Tag: Python
Recalculate Adobe Target Result
Go beyond the basics and unlock powerful insights by integrating Adobe Target with Adobe Analytics via A4T, to analyze testing and personalization activities using a wide range of metrics. Addressing the limitation of the A4T solution by leveraging Data Warehouse and Data Feed to perform offline calculations using Python to perform Welch’s t-test and proportion…
Converting event_list And post_event_list In Adobe Analytics Data Feed
The event_list and post_event_list columns are heavily encoded and impossible to use directly. Showing how to convert the encoded event_list and post_event_list columns in Adobe Analytics Data Feed into meaningful event names and split them into individual columns using Apache Spark to make the data more interpretable and easier to analyze.
User Retention By Days Of Access
Instead of the traditional day since the last visit or cohort analysis, calculating user retention based on the number of days users access a portal or app over multiple visits provides a more comprehensive understanding of retention. Using PySpark with Adobe Analytics Data Feed to extract the required data, calculate the weekly and monthly access…
Rebuilding Adobe Analytics Full Path Report With Spark
The full path report is missing in the new Analysis Workspace in Adobe Analytics and rebuilding using Apache Spark. However, we can rebuild it using Apache Spark and data from the Adobe Analytics Data Feed, by reading the hit data, filtering valid page names, grouping by visit, ordering the page sequences, removing duplicates if needed,…