Constantine Yurevich is author of this article in which he argues why you should create a custom attribution tool. With out-of-the-box tools, you’re limited by their functionality, data transformations, models, and heuristics.
With raw data, you can build any attribution model that fits your business and domain area best. While few small companies collect raw data, most large businesses bring it into a data warehouse and visualize it using BI tools such as Google Data Studio, Tableau, Microsoft Power BI, Looker, etc.
- The challenge of attribution
- What machine learning changes
- Four steps to build an attribution model with Google BigQuery ML
- Step 1: Feature mining
- Step 2: Training a model
- Step 3: Model evaluation
- Step 4: Building an attribution model
- Pros and cons of behavior-based attribution
Quite in depth information about attribution models in general and good steering for anybody interesting in creating a custom attribution tool. Well worth your time!
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