“Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.
Today’s column is written by Paul Longhenry, senior vice president of strategy, corporate and business development at Tapjoy.
For the last 20 years, advertising technology has fueled development of the free internet and mobile services on which we have all become hopelessly dependent.
What began with basic banner ads on the first content “portals” has experienced incessant innovation and now encompasses dozens of discrete services from thousands of competitive providers, all working to facilitate valuable connections between advertisers and their desired audiences. Ad tech is an advertiser-funded world, in which app and site publishers effectively sell access to their consumer base.
Marketing technology has an even longer history – IBM and Oracle have been selling customer databases for a very long time. Recently, however, mar tech has assumed a much more exciting role in this era of big data. Yesterday’s archived but underused data is today’s machine-learning input driving myriad customer engagement and product promotion output.
In historical practice, ad tech and mar tech clearly addressed different segments of the digital landscape and were distinguishable by business model. Those looking to attract audiences and package their collective attention for advertisers required ad tech services. Those that actually sell products and services to end customers required mar tech solutions.
Digital content publishers typically don’t sell anything to consumers, while product and service companies typically haven’t focused on attracting advertisers. But as mobile game developers started flocking to the freemium monetization model, this historical demarcation line between ad tech and mar tech has begun to blur.
Unique to the freemium model is the potential to balance monetization from both ads and transactions. Nonpaying freemium customers are very high in number – typically more than 95% of app consumers and accounting for 20% to 80% of total revenue, depending on app genre and design, if powered by a powerful ad tech service.
Paying freemium customers are small in number – typically less than 5% – but can deliver high value per customer based on the sophisticated sequencing of marketing messages, discounts and loyalty programs available via powerful mar tech stacks.
There are several trends driving the combination of ad tech and mar tech systems and their underlying data sets, including lookalike user acquisition and ad and engagement optimization. When examined in detail, these strategies highlight why consolidation of these tech sectors is destined to accelerate in the near term.
Lookalike User Acquisition, In All App Categories
Regardless of business model, all serious app developers invest great time and resources into their user acquisition strategies, typically using an ad tech stack to execute their desired campaigns. While look-back analysis of monetization delivered by channel, typically measured by ROI or ROAS, is standard practice, only recently have leading developers begun to leverage mar tech insights to inform more precise campaign targeting in the first place.
Data resident in mar tech systems can illustrate a given developer’s most valuable customer profiles and be invaluable in defining those audiences against which new user acquisition budget should be applied. The power of this trend is evident in the acquisition strategies of Adobe, Oracle, IBM, Salesforce and SAP, as they work to enhance the media-buying (ad tech) capabilities they are adding to their traditional mar tech systems.
Ad And Engagement Optimization, In Traditional Media Apps
While traditional media monetizes solely via advertiser spend, powered by ad tech, many mobile developers also use separate mar tech implementations that send push notifications and/or email announcements to drive increased consumer visits, resulting in greater ad impression opportunities.
The as-yet-untapped synergistic opportunity is to tie ad tech performance data into mar tech retention analysis tools. Once developers are able to factor ad placement, frequency and genre into retention and monetization analysis, they will be able to automate control of their ad tech stacks and optimize results for a variety of consumer profiles.
These examples of ad tech and mar tech synergy are the beginning of what will become mainstream best practice as participants of these two ecosystems continue to cooperate and consolidate. The app developers that choose to leverage these emerging advantages and then help drive further innovation will be the ones that deliver differentiated consumer experiences and become tomorrow’s market leaders.