Olivia Kory
CMO and strategy lead at Haus (haus.io), a geo-experiment incrementality platform with a “couple hundred” customers (2026). Previously at Publicis, TubeMogul (DSP), Netflix (where she learned incrementality testing), Quibi (mobile app) and Sonos (e-commerce).
Strong on: designing incrementality tests, how last-click biases channel results, CTV and upper-funnel test duration, translating measurement into finance-team language.
Bias to keep in mind: sells incrementality testing — naturally argues that last-click misleads and that longer/bigger tests are needed. Haus runs vendor-funded tests (ad-platform credits), and its AppLovin meta-analysis covers Haus customers (mostly e-commerce/DTC brands), not a random sample of app advertisers.
Sources: Mobile Dev Memo podcast (sources/youtube/mobile-dev-memo/).
Notes citing Olivia Kory
- Designing incrementality tests (duration, budget, CTV and brand)Practical rules for geo/holdout incrementality tests — run upper-funnel and CTV tests 4–6 weeks (two weeks shows nothing), give them enough budget not to be swamped, avoid launching into seasonality, and judge brand spend on a finance timeline.
- Do you need an MMP? AppsFlyer, Adjust and the state of SKAdNetwork (2026)Why mobile measurement partners (AppsFlyer, Adjust, Branch…) remain mandatory for app advertisers buying Meta at scale, what the 2026 AppsFlyer consortium investment means for neutrality, and why SKAdNetwork/AdAttributionKit is not worth heavy investment.
- How last-click attribution biases channel resultsLast-click flatters low-funnel, intent-capturing channels (search, Apple Ads, Google UAC standard) and under-credits view-based ones (YouTube, CTV, chatbot ads); incrementality tests have reversed channel rankings — and platform-reported numbers are biased toward the platform.
- 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.