This article is part of a mini-series ahead of Mobile World Congress and the launch of our Modeling as a Service offer for DSPs. In the series, we'll be exploring the headwinds and opportunities for DSPs in a fast-evolving mobile performance advertising space.
The headlines read that mobile ad spend is sky rocketing, but for many DSPs that trend isn’t being realized in their revenue. Why? Because most of that growth (up nearly 25% YoY) is being captured by Facebook and Google.
According to AdAge, roughly 60% of all app ad spend will go to either Facebook or Google. The rest of the opportunity is largely shared among a high number of DSPs that are struggling to differentiate from one another, and more importantly, compete on performance with Facebook and Google.
That context has birthed new trends in programmatic creating momentum for private networks, trust exchanges and other instruments that offer advertisers control, but may or may not result in better performance.
While those trends may help DSPs differentiate from one another, the real challenge is embracing the new realities of the mobile ad space to improve performance. To do that, it’s important to understand why Google and Facebook currently have the edge.
How DSPs May Overcome the Data Disadvantage
What works for brand marketers doesn’t work for mobile performance marketers. With uncompromising KPIs and profitability goals, advertisers are less tolerant of a murky ROI. The challenge is compounded when you consider that mobile users have constantly evolving contexts, tastes, preferences and lifecycle stages that require profiling as near to real-time as possible.
The ability for Facebook and Google to synthesize unstructured data, search patterns and mobile app usage among practically ubiquitous audiences allows them to build rich and constantly evolving profiles on their users. As a result, targeting is more effective and advertisers get more bang for their buck.
Traditionally, DSPs are somewhat limited by the freshness of third party targeting data and the rate at which users found in targeting data can be matched to available bid inventory. In addition, targeting data from third parties sometimes uses context from only a limited pool of apps. In spite of investing in sophisticated bid and placement optimization algorithms, overall performance suffers because of the lack of dynamic lookalike models that capitalize on the wide range of data sources that can predict user intent.
So how can DSPs gain access to the right data?
The short answer is that they should take a page out of the Facebook and Google playbook and find ways to access larger scale data that stays fresh and keeps pace with behavior patterns. The data is available in app stores and on the web, it just needs to be unlocked.
That’s where Appnique comes in.
Over the past few years, Appnique has been refining our machine learning and AI technology to uncover powerful affinity signals and map them to relevant targeting parameters. In the past, we’ve mapped those affinities to correlated interests on Facebook with great success. Recently, we’ve made that data accessible to DSPs in a manner that can be integrated directly to the DSPs own proprietary technology.
This new capability gives DSPs the ability to access an exhaustive and always-evolving set of data and affinity signals to deliver rich, high performance audiences and take another step towards parity with Facebook and Google. Because our service integrates directly with your own proprietary technology, we help to further differentiate your offering at a time when the space is being perceived as increasingly commoditized.