Machine learning in attribution

In our dynamically changing world, we all want not only to keep pace with the times, but more and more often to “work ahead of the curve”. In marketing and sales, this is especially true. Attribution assessment helps to choose the most effective conversion channels. But in the conditions of increasing the level of confidentiality, the introduction of restrictions on the collection and storage of data, more and more experts ask themselves the question: do they correctly assess this or that channel?

And here a new, actively developing direction of “machine learning” comes to the rescue. Machine learning systems allow you to aggregate and process huge amounts of data, make forecasts, analyze the current situation and predict future developments.

Who Will Machine Learning Help With Attribution?
First, the heads of sales and marketing, and secondly, the heads of data analysis. These specialists need analytical data for strategic planning, setting KPIs, and here indicators on extensive data will be useful. The only thing that this system is not very convenient for when assessing the attribution of different channels is that they cannot be compared with each other. for data analysis, a different set of algorithms and calculation models are used. In addition, it will be necessary to adjust or refine the site to meet the requirements of the system, so that the data is correctly collected and taken into account.

Before you can start setting up and using machine learning, you need to have data in the following areas:

• Budget of previous advertising campaigns,
• Types of channels attracted / used,
• The capacity of the channels used,
• Profile market trends,
• Knowledge of competitors and their activity,
• User-level data.

Typically, in the current environment, some data are not available or are difficult to translate into numbers. Therefore, for the correct use of the obtained data based on machine learning, they should be considered not as a “guide to action”, but as a set of “data and personal expertise”. For example, one cannot do without personal experience in such issues as determining the attribution window, choosing channels and reallocating the budget, assessing the influence of channels on each other.

Machine learning is the future. However, it is important to use it wisely: do not rush to conclusions and apply personal expertise.
Machine learning is incorporated into the work of the GoCPA system, and we are happy to share the collected expertise in the form of a platform so that it is more convenient for you to achieve the desired results in promotion.