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Advantages of advanced attribution models with machine learning

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Advantages of advanced attribution models with machine learning

Despite the downturn caused by the coronavirus crisis , the digital advertising industry has been growing strongly for years and, according to Statista , in 2019 it reached a global turnover of more than 335 million dollars. With a complex advertising landscape made up of constantly  canada phone number reinventing channels with new players being added every day, advanced attribution models become essential to accurately assess the return on advertising investment and the effectiveness of the website.The limitations of traditional attribution models 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 we talk about omnichannel models). Traditionally, the most used attribution models are the following: Last-click : Grants the entire conversion to a single hit, the last click in the conversion flow. This model reflects a very limited reality: customer journeys today are much more complex .
Multichannel or multi-channel : incorporates more channels and phases, distributing a fixed percentage of the conversion between the different impacts. Their limitation is that they are closed models: the percentages are established and do not evolve, although the user and their behavior change.
Data-driven attribution : these models variably adjust the attribution percentages to each of the impacts. Their great limitation is related to the fact that they examine the path that the user is drawing externally to the web (especially the different advertising campaigns) and, therefore, they do not take into account their behavior on the page itself.
Unlike these formulas, advanced attribution models not only evaluate the channels that have led the user to the web, but also analyze the behavior that the user has had on the site and cross all the information to represent the complete purchase cycle .Advanced attribution models: the benefits of applying machine learningThe supervised learning algorithms work with already classified input and output data , that is, the system is indicated the desired result and it, from the data it receives, infers rules to apply.
Therefore, machine learning attribution models are not pre-designed in advance, but are continually adjusted and gain precision over time. For example, every time the web design is changed it is not necessary to rethink the attribution model again, but rather this new feature is incorporated into the algorithm and it readjusts itself.In the initial classification of the information, it is essential to measure with events any possibility that the user has of interacting with the page and, in addition, to incorporate additional data that gives the activity context. These data have to answer questions such as: how long has elapsed between event and event? How many times has each one been triggered? What devices has the user used? The richer the information, the better the model will process it to determine what is quality traffic and what is not .

 

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with machine learning are capable of detecting fraud , since if a bot is bringing huge amounts of traffic but then its activity on the web is null, the attribution  gulf email list  model with machine learning will detect it and will not give it weight in the conversion Advanced attribution models harness the potential of machine learning
How an attribution model works with machine learning
It can be summarized in three key stages. Data collection : can be exported through Google Analytics 360 or, failing that, with a JavaScript code that saves the information. You can also add other data sources such as Adservers, CRM or models that interpret UTMs.. Formulation of a data lake in which the information is grouped : it can be articulated with the help of platforms such as BigQuery , Amazon, Azure or Snowflake.. Implementation of the attribution model with machine learning in the data lake so that it assigns a value to ech session and to each external impact that the user has received. In this way, it is possible to accurately measure the probability of conversion . The algorithm performs the calculation at the individual level for each visit and is then grouped to obtain global percentages in order to identify the weight that each interaction has had in the final salesAttribution in real time and constantly evolvinAttribution models with machine learning not only allow you to analyze the effects of completed advertising campaigns, but also offer a global vision of what is happening in real time. This makes it easier to improve strategy on the fly by enabling inefficiencies to be detected and corrected . In addition, advanced attribution models can forecast the future to propose possible scenarios and choose the most profitable ones.If you want us to help you implement an advanced attribution model on your website or e-commerce, do not hesitate to contact us . We will study your case in a personalized way to find the most effective way to start it.

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