What Caused This

Last modified: November 22, 2022


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 6 most likely causes for Sales to increase, decrease or remain stable in a given time period.

What caused this example output


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 segments 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.

top factors selection

C. Identify top levels within these factors to explain changes.

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

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'What Caused This' Change Settings

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