Dispelling the myths of mobile advertising on the open internet

However, as advertisers and agencies venture into mobile in-app advertising on the open internet, common misconceptions may hinder performance. By identifying and dispelling these myths, agencies and advertisers can ensure they use the most effective strategies to achieve optimal results for their apps.

Myth: The open internet is uncharted territory that makes mobile advertising harder

The open internet is a vast and decentralized landscape with billions of devices, millions of apps and a myriad of supply and demand platforms. However, many advertisers are drawn to the familiarity of walled gardens, believing they provide sufficient reach for all customers, often leaving opportunities for growth on the table.

“Walled gardens have less financial incentive to prioritize impressions on the open internet,” said Dario Sheikh, head of agency at Moloco. “Because they must compensate publishers for ad placements, they often find it more profitable to focus on owned inventory. Given this, solely focusing on walled gardens significantly limits reach to new, undiscovered audiences across the open internet.”

While the open internet may seem like uncharted territory, it presents rich opportunities for innovation and growth. The key difference lies in the complexity and vastness of the space, which requires a more nuanced, granular approach to target and acquire new users effectively. 

Marketing teams can maximize ROI on the open internet by partnering with mobile DSPs with advanced machine learning. Mobile DSPs, specifically engineered for the intricate open internet landscape, offer advertisers the flexibility to scale campaigns and achieve measurable results in ways that broader platforms often fail to deliver.

Myth: High-level audience data is sufficient for targeting mobile campaigns on the open internet

Mobile behavior is changing constantly, and next week’s users may not be the same as today’s audience. Relying solely on historical data for cohort targeting limits reach and effectiveness. To stay ahead, marketers must adapt to real-time mobile user trends and avoid making assumptions based on past user behavior. 

The most advanced mobile DSPs take in live data, including first-party data such as a user’s in-app purchase behavior or downloads, campaign data related to overall performance and trends, and contextual data such as time of day, location and battery life on mobile phones. These platforms analyze user behavior and context, leveraging machine learning to deliver optimal ad experiences and serve the right message at the precise moment, to reach specific users.

Selecting the right partner and applying this more granular, dynamic approach to targeting significantly enhances campaign precision and effectiveness. Machine learning analyzes real-time user data to identify ad placements and timing, avoiding clutter while continuously optimizing.

Myth: Access to exclusive inventory is necessary for successful mobile advertising campaigns

The top six advertising exchanges account for approximately 70% of total impressions, yet only 1% are unique to any specific supply partner. Rather than exclusive access to specific inventories, the key to success is effectively identifying and reaching the most relevant audience within that available inventory through the right supply path. 

Analyzing vast real-time, user-level data is essential to find the most relevant users among the common inventory. Machine learning simplifies this process by making instant decisions about the best buying path for relevant audiences. In turn, marketers can drive greater value from their open internet campaigns. 

Sheikh explains that many open internet providers limit themselves by focusing on the top 100–200 publishers of games like Candy Crush. Although publisher-based targeting offers access to a vast audience, relying exclusively on this method can hinder the discovery of high-value users in less competitive app environments where ideal target audiences may also engage with content. 

“These popular apps are used at a high clip and can be a fantastic place to buy users, but they tend to be more crowded and realistically, there are less competitive, less crowded and more relevant apps where users likely spend more time,” he said. “Find a mobile partner with a nuanced understanding of users who can pinpoint the right individuals using advanced machine-learning tools. This requires specialized expertise that not all partners possess.”

While traditional DSPs often rely on publisher placements, advanced mobile DSPs focus on identifying and acquiring high-value users across various app environments. By concentrating on user-centric targeting, these platforms can discover valuable users in less competitive inventories.

Identifying a mobile-focused partner

While performance indexes from mobile measurement providers such as AppsFlyer or Singular can help further evaluate the current technology stack, marketers should examine the features and capabilities of mobile-focused DSPs. 

For instance, marketers need a basic understanding of how mobile DSPs leverage machine learning to optimize ad campaigns, such as how user-level data is utilized to make informed buying decisions, instead of solely relying on publisher or cohort data. It’s also crucial to evaluate a mobile DSP’s ability to adapt to changes like the deprecation of IDFA and serve as a partner and educator that can help teams successfully navigate industry shifts like Apple’s SKAN and AAK measurement solutions. 

“When we examine the partner landscape and evaluate media opportunities for our clients, we approach it from a mobile app-first lens,” said Tim Woitkun, vice president of mobile app marketing at dentsu media.

Determining whether a prospective mobile DSP provides agencies and advertisers with full dashboard transparency is also essential. Transparency allows marketing teams to understand how their budget is spent, glean creative insights, further validate measurement solutions and ensure brand safety. 

“We leverage brand-safe and innovative partners that enhance our connections across various data sources, while also allowing the ability to be agile in their approach toward meeting expected outcomes (KPIs) of business goals,” Woitkun said. “With machine learning and AI being integrated — or at least being discussed in some instances — across the ecosystem, we are carefully vetting how this is structured. Because mobile app isn’t the same as other digital channels within marketing, we’re always ensuring privacy compliance is in consideration.” 

By partnering with mobile DSPs, leading agencies and advertisers can harness the power of advanced machine learning to unlock the full potential of the open internet. These platforms offer granular insights and optimization capabilities that go beyond the limitation of walled gardens, enabling marketers to deliver more efficient and effective campaigns. As machine learning continues evolving, the industry can expect even more sophisticated tools and strategies to emerge, further revolutionizing the mobile advertising landscape.

Sponsored by Moloco