Multi-Touch Attribution (MTA)
Multi-touch attribution spreads conversion credit across all the ad interactions a user had before converting, not just the last click.
Definition
Multi-touch attribution (MTA) recognizes that users often interact with multiple ads across multiple channels before installing an app or making a purchase. Instead of crediting only the final touchpoint (last-click attribution), MTA distributes credit across all the ad interactions in the conversion path.
Common MTA models include: linear (equal credit to every touchpoint), time-decay (more credit to touchpoints closer to the conversion), position-based (40% to first touch, 40% to last touch, 20% shared across middle touches), and data-driven (algorithmic credit allocation based on actual conversion lift contribution).
MTA is significantly harder to implement than last-click attribution because it requires tracking users across multiple ad interactions, which depends on persistent identifiers. Post-ATT, cross-channel MTA on iOS has become much harder to execute accurately. Most teams rely on last-click for day-to-day optimization and use incrementality testing to validate the broader contribution of each channel.
Why it matters
Last-click attribution systematically undervalues upper-funnel channels that initiate the user journey. A TikTok awareness video that introduced a user to your app gets zero credit if the user later searched on Google and installed. Multi-touch attribution corrects this bias, giving you a more accurate picture of which channels are actually contributing to growth so you can allocate budget more rationally.
Example
In practice
A user sees a TikTok ad (impression), clicks a Meta ad three days later, then searches Google and installs via a Play Store ad. Last-click gives 100% credit to Google. A linear MTA model splits credit evenly across all three touchpoints, revealing TikTok's role in initiating the journey.
Frequently asked questions
Should I use multi-touch attribution instead of last-click?
For most mobile app teams, last-click remains the primary operational model because it's simpler and universally supported. MTA is most useful for understanding channel contribution at a strategic level, particularly if you're running awareness campaigns alongside direct-response campaigns and want to avoid under-investing in upper-funnel channels.
What's the best multi-touch attribution model?
Data-driven attribution, which uses actual conversion data to weight touchpoints, is the most accurate but requires significant data volume. For smaller programs, position-based (first/last touch get most credit) is a practical middle ground. The right model depends on your funnel length and how much multi-channel data you have.