Algorithmic Attribution (AA) is one of the most advanced techniques that marketers can use to analyze and improve the performance of their advertising channels. Through better investment with every dollar invested, AA helps marketers maximize return for every dollar they spend.
While algorithmic attribution provides a myriad of advantages to businesses, not every business is eligible. Some organizations do not have access to Google Analytics 360/Premium accounts which allows for algorithmic attribution.
The Benefits of Algorithmic Attribution
Algorithmic attribute (or Attribute Evaluation Optimization or AAE) is an effective, data-driven method of evaluating, and enhancing marketing channels. It helps marketers pinpoint the channels that drive conversions the most effectively while simultaneously optimizing spending across channels.
Algorithmic Attribution Models can be constructed using Machine Learning (ML) and improved and updated continuously to increase accuracy. The models can be modified to changing marketing strategies and product offerings, all the while learning from the latest sources of data.
Marketers who use algorithmic attribution have higher conversion rates and higher returns on their marketing budget. Marketing insights can be improved by those who have the ability to respond quickly to market changes and keep pace with competitors tactics.
Algorithmic Attribution can assist marketers in identifying the content that generates conversions, and prioritizing marketing activities that yield the highest revenue, while minimizing those that don't.
The disadvantages of the algorithmic method of attribution
Algorithmic Attribution (AA) is the most modern method of attributing marketing efforts. It utilizes sophisticated mathematical models and machine learning technology to quantify objectively marketing efforts along the journey towards conversion.
The data can help marketers evaluate the effectiveness of their campaigns, find conversion-boosting factors and allocate budgets more efficiently.
Many organizations are struggling with this type of analysis as algorithmic attribution needs large databases and numerous sources.
The most common reason is due to a company not having sufficient data or the necessary technology to make use of this data.
Solution: A modern data warehouse in the cloud acts as a single source of truth for all marketing information. A holistic perspective of the customer and their interactions ensures insight is gained more quickly, relevancy is increased, and the attribution results are more precise.
The Advantages of Last Click Attribution
The attribution model for last clicks has rapidly been able to become one of the widely used attribution models. It allows all credit for conversions to return to the previous ad or keyword that contributed, making the process of setting up easy for marketers while not requiring any interpretation of data on their part.
This model of attribution does not give a complete picture of the journey a customer takes. It doesn't consider any engagement with marketing prior to conversion as a barrier which could cost you due to lost conversions.
Today, there are more powerful attribution models that will to provide a better picture of the buyer's journey, as well as more quickly identify which touchpoints and channels can be more effective at turning customers into buyers. These models can include linear attribution, time decay and data-driven.
The Drawbacks of Last Click Attribution
The last-click model is considered to be one of the most well-known attribution models in marketing. It is perfect for marketers that want to quickly determine which channels are the most critical to conversions. However, its use should be thoroughly evaluated prior to implementing.
Last click attribution is the practice of crediting only the most recent customer interaction prior to conversion. It can produce false and biased performance measurements.
First click attribution takes a different method of rewarding the user's first interaction with marketing prior to conversion.
This method is effective at a smaller scale, however it could be misleading if you're trying to maximize your campaigns, and prove how valuable they are to all people who participate.
This approach doesn't take into consideration the impact of conversions triggered by more than one marketing touchpoint therefore it is not able to provide useful insights into the effectiveness of your brand awareness campaign.
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