What Caused This uses machine learning and statistical methods to automate the process of identifying root causes of change.

Example output:

Auto Insights identifies the top 5 most likely causes for Sales to increase, decrease or remain stable in a given time period.


A. Auto Insights identifies the top factors to explain changes

In order to identify which segments within the dataset can be used to explain changes, Auto Insights uses Ensemble Learning which combines several Machine Learning techniques into one analytical model in order to decrease variance (bagging), bias (boosting), and improve outcomes (stacking).

Examples of machine learning algorithms that are used:

  1. Random Forest

  2. Named Entity Recognition (NER)

  3. Cramér's V

Other algorithms are used to take into account the context of the changes in question, in order to remove results that are not sensible.

What Caused This will exclude segment with 2,000 or more levels from the selection criteria.

B. Users declare top factors to explain changes

Users can enrich and tailor the results based on their business needs by setting segment relevancy as below.

The below selection would overwrite Auto Insights' top factors selection.

C. Identify top levels within these factors to explain changes

Auto Insights looks at the variance & contribution of each level within a segment to determine which levels to call out.

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