<?xml version="1.0" encoding="UTF-8"?><ns2:project xmlns:ns1="http://gtr.rcuk.ac.uk/gtr/api" xmlns:ns2="http://gtr.rcuk.ac.uk/gtr/api/project" xmlns:ns3="http://gtr.rcuk.ac.uk/gtr/api/fund" xmlns:ns4="http://gtr.rcuk.ac.uk/gtr/api/person" xmlns:ns5="http://gtr.rcuk.ac.uk/gtr/api/project/outcome" xmlns:ns6="http://gtr.rcuk.ac.uk/gtr/api/organisation" ns1:created="2026-06-22T07:57:45Z" ns1:href="http://gtr.ukri.org/gtr/api/projects/1133C705-DEEE-48DE-9BD9-C60DCC95129F" ns1:id="1133C705-DEEE-48DE-9BD9-C60DCC95129F"><ns1:links><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/60861E7B-1EB1-426D-8903-CCE11C497387" ns1:rel="LEAD_ORG"/><ns1:link ns1:href="http://gtr.ukri.org/gtr/api/organisations/60861E7B-1EB1-426D-8903-CCE11C497387" ns1:rel="PARTICIPANT_ORG"/><ns1:link ns1:end="2016-12-31T00:00:00Z" ns1:href="http://gtr.ukri.org/gtr/api/funds/B44117CE-6D5E-443A-B4CB-60377154F0FD" ns1:rel="FUND" ns1:start="2016-03-31T23:00:00Z"/></ns1:links><ns2:identifiers><ns2:identifier ns2:type="RCUK">230031</ns2:identifier></ns2:identifiers><ns2:title>Exogenous Attribution For Generic Search</ns2:title><ns2:status>Closed</ns2:status><ns2:grantCategory>Procurement</ns2:grantCategory><ns2:leadFunder>Innovate UK</ns2:leadFunder><ns2:abstractText>Exogenous attribution modelling will combine the insights of digital attribution approaches with the

methods of machine learning, plus classic market mix modelling, in order to account for exogenous

factors to digital marketing, such as above the line marketing. These new models will evaluate the

contribution of offline, generic and brand search to the final purchase.

To achieve Metageni data scientists will leverage both new signals and new modelling approaches.

The idea is that within a digital path to conversion analysis, variations in timing, geography, devices

and social sentiment can be leveraged to model exogenous factors, external to the digital path

analysis, such as TV campaigns.

The aim would be to create an experimental validation

for modelling based on these factors, and to avoid the ambiguity of 'multi touch' models, providing a

definitive ROI attribution solution at the campaign and ad level.</ns2:abstractText></ns2:project>