Understanding Null Dimensions
Sometimes when we look at results by a certain Dimension, we'll see that one or more of the lines have no content in them:
There are a few reasons why this might be expected, and there are ways that we can use Analytics to improve the reporting if needed.
Some Fields are Expected to be Null
For example, in Analytics we can report on items by Matrix, to gather all sizes and colours into one "style", but if the items are not Matrix items, their relative matrix is expected to be null.
We can improve in this particular reading by using the Matrix or Item Custom Dimension
Additionally, some fields may not need any definition:
- Not all Sales Lines will have Discount descriptions
- Not all Customers will have a "Title" or "Type"
- Not all Order will have a "Received" date
Not all Data in Lightspeed is defined.
When anyone creates data in Lightspeed Retail, often there will be some fields that are not fully filled out: mostly on Items or Customers.
- Sometimes items will be created without Categories or Vendors.
- Not all Sales will have identified Customers.
- Not all Customers will have filled out addresses
A few things we can do
If we want, we can actually filter our Analytics report just to look at the null instances, and then add another Dimension to extract the data.
Let's say we find a null Top Level Category in our results:
We could add the Top Level Category as a filter:
and filter for "Top Level Category" is null:
So now, if we add in an identifier Dimension, such as Description or System ID:
...we can now get a list of the Items that may need some action in Retail:
Lightspeed Analytics and reporting consultant