Google Analytics Integrations - Waisberg Daniel

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export data out of Google Analytics are also absolutely amazing, and they can be used to create powerful and customized solutions to businesses. They are just not in the scope of this book.

      Since integrations are not that useful if the underlying data is inaccurate, I decided to start with an introductory chapter about implementation best practices. This chapter provides the most important information you need to know when implementing Google Analytics.

      Following the chapter on implementation best practices, the book is structured in two main parts. Part I, “Official Integrations,” discusses the Google Analytics official integrations – AdWords, AdSense, Google Play, iTunes, Webmaster Tools, and YouTube. Part II, “Custom Integrations,” discusses ways to bring custom data into Google Analytics, mostly using the Data Import feature and the Measurement Protocol.

      How to Contact the Author

      In this book, I provide practical advice on integrating Google products and external data into Google Analytics, with detailed information and screenshots. As you probably know very well if you are reading this, the Google Analytics team is constantly improving the tool and adding new functionality to it, which means you might not see exactly what I saw when writing the book. If that is the case, feel free to send me a note through the contact form at http://danielwaisberg.com/connect.

      Chapter 1

      Implementation Best Practices

      On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

– Charles Babbage, Passages from the Life of a Philosopher

      Charles Babbage's quote is a succinct explanation of the term GIGO (garbage in, garbage out), which, in decision sciences, is commonly used to describe situations where inaccurate data is fed into a model, resulting in the production of equally inaccurate results. The same is true in this book's context: You must make sure you are collecting accurate data before you start using it.

      In order to use Google Analytics as a decision-making tool, companies cannot afford to rely on partial, inaccurate, or otherwise misaligned data. Google Analytics must be set up properly to meet the measurement needs and business objectives of companies.

      In this chapter you will learn some of the most important steps in order to have clean, organized, and accurate data. The chapter is divided in five sections, each representing a step when it comes to implementing Google Analytics in a website or app successfully:

      1. Understanding the web analytics process: Before you implement Google Analytics, it is important to understand how the data will be used and how the collection and analysis of data relate to other business areas. This will help you decide on the data needs of your company and which metrics will be used to measure success.

      2. Implementing and customizing codes: Once your data needs and success metrics are defined, you should start looking for the necessary Google Analytics customizations to implement on your website or app.

      3. Setting up the Google Analytics interface: Following the code implementation, you will need to set up the Google Analytics interface to make sure it processes your data in the way you want.

      4. Tagging inbound traffic: In order to accurately measure all your website or app traffic, especially marketing campaigns, you will need to tag inbound links with custom URL parameters called UTMs.

      5. Managing the implementation: To ensure that your implementation is always tidy, you should always keep track of changes on your Google Analytics account.

      Please note that this chapter does not intend to provide a comprehensive description of Google Analytics implementation methods and capabilities; rather, it focuses on the most important aspects required to build an accurate and organized data collection.

      Planning Your Implementation

      The objective of web analytics is to improve the experience of online customers while helping a company to achieve its results; it is not a technology to produce reports and spill data. Web analytics is a virtuous cycle that should never start with data collection; collecting data is a means to an end.

The diagram in Figure 1.1 shows a process you can use to implement web analytics in your company. It is not the process; it is a process. Each company should find the process that works best for it, but this is a simple process that might work for you.

      1. Start with a clear definition of business goals.

      2. Build a set of key performance indicators (KPIs) to track goal achievement.

      3. Collect accurate and complete data.

      4. Analyze data to extract insights.

      5. Test alternatives based on assumptions learned from data analysis.

      6. Implement insights based on either data analysis or website testing.

Figure 1.1 The web analytics process

      This book focuses on steps three and four of the process in Figure 1.1: collecting and analyzing data. However, it is important to take a step back, before we dive into the bits and bytes of data, to remember that data should not live in a silo; it should be strongly linked to business and customer needs. Below you will learn a little about each of the steps shown in Figure 1.1. Following this section you will dive deeper into the technical aspects of Google Analytics implementation best practices.

1. Define Business Goals

      This is the first step when it comes to understanding and optimizing a website or app: You must understand your business goals in order to improve it. The answer to the following question is critical in defining your goals: Why does your website or app exist?

      Each website or app will have its own unique objectives. For some, the objective will be to increase pages viewed in order to sell more advertising (increase engagement); for others, the objective will be to decrease pages viewed because they want their visitors to find answers (increase satisfaction). For some, the objective will be to increase ecommerce transactions (increase revenue), and for others the objective will be to sell only if the product fits the needs of the customer (decrease products returns).

      As you can see in the web analytics process proposed in Figure 1.1, the objectives are absolutely necessary in order to start the process. Only after they are defined can you proceed to build the KPIs. It is also very important to constantly revisit the goals in the light of website analyses and optimization to fine-tune them.

2. Build Key Performance Indicators

      In order to measure goal achievement, you will need to create KPIs to understand whether the website results are going up or down. A KPI must be like a good work of art: It wakes you up. Sometimes it makes you happy and sometimes it makes you sad, but it should never leave you untouched, because if that is the case, you are not using the right KPIs.

      And good works of art are rare. You have just a few truly touching works of art per museum, and not every work of art touches the same people. The same applies to KPIs. There are just a few truly good KPIs per company, and each person (or hierarchy level) will be interested in different KPIs – the ones that relate to their day-to-day activities. Upper-management will be touched by the overall achievement of the website's goals; mid-management will be touched by campaign and site optimization results; and analysts will be touched by every single metric in the world!

      Good KPIs should contain three attributes:

      ● Simple: People in several

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