Metric Component Analysis: Doing Analysis

This is part 6 of our series on Metric Component Analysis, previous segments are available in our archives.

Last week we explored how breaking down metrics into components made it easy to find the drivers when our metrics changed. However, in every case I magically knew exactly which dimensions to look at! In the real world, the hardest part of solving these mysteries is finding the right dimensions to analyze.

If you think about any metric in your business there are dozens of different dimensions you might analyze. For example, you might be able to analyze Revenue by Country, Product, Customer, Channel, etc. Most metrics will have far too many dimensions for you simply to look at them all.

Luckily, there are a number of powerful statistical tools you can use to analyze your data and identify the most important dimensions. They have fancy names like Principal Component Analysis and Latent Class Analysis, but they all focus on taking high dimensional data and extracting only the most important dimensions. We will not go into heavy detail but I will make sure you understand how they work so you can make an educated decision about which tools you should use.

This is a short week, so we will highlight two of the most important techniques. Specifically we will cover:

Tomorrow we’ll start with a high level description of how dimension reduction works.  

Do you do look for metric drivers in your business? Outlier is a product designed to help! Outlier looks deep into the dimensions of your data to identify the drivers and emerging trends that result in changes to your main KPIs. If you’re interested in seeing a demo, schedule a time to talk to us.


Quote of the Day: “We’ve taken the world apart but we have no idea what to do with the pieces.” ― Chuck Palahniuk