Most mobile attribution defaults to last-click: the final ad the user clicked before install gets 100% credit. Simple, easy to implement, and the default in every MMP. But the user journey is rarely single-touch — a user might see a TikTok ad on day 1, a YouTube ad on day 3, click a Meta retargeting ad on day 5, then search the brand and install. Last-click gives Meta all the credit; the upstream creators (TikTok, YouTube) get none, even though they generated awareness.
Multi-touch attribution (MTA) distributes install credit across multiple touchpoints. Common MTA models:
- Linear: equal credit to every touchpoint. 5 touches → 20% each.
- Time-decay: more recent touches get more credit. The last click might get 50%, the previous 25%, the one before 12.5%, etc.
- Position-based (U-shaped): first and last touches get the most credit (typically 40% each); middle touches split the remaining 20%.
- Data-driven: machine-learning model that assigns weights based on observed correlation with conversion. Generally most accurate but requires significant data and platform support.
MTA is significantly harder on mobile post-ATT. The problem: cross-channel multi-touch requires you to know that User A saw a TikTok ad AND a Meta ad AND a YouTube ad — which requires a stable identifier across networks. Post-ATT, that identifier isn't reliably available on iOS for the 60-80% of users who don't opt in. Each network (Meta, TikTok) has its own attribution view but they can't be unified at the user level.
Workarounds in 2026
- Within-network MTA still works — Meta can do internal multi-touch within its own ecosystem (Facebook, Instagram, Audience Network). Same for TikTok internally. But cross-network MTA is degraded.
- Incrementality testing — instead of attributing credit to touchpoints, run holdout experiments: a control group sees no ads, a treatment group sees the campaign, measure the lift in conversions. The "incremental" conversions are the campaign's true impact. More rigorous than MTA, but requires holdout group infrastructure.
- Marketing Mix Modeling (MMM) — top-down statistical models that estimate channel-level impact on overall sales / installs from aggregated data, no user-level identifier required. Long timeframes, requires a lot of historical data.
Bottom line in 2026: pure multi-touch attribution at the user-journey level has degraded on iOS. Most sophisticated mobile UA teams now run a combination: last-click attribution as the operational source of truth, plus incrementality testing for big strategic decisions, plus MMM for top-down channel allocation. MTA models persist mostly inside walled gardens (Meta, TikTok internal MTA) rather than across them.