Paid User Acquisition, with the whole app in view.
Paid user acquisition addresses the challenge of scaling app installs while maintaining profitable unit economics. Without rigorous audience segmentation, creative testing, and downstream value tracking, marketing budgets are easily consumed by low-intent users who churn before generating revenue. Post-privacy tracking changes like Apple's SKAdNetwork (SKAN) and Google Privacy Sandbox have compounded this challenge by obscuring real-time deterministic attribution.
What we can take care of
- Cross-channel media buying strategy across Meta Ads, Google App Campaigns (UAC), TikTok, and Apple Search Ads
- Granular audience segmentation, lookalike modeling, and contextual placement configuration
- SKAN 4.0/AdAttributionKit and Google Privacy Sandbox conversion value schema architecture
- Creative testing matrices combining concept variations, aspect ratios, and format requirements
- Daily budget pacing, automated bid optimization, and target cost-per-action (tCPA/tROAS) management
- Channel incrementality testing models to measure net-new install lift versus organic cannibalization
- Downstream cohort profitability reporting connecting ad spend to in-app revenue and retention
How we approach the work
We manage paid user acquisition as an investment portfolio governed by strict unit economics benchmarks. We establish target payback periods and LTV-to-CAC thresholds before deploying capital. Campaigns launch with small, controlled test budgets across diversified channels to establish baseline conversion efficiency and validate audience responsiveness. We then allocate scale budget toward channels demonstrating strong Day 7 and Day 30 post-install engagement, constantly pruning inefficient placements.
What progress looks like
Blended and channel-specific Cost Per Install (CPI), Customer Acquisition Cost (CAC), Day 1/7/30 user retention, Return on Ad Spend (ROAS), and incrementality lift over organic baseline.
Before we begin
Share your app links, the challenge and the outcome you want. Your concierge will confirm inputs, deliverables, timing and costs for your scope before work begins.
How do you navigate user privacy restrictions like Apple's SKAdNetwork (SKAN)?
We design custom conversion value schemas that compress early predictive in-app behavioral signals into the initial postback windows. By mapping engagement milestones, subscription trials, or early purchases into fine and coarse conversion values, we provide the ad network algorithms with sufficient optimization signals while maintaining compliance with privacy frameworks.