With our Analysis Workspace feature, you get a robust, flexible canvas for building custom analysis projects. Drag and drop any number of data tables, visualizations, and components (channels, dimensions, metrics, segments, and time granularities) to a project.
Multiple rules-based and algorithmic approaches to attribution offer robust analysis of customer behavior, providing a best-fit model per channel based on your customers’ actual interaction patterns.
Not all touchpoints are created equal.
Marketing attribution helps you understand what your customers want and leads to smarter spend decisions. By helping you understand how different interactions effect movement along the customer journey, attribution makes it easy prioritize the right content and channels.
Adobe Analytics offers a wide variety of attribution models that are easy to use in your day-to-day analysis. From first and last touch to participation to time decay and more — including algorithmic attribution powered by advanced machine learning — our attribution models paint a clear picture of where meaningful marketing touches are taking place, how users are exposed to your messaging across channels, and the optimal budget allocations that should take place within those channels.
Our models include first and last touch, linear, participation, U-shape, J-curve and inverse J, last non-direct click, time decay, and algorithmic models.
Qualify interactions that should receive credit for realized revenue or participation downstream in the customer journey.
Merge visitor profiles after they’ve been associated with the same visitor ID variable so attribution does not change in the historical data set.
Learn more about marketing attribution in Adobe Analytics.
See related features.
Data warehouse and data feeds
We offer extended storage, data reprocessing, and reporting capabilities for granular-level customer data in our data warehouse. And our data feeds deliver batched raw data on a recurring daily or hourly delivery schedule.
Discovers hidden patterns within your data to explain statistical anomalies and identify correlations behind unexpected customer actions, out-of-bound values, and sudden spikes or dips for metrics across audience segments.