Settings

DataMa Pivot include a series of parameters in the left menu, that you may have to use to refine or better visualise your analysis.

Most of them are editable through Shiny Web App, but you can also edit them through R code with references below (by those who have a « develop » license)

 

  • Definition: This includes definition of the KPI you are analysing. This KPI is a ratio (e.g. Revenue / User)
    • Primary numerator (Primary numerator): This can be whatever dimension is in your source, depending on your use case. Typically Revenue, Margin, …
    • Primary denominator (Primary denominator): This can be whatever dimension is in your source, depending on your use case. Typically User, Purchase, Session, Margin,…

 

  • Filters:
    • Excuded dimension (Excluded_Dimension): Dimension you want to exclude from the analysis
    • Filter segment (Dimension_filter and Filter): Segment within a given dimension that you want to filter on
    • Dimension excluded from Summary View (Dimension_filter_SimplifiedView) : A dimension you don’t want to consider within summary view, for some reason.
  • Settings:
    • Tree depth: level of depth of the decision tree in the Dimension Importance window >> Decision Tree tab
    • Level of aggregation (NumLimit): The level of aggregation that the model is using – e.g. if Level of aggregation is set at X%,  segment within each dimension that represents less than X% of the Primary Numerator (e.g. Revenues) of the main KPI you’re analyzing will be clustered in one « Other » segment. X is set at 2 by default, but you may want to play with this parameter quite a bit because it can change significantly the calculation of mix effects.
    • Continuous clustering depth:

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