Katie is a product manager at Adapt , a measurement and analytics suite that helps marketers build their apps across platforms .
In most cases, the result of forced and unexpected change is uncertainty and anxiety. This has certainly happened in the mobile marketing industry in recent years, as major players like Apple and Google have tightened controls on user data privacy, introduced stricter regulations, and questioned many of the tactics historically used by mobile marketers.
At the time of these events, few in the industry understood the challenges (and opportunities) that would arise from changing user privacy. New measures, such as the iOS 14.5 App Tracking Transparency (ATT) and SKAdNetwork (SKAN) platforms, as well as Google's privacy sandbox, significantly limit the attribution of data used by mobile marketers to measure and optimize marketing campaigns.
But there is also a positive side: the opportunity for marketers to explore new innovative directions. Innovative measurement techniques powered by machine learning (ML) have enabled mobile marketers to reach their audiences like never before.
Use incremental modeling and media mix simulation
Measurement and analytics tools have always been essential to mobile marketing campaigns, providing insights into performance, user behavior and ROI. These tools provide a framework for marketers to make data-driven decisions, optimize strategies and target the right audience for campaign success.
Historically, marketers have had access to detailed information about individual users, allowing them to deliver highly personalized ads. However, as mobile operating systems and advertising platforms limit the collection and sharing of this data without express consent from the user, marketers have been forced to look for alternative ways to measure the effectiveness of their campaigns. In particular, the constraint catalyzed the revival of two classic measurement methods: incremental and media mixture modeling (MMM).
Rather than focusing on direct attribution, cumulative measurement considers the cumulative effect of advertising on user behavior. With this knowledge, marketers can determine whether launching a new campaign will have a significant impact on the behavior of new and existing users. Growth creates opportunities to constantly test new marketing methods without putting too much of your budget at risk. It also corrects previous attribution errors by using statistical models to detect biological cannibalization.
MMM essentially analyzes how a marketing budget is spent and results, then uses that data to inform future marketing efforts. Like leverage, the MMM method is not a detailed metric, but it allows clients to be more strategic about how they allocate their marketing budget.
What's more, thanks to advances in machine learning, mobile measurement partners (MMPs) like Adapt have been able to create next-generation versions of these classic measurement techniques. The latest machine learning technology can now use aggregated data from campaigns to provide insights in a fraction of the time it takes. Importantly, machine learning can now be used to predict patterns in usage data and make recommendations to optimize campaigns accordingly.
Explore new directions
Connected TV
These new machine learning measurement techniques have revitalized channels that were previously considered relatively separate from a performance marketing perspective. Marketers can now look beyond mobile devices and use these channels to reach new users with the data they need to optimize campaigns.
For example, MMPs can now give marketers improved insight into the performance of connected television (CTV) campaigns by spreading the value of conversions generated by CTV ads across all marketing channels. This allows marketers to understand the value of CTV campaigns in the context of the entire marketing mix.
I believe CTV has become a new frontier in advertising, with incredible double-digit growth every year since 2017. In fact, advertising spending in the CTV market is expected to reach $26.92 billion by 2023, making it a growth-oriented channel that marketers should not be ignored.
outside the house
Similarly, out-of-home (OOH) campaigns such as billboard and transit advertising offer new opportunities for mobile marketers to expand user reach and potentially generate stronger results. The presence of OOH campaigns combined with mobile devices in daily life increases the advertising impact of this channel. Again, aggregated data analysis using machine learning allows for precise measurement consistent with other performance marketing channels.
Previously, marketers had to wait about six months to see how an outdoor campaign performed. With machine learning-enabled measurements, marketers can determine within weeks whether an ad is driving usage of their program.
PC and console
By 2022, there will be "1.1 billion PC gamers and 611 million console gamers," opening up additional revenue opportunities for mobile game marketers. With a user base of this size available on desktop and console channels (PCC), it's easy to see why mobile marketers are eager to expand their campaigns to these platforms.
The PCC advertising landscape is less saturated than mobile, allowing marketers to differentiate and reach users who are not used to seeing ads on this platform. Users of these platforms tend to be more engaged and receptive to interactive and engaging content, making them ideal audiences for creative advertising campaigns.
Progress of panic
The initial anxiety and panic caused by privacy change fuels what we at Adjust call “creative destruction”: the idea of unexpected change that forces companies to be more strategic and innovative. In fact, mobile marketing is turning industry panic into progress and enabling marketers to reach their audiences in new ways.
While mobile is one of the leading marketing channels, innovative measurement techniques powered by machine learning have opened up new advertising opportunities for today's growing marketers. This evolution from uncertainty to growth and stability shows an industry that not only resists change, but thrives on it.
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