Navigating the Algorithmic Attribution Landscape: A Comprehensive Handbook

Algorithmic Attribution (AA) is one of the latest methods that marketers have for measuring and optimizing the performance of their marketing channels. Through making better investments for every dollar invested, AA aids marketers in maximizing the return on every dollar spent.

While algorithmic attribution provides a myriad of advantages for companies, not every company is eligible. It is not all companies have access Google Analytics 360 or Premium accounts that make use of algorithmic attribution available.

The Benefits of Algorithmic Attribution

Algorithmic Attribution, also referred to as Attribute Evaluation and Optimization (AAE), is a data-driven, efficient way to evaluate and optimize marketing channels. It helps marketers identify the channels that are driving conversions while optimizing media spending across all channels.

Algorithmic Attribution Models can be created by Machine Learning (ML) and improved and updated continuously to increase accuracy. They can be tailored to new marketing strategies and product offerings, as well as learning from new sources of data.

Marketers who use algorithmic attribution have seen greater levels of conversion and better profits from their advertising budgets. Marketing insights can be improved by marketers who are able adapt quickly to market changes and keep up with competitors and strategies.

Algorithmic Attribution is another tool that can assist marketers to identify content that converts, and prioritize marketing efforts which generate the most revenue while decreasing those which do not.

The Negatives Of Algorithmic Attribution

Algorithmic Attribution (AA) is the modern approach to attributing marketing efforts. It utilizes sophisticated algorithms and statistical technologies to measure objectively the marketing efforts along the journey to conversion.

By using this information marketers can better gauge the impact of their campaigns, and also identify the factors that drive conversions and are likely to bring high returns. They can also determine budgets and prioritize channels.

Many organizations are struggling with this type of analysis since algorithmic attribution requires large datasets and many sources.

A common cause is that a business might not have enough data, or the required technology to extract these data efficiently.

Solution: A modern data warehouse in the cloud is an unifying source of truth to all marketing data. With a complete overview of customer interactions and touchpoints, this ensures faster insights, increased relevancy and more precise attributing results.

The Benefits of Attribution to Last-Click

It's not a surprise that last-click attribution has quickly become one of the most sought-after models of attributing. This model permits credit to be awarded to the latest ad, the keyword or campaign that brought about the most conversion. It is easy to set up and does not require any data interpretation by marketers.

This model of attribution does not offer a complete view of the journey a customer takes. The model doesn't consider marketing interactions prior to conversions as a barrier which can be expensive in terms lost conversions.

These days, there are more robust models of attribution that can give you a an accurate picture of the customer journey. They also allow you to discern more precisely what channels and touchpoints are converting customers more effectively. These models can include linear, time decay and data-driven attribution.

The disadvantages to Last Click Attribution

The model of the last-click is one of the most popular attribution models in marketing. It is ideal for marketers who wish to quickly determine the channels that are crucial in converting. However, its application must be thoroughly evaluated prior to implementing.

Last-click attribution can be described as a marketing method that lets marketers only attribute the last point of interaction with a client prior to conversion. This could lead to inaccurate and biased performance metrics.

But, the first click attribution takes an alternative approach - giving customers a reward for the initial marketing interaction prior to conversion.

On a smaller scale this method can be beneficial but it can also be misleading in trying to maximize campaigns and prove worth to the people who are involved.

This approach is flawed as it only looks at conversions that occur because of one marketing touchpoint. This leaves the most important data regarding the effectiveness of your brand awareness campaigns.