Note that the introduction of Click Validation has vastly improved multi-touch capabilities. This means the sample data is incomplete and skewed, opening the door for spoofed engagements. The issue is that third-party mobile attribution companies often only see a tiny fraction of engagements. In multi-attribution, we expect the uplift effect of Ad A to be represented in a cohort of users who have seen Ad A and are now more likely to click Ad B. It’s quite a stretch to assume that a "low intent" conversion like downloading a free app is the same as spending money in-app. However, the user's intent to install should be weighed much less than a purchase decision, for example. Most often, multi-touch solutions focus on app installs as the conversion goal. The first problem with multi-touch is defining the conversion event made possible by a series of ads. However, metrics for multi-touch attribution are difficult to obtain as several problems plague this attribution model. The conversion rate from landing on an app store page to clicking the download button is also likely to be driven by the app page content and the quality of the user. Theoretically, we would expect to see a higher click-through rate (CTR) for users that have previously viewed another ad. This attribution model aims to identify ad campaigns that create an uplift of conversions for other ads while also crediting everyone involved in an install. The crux of multi-touch attribution is that credit is given across the entire user journey, whether ad impressions, clicks, or conversions.
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