Multi-Touch Attribution (MTA)
Multi-Touch Attribution (MTA) is an analytical framework that distributes credit for a booking across every marketing touchpoint a guest encountered during their path to purchase — rather than assigning 100% credit to a single interaction such as the last click.
Common MTA models
- Linear: equal credit split across every touchpoint
- Time-decay: more credit to touchpoints closer in time to the booking
- U-shaped / Position-based: 40% each to first and last touch, 20% spread across middle interactions
- Data-driven: algorithmic weighting based on observed conversion patterns
Example
A guest searches on Google Hotel Ads (first touch), sees a retargeting ad on Instagram, opens a promotional email, then books direct (last touch). Under last-click attribution, the email receives 100% of the credit. Under a linear MTA model, each touchpoint receives 25%, revealing that metasearch played a material discovery role that would otherwise be invisible.
Why it matters
Hotels and OTAs running campaigns across metasearch, paid social, email, and display advertising need to understand which combination of touchpoints drives conversions. Last-click attribution systematically over-credits the final interaction and under-values top-of-funnel awareness activity. MTA models provide more accurate signals for budget allocation — in practice, this often leads to increased investment in metasearch and display, which look weak under last-click but contribute meaningfully to the overall booking path.
The primary challenge of MTA is data: cross-device journeys and OTA-walled-garden data make it difficult to stitch together a complete picture. Hospitality brands with strong direct channels and first-party data are best placed to implement MTA credibly.
Related
See also: Last-Click Attribution, Attribution Window, CPA (Cost per Acquisition), Metasearch, Retargeting