Optimizing Apple Search Ads: Practical Insights from Viktor Orlov at Business of Apps Berlin 2025

At the Business of Apps Berlin 2025 conference, Viktor Orlov presented key insights into optimizing Apple Search Ads (ASA). Orlov highlighted a shift from theoretical discussions to offering practical, actionable guidance, noting that previous presentations often dwelled too much on abstract concepts.

Apple Search Ads Characteristics

Orlov emphasized that Apple Search Ads remains a strong mobile user acquisition channel primarily due to direct search intent, which translates into high purchase intent. This inherent intent contributes significantly to the channel's stability, marked by less volatile prices and consistent volumes compared to more fluctuating social channels.

He also pointed out that creative fatigue is a lesser constraint in ASA, and the system consistently rewards correctness over novelty. Given the channel's inherently manual nature, lacking native post-install integrations and significant automation, Orlov underscored the importance of discipline in its management.

The Importance of Control

Effective ASA management, according to Orlov, begins with campaign structure. He critiqued the common practice of grouping numerous keywords into single ad groups as inefficient. Instead, he strongly advocated for single-keyword ad groups (SKAGs) to achieve granular control, which he deemed essential for truly understanding auction dynamics.

Increased control is the primary factor for success in Apple Search Ads, enabling better relevance.

This granular control, Orlov explained, is fundamental for optimizing performance.

Key Competitor Misconception

When conference attendees were asked about their competitors in ASA, they typically cited other advertisers or large brands. Orlov clarified this common misconception, stating that the most significant competitor is the top organic search result. Paid ads directly compete with organic listings for user attention and clicks.

Therefore, the optimization goal isn't merely to outbid other advertisers, but to outperform the best organic alternative for a given query.

Installs Per Thousand Impressions (IPM)

Orlov highlighted Installs Per Thousand Impressions (IPM) as a crucial, though not directly exposed, metric that governs scale in ASA. IPM effectively combines tap-through rate and conversion rate, acting as a direct signal of relevance to Apple's auction system.

IPM combines tap-through rate and conversion rate, signaling relevance to Apple's auction system.

He explained that below a certain IPM threshold, impression share can stall, and campaigns face significant constraints. However, crossing this "invisible wall" can lead to accelerated impression allocation and improved CPIs.

Custom Product Pages for Relevance

To improve IPM, Orlov stressed the importance of optimizing what users see. He identified Custom Product Pages (CPPs) as the most powerful, yet often underutilized, tool in Apple Search Ads. Despite the capability to create up to 70 CPPs per app, many teams fail to leverage them fully.

CPPs enable the precise matching of messaging, visuals, and onboarding flows to specific keyword intent. This targeted approach enhances conversion, raises IPM, and ultimately increases impression share. Furthermore, deep links extend this logic by directing users to specific features or offers within the app, which further signals relevance to Apple.

Pricing and Profitability

Orlov asserted that pricing in ASA is an outcome, not a primary lever. Apple possesses visibility into in-app purchase revenue and uses this data to estimate profitability for various keywords. He suggested that if a keyword is unprofitable for one app, it is likely profitable for another within the same category.

Campaign failures, he concluded, often reflect a mismatch between execution and category economics, rather than indicating a broken channel.

The Role of Knowledge

In his closing remarks, Orlov stated that while marketing tools have largely converged, understanding the underlying auction mechanism remains a critical differentiator. Case studies presented consistently demonstrated that improved relevance and increased IPM consistently led to lower prices and greater scale, irrespective of the app category.

Apple Search Ads is predictable but unforgiving, rewarding consistent execution and deep system understanding.

He characterized Apple Search Ads as a system that is predictable but ultimately unforgiving, rewarding consistent execution and deep system understanding.