![]() ![]() The Pros of “U” and “W” Shaped Attribution: In the “W”, more credit is given to the “mid-funnel” with the assumption that it contributes more too. These Attribution methods also attempt to assign weights, factors or “rules” to give credit to different points in the funnel- usually by assigning more credit to the first and last ads in the sequence. It is usually highly inaccurate and will lead to extremely poor marketing investment decisions that will harm the brand in the long run.The weights are too simplistic and assign the same credit for channels that likely have significant differences among specific campaigns or ads within them.Most of the weights are arbitrary and are not rooted in any data or evidence.It can lead to more investment in upper-funnel media.Rules-Based Attribution tries to address the weaknesses of Last-Touch Attribution by giving more credit to ads that don’t usually get credit.While it is noble in its attempt to overcome the weaknesses of more simplistic attribution approaches such as last-touch, the weights or rules can be overly simplistic, or even just flat-out incorrect, which creates more problems than it solves. Rules-Based Attribution attempts to assign weights, factors or “rules” to give credit to different points in the funnel. It can lead to extremely poor marketing investment decisions that will harm the brand in the long run.It relies on clicks thereby ignoring the awareness building effects of ad impressions.Cookie windows and cookie expiration dramatically shorten the measurement window (and potential effects) of longer-term awareness building ads.It almost always over-values upper funnel ads that have been seen within short windows of time (1-2 weeks before conversion).It is easily measured for free and doesn’t require complex software.It gives more credit to earlier funnel media, and avoids giving all of the credit to lower funnel media.This is a common attribution option in free attribution tools within Google Analytics or other web analytics solutions. It assigns full credit for the conversion to the first ad clicked. Different Types of Multi-Touch Attributionįirst-Touch Attribution is also a simple form of marketing attribution. The result is typically a lengthy deployment cycle, added costs with 3 rd party identity data graphs, brittle solutions that are difficult to maintain (or change), and measurement without known accuracy. And this is fraught with accuracy problems when even a small number of conversion identities is mistakenly mis-matched with ads.įinally, building a marketing attribution solution requires an enormous amount of data, server-to-server integrations with third parties, the exchange of PII, as well as user-ad level impression data, which is now increasingly difficult to obtain from the walled gardens. Doing this with any level of accuracy requires evaluating billions of comparisons as even a small set of campaigns across channels generates an enormous number of potential sequences and combinations. Compounding this complexity is that the MTA solution must also compare sequences of ads seen, keep the identity straight across multiple devices and the order of the ads seen to be able to measure the magic combination of ads that leads to the highest conversion lift. Once a consumer’s identity has been matched to an ad, a device, and a purchase transaction, the multi-touch attribution solution must then compare conversion performance of those customers who’ve seen the ad with those consumers who have not in order to determine whether the ad has generated any true lift. Complicating this are major technology industry changes from Apple’s iOS 14.5 update that has blocked a major portion of mobile device tracking and Google Topics will stop all consumer level targeting and tracking for Google-owned ad networks and Chrome. Now, a brand must use 3 rd party identity graphs along with their own first party data to attempt to stitch this picture together. Traditionally this was accomplished using cookie data but this method has all but been shut down as a result of browser cookie blocking. To make MTA work, a brand must align ad impressions with consumer and device identity data in order to know “who saw which ad” and whether a conversion occurred. Also, it cannot measure conversions where the consumer’s identity is not known (in-store cash transactions for example). This marketing attribution approach requires user identity data and because of this, it can only be used to track addressable media (mainly digital advertising) and cannot measure traditional media such as print, TV, radio and OOH. Multi-touch attribution (MTA) is a marketing measurement approach that attempts to track users across devices and the ads they’ve seen in order to determine how the ads contribute to the path to purchase. ![]()
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