketing actions. For example, referral traffic that occurs when a user finds you through a site other than a major search engine. Referral traffic can be a strong indicator of which external sources are most valuable in helping your business achieve its goals.
You have to cultivate your owned media
In this context, owned media are also essential. Your organization’s ability to control its own message, as well as the timing and delivery of said messages across multiple channels, helps owned media drive value. From the first day you launch your project, you need to establish and strengthen your owned audience, or earned audiences (through PR, featuring). Of course, it is a longer journey and you will start to see results later. You need to be patient.
Through owned media you can start building a community, or trying to gather data about your current users.
Machine Learning and predictiveness can really help marketers in the post iOS 14.5 era
With the implementation of SKADNetwork, the attribution model has shifted. The deterministic attribution has given way to the probabilistic one, which is completely anonymous and respectful of the user’s privacy. As a result of this, advertisers are getting less granular data, less accurate, and post backs are delayed by at least 24 hours after install, which makes optimizing campaigns in real-time very difficult.
In the iOS 14 reality, machine learning can give a complete view of how marketers’ efforts are performing, and help them to improve, wasting less time, which means, since time is money, spending less. Here at Mapendo, Jenga, our proprietary AI powered CPA optimizer, helps to understand trends, user behaviours and journeys in a way that is respectful to the user. Jenga collects tens of thousands of data related to a given topic, finds patterns, macro-trends and insights, and on the basis of the data gathered, it manages to predict the possible outcome of a marketing campaign and finds the audience that is most likely to convert. So the expected result is that you figure out your high value users, namely users who are going to drive profitability.
Conclusion
In conclusion, advertising has changed a lot, and it is mandatory to adapt to this change, or it will drag you down. The immediate challenge is how to create advertising campaigns that deliver great results, regardless of whether someone has opted in or out. Companies need to value the data that are provided directly from the final users, learn who these are and nourish them, also through their wholly-owned content, and find the right balance between organic and paid acquisition.
Companies must not consider user’s privacy as a problem, but as something that they must cope with, and that they can still make profits, by using different tools, like artificial intelligence. In fact, it will be more and more fundamental for companies to leverage this new technology and its corollaries. This will help them to gather fragmented data and analyse them efficiently, in order to build performant campaigns, and achieve satisfying results from a monetary point of view.
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