In this lesson, we'd like to cover two techniques - segmentation and context -

that we believe are critical to good data analysis.

First, let's talk about segmentation.

Looking at aggregated data helps you understand overall user behavior trends,

like how their purchase patterns change over time.

But in order to understand why purchase patterns changed,

you need to segment your data.

Segmentation allows you to isolate and analyze subsets of your data.

For example, you might segment your data by marketing channel

so that you can see which channel is responsible for an increase in purchases.

Drilling down to look at segments of your data

helps you understand what caused a change to your aggregated data.

All reports in Google Analytics provide segmentation of your traffic.

For example, take a look at your traffic sources report.

Each row in the table shows how a specific traffic segment performed.

This let's you compare different segments and understand which sources are bringing

in the highest value traffic.

Let's talk through some common segments that you might want to consider when looking at

your own data.

You can segment your data by date and time,

to compare how users who visit your site on certain days of the week

or certain hours of the day behave differently.

You can segment your data by device

to compare user performance on desktops, tablets and mobile phones.

You can segment by marketing channel

to compare the difference in performance for various marketing activities.

You can segment by geography

to determine which countries, regions or cities perform the best

And you can segment by customer characteristics,

like repeat customers vs. first-time customers,

to help you understand what drives users to become loyal customers.

In addition to segmentation,

another analysis technique that's really important is adding context to your data.

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.

Throughout this course we will talk about how you can use segmentation and context

when working with Google Analytics data or other digital analytics data,

so keep these techniques in mind for future application.