1/16/2024 0 Comments Multi touch attribution![]() ![]() ![]() ![]() Having a clear understanding that multi-touch attribution is not a perfect solution may enable marketers to better appreciate the broader picture, viewing multi-touch attribution as a vehicle for (and a product of) experimentation and optimization rather than simply for resource allocation. But if a campaign consists of multiple advertisements, perhaps one is effective, and the others are instead driving customers away. A brand that attributes 70 cents of each purchase dollar to an advertising campaign and 30 cents to a direct mail piece may decide to boost spending on advertisement. The “recorded and measured” sequence of events does not in and of itself show the reasons for the purchase.Īnother limitation of multi-touch attribution is that an allocation across touchpoints may fail to account for multiple touches within a specified allocation. But perhaps the buyer’s daughter, thinking her mother would like the outfit, put it in an online shopping cart and shared the link. If a consumer purchases an outfit after seeing an ad, it may be easy to assume that seeing the ad triggered the purchase. Post Hoc Fallacyīefore examining how a customer data platform (CDP) can help with multi-touch attribution, we should recognize some difficulties with multi-touch attribution that are inherent in the “rear-view mirror” approach of trying to understand the causes of a customer action that’s already happened.įirst, multi-touch attribution is subject to a logical fallacy: Since Y follows X, Y must therefore have been caused by X (in Latin: post hoc ergo propter hoc). In fixing multi-touch attribution, marketers have the opportunity for a more accurate representation of customer journeys and customer response to interactions, one that measures the effectiveness of a campaign or channel with a higher degree of certainty. When marketing dollars are tied to these factors, which they eventually will be, it is easy to see how marketers end up increasing chaos or contention in their allocations vs. Likewise, the seemingly randomized associations may skew the cause-and-effect decisions further downstream, from channels and campaigns to offers, messages, content and any number of factors. If a customer makes a purchase on a website directed from a Google search, for example, a last-touch attribution model may not acknowledge or credit the influence of the ad the customer had engaged with on YouTube earlier that day, or that two days prior the customer had browsed the same product on a branded website. First, traditional methods use haphazard associations. But marketers are far from measuring and improving multi-touch campaigns in an optimal way.ĭynamic customer journeys lay bare the shortcomings of first-touch, last-touch, evenly weighted distribution or other traditional attribution methods, which create incentives for doing multi-touch attribution. Some form of multi-touch attribution is needed to understand increasingly complex, multi-touch, multi-channel customer journeys. Though perhaps better than simpler attribution methods, multi-touch attribution by virtue of its over-the-shoulder rear view will never match, with 100 percent accuracy, an action to a reaction. Multi-touch campaign attribution is an imperfect solution to a vexing problem. ![]()
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