Multiple rules-based and algorithmic approaches to attribution offer robust analysis of customer behaviour, providing a best-fit model per channel based on your customers’ actual interaction patterns.
Marketing attribution helps you to understand what your customers want and leads to smarter spend decisions. By helping you to understand how different interactions effect movement along the customer journey, attribution makes it easy prioritise 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 realised 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 dataset.
With our Analysis Workspace feature, you get a robust, flexible canvas for building customised analysis projects. Drag and drop any number of data tables, visualisations and components (channels, dimensions, metrics, segments and time granularities) to a project.
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.