Nebojsa Radovic (“Nebo”)
Mobile gaming UA veteran since 2013: Nordeus (acquired by Take-Two), Machine Zone (one of the largest mobile-game advertisers of the mid-2010s; he ran DSP/programmatic buying), N3TWORK (with Eric Seufert), and Zynga for ~6 years. On X as “ENIAC”.
Strong on: how mobile UA evolved from manual to algorithmic buying, signal engineering for purchase-optimised campaigns, creative “hero rate”, UA team structure and channel mix by budget.
Bias to keep in mind: big-budget mobile gaming perspective (tens of millions per month, licensed IP); channel advice (AppLovin first, Meta later) is game-specific and doesn’t transfer directly to subscription apps.
Sources: Mobile Dev Memo podcast (sources/youtube/mobile-dev-memo/).
Notes citing Nebojsa Radovic
- How mobile UA changed (2015 → 2026) — the remaining levers, team structure and channel mix by budgetFrom 100-person manual media-buying teams (Machine Zone era) to algorithmic buying on 4–5 consolidated networks — what's left to control (bid, campaign type, budget, creative, signal), how a modern UA team is structured, and how channels and process change from six- to eight-figure monthly budgets (gaming lens).
- Should we add a new ad channel? Scale, return, effort — and small platform syndromeHow to decide whether a new or smaller ad channel (CTV, AppLovin for e-commerce, chatbot ads, retail media) is worth testing — judge scale, achievable ROAS and team effort, require it to beat (not match) incumbents, and budget for a real test plus data integration.
- Signal engineering — auditing and shaping the conversion events ad platforms optimise onFirst check what conversion data actually reaches Meta/Google (duplicates, wrong optimisation event), keep "what the platform needs" separate from "what the business wants", and engineer enough purchase events (e.g. ~20/day per campaign in gaming) for campaigns to exit learning.
- The measurement stack — last-click attribution, incrementality and MMM togetherWhy app advertisers need both deterministic last-click attribution (for platform optimisation and daily decisions) and incrementality testing (for budget allocation), where MMM fits, and how to reconcile them against top-line results.
- When last-click attribution is good enough (and when to invest in incrementality)For small, performance-driven advertisers on one or two channels, last-touch attribution is often ~85% as good as experiments and better than a bad MMM; invest in incrementality tests as spend, channel count and brand effects grow.