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  • There are many different ways to analyze data.

  • 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.

There are many different ways to analyze data.

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B1 中級

數字分析基礎--第2.2課 核心分析技術 (Digital Analytics Fundamentals - Lesson 2.2 Core analysis techniques)

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    patty 發佈於 2021 年 01 月 14 日
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