Attribution|Algorithmic Attribution}
Algorithmic Attribution (AA) is one of the top methods available to marketers to measure and optimize the performance of their advertising channels. AA maximizes the return on each penny spent by helping marketers make better decisions about their investment.
Although algorithmic attribution has many advantages to businesses, not all organizations are eligible. Some organizations do not have access to Google Analytics 360/Premium Accounts, which makes algorithmic attribution possible.
The Benefits of Algorithmic Attribution
Algorithmic attribute (or Attribute Evaluation Optimization or AAE) is a data-driven, effective method of evaluating and optimising marketing channels. It aids marketers to determine the channels that are most effective in driving conversions efficiently, while simultaneously optimizing their the media budget across all channels.
Algorithmic Attribution Models are created through Machine Learning (ML), they can be trained, and updated over time in order to continually increase accuracy. They are able to learn from new data sources, while also adapting their model according to changes in marketing strategies or product offerings.
Marketers who employ algorithmic allocation have seen higher rates of conversion, and a greater return on their advertising dollars. Marketers can optimize real-time insights by quickly adapting to changing market trends and staying up with the evolving strategies of competitors.
Algorithmic Attribution helps marketers in identifying the type of content that boosts conversions and identifying marketing initiatives that bring in the most revenue while reducing efforts that do not.
The disadvantages of the algorithmic method of attributing
Algorithmic Attribution is a modern method of assigning marketing effort. It employs advanced algorithms and statistical models to evaluate the effectiveness of marketing touchpoints all along the journey of a customer towards conversion.
Marketers can evaluate the impact of their campaigns and identify high-yield conversion catalysts with this data, while spending their budgets more efficiently and prioritizing channels.
The complexity of algorithmic attribution as well as the need to access huge datasets from different sources make it difficult for many companies to carry out this type of analysis.
A common cause is that a company may not have the right data or the required technology to extract the data effectively.
Solution: An integrated cloud data warehouse can be the only source of absolute truth when it comes to marketing data. A holistic view of the customer and their interactions ensures insight is gained more quickly while ensuring that the relevance is enhanced and the attribution results are more precise.
Last click attribution: Its benefits
The model of attribution for last clicks is the most well-known model for attribution. This model permits credit to be awarded to the latest ad keyword or campaign that generated the conversion. It is easy to implement and does not require any analysis of data from marketers.
But, this model of attribution doesn't provide a comprehensive picture of the customer's journey. It disregards any marketing engagement before conversion as an obstacle and this could prove costly due to lost conversions.
There are now more reliable models of attribution that could provide you with a fuller view of the buyer's journey and more easily identify which marketing channels and touchpoints have the best chance of turning customers into buyers. These models can include linear attribution, time decay, and data-driven.
The drawbacks of Last Click Attribution
Last-click-attribution, one of marketing's most popular models is an excellent method to identify which channels are most effective in contributing to conversions. However, its application must be carefully evaluated before implementation.
Last click attribution technology permits marketers to only credit the point at which customers have completed their engagement prior to conversion, leading to inaccurate and biased performance metrics.
The first method of attribution for clicks provides customers with a bonus for their first marketing interaction prior to conversion.
On a smaller scale, this may be helpful however, it can be untrue when trying to maximize campaigns or prove importance to people who are involved.
Because this method only looks at the effects of one marketing touchpoint - meaning it misses important information regarding the brand awareness campaign's effectiveness.