Nov 10, 2018

Segmentation and context ­­for data analysis

Segmentation and context are 2 techniques of ­­data analysis

Segmentation allows you to isolate and analyze subsets of your data. For example, you might segment your data by countries so that you can see which contry is responsible for an increase in
traffic. Drilling down to look at segments of your data helps you understand what caused a change to
your aggregated data.


Adding context to your data is another analysis technique that’s really important. Context helps you understand if your performance is good or bad.

There are two ways to set context ­­ internally and externally.
● Externally, context can come from industry benchmark data. This can help you understand how you perform relative to other businesses similar to yours. For example, external context makes it easy
to see if an uptick in your business is due to a general growth trend for your sector, or is just
specific to you.

● Internal context helps you set expectations based on your own historical performance. For
example, you use historical data as a benchmark and set your key performance indicator targets in
your measurement plan.