The Benefits of Advanced Attribution Models With Machine Learning

Despite the slowdown caused by the coronavirus crisis , the digital advertising industry has maintained its strong growth for years and, according to Statista , in 2019 it reached an aggregate turnover of more than $ 335 million. In a complex advertising landscape made up of constantly reinvented channels to which new players are added every day, advanced attribution models are becoming essential to accurately assess the return on advertising El Salvador WhatsApp Number List investment and the effectiveness of the website. The limits of classic attribution models are a set of rules through which companies assess the weight of each interaction with the brand in the final conversion (advertising impacts, but also each touchpoint on the page

and even in the physical environment if the we are talking about an omnichannel model). Traditionally, the most widely used attribution models are: Last-click : it grants the entire conversion to a single impact, the last click in the conversion stream. This model reflects a very limited reality: currently, customer journeys are relatively more complex . Multichannel : it integrates more channels and phases, sharing a fixed percentage of the conversion between the different impacts. It has a limit since these are closed models: the percentages are established and do not change, even if the user and his behavior change. Data-driven attribution : these models vary the attribution percentages to each impact.

Their Main Limitation Is Linked to the Fact That They Examine

the user’s journey from outside the site (especially the various advertising campaigns) and therefore do not take into account the user’s behavior on the site itself. Unlike these formulas, advanced attribution models not only assess the channels that took the user to the website, but also analyze their behavior on the site and cross-reference all the information to represent the entire lifecycle. purchase . Advanced Attribution Models: The Benefits of Applying Machine Learning Supervised learning algorithms work with input and output data already classified , i.e. the system is informed of the desired result and it deduces rules to be applied from the data it receives. Therefore, machine learning attribution models are not pre-designed, they are constantly adjusting and becoming more precise over time.

For example, each time the design of the website is modified, it is not necessary to revise the attribution model, this new functionality is integrated into the algorithm which readjusts itself. In the initial classification of information, it is essential to measure with events any possibility that the user has to interact with the page and, in addition, to incorporate additional data that gives context to the activity. This data must answer questions such as: how long has elapsed between events, how many times has each triggered, which devices has the user used, from which geographic location he accesses, among others.

The Richer The Information, The Better The Model Will Process

it to determine what is quality traffic and what is not. ÁLEX MASIP , HEAD OF DATA AT LABELIUM SPAIN In addition, by relying on user behavior, attribution models with machine learning are able to detect fraud , because if a robot brings in huge amounts of traffic but its website activity is null, the attribution model with machine learning will detect it and give it no weight in the conversion. Los modelos de atribución avanzados aprovechan el potencial del machine learning How does an attribution model with machine learning work? It can be summed up in three key steps:

Data collection : they can be exported via Google Analytics 360 or, failing that, with a JavaScript code that records the information. Other data sources can also be added, such as Adservers, CRMs or models that interpret UTMs. 2. Formulation of a data lake in which information is gathered: it can be articulated using platforms such as BigQuery , Amazon, Azure or Snowflake.

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