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 departments with different backgrounds make decisions in companies. If KPIs are complex and hard to understand, it is unlikely that decision makers across the company will use them.
• Relevant: Each company has its unique objectives; therefore, it should also have its own set of KPIs to measure improvement.
• Timely: Even excellent KPIs are useless if it takes a month to get information when your industry changes every week.

By following the definition of the business objectives and the metrics that will be used to measure them, you will be in a much better condition to collect the data that will be needed.

3. Collect Data

When any company starts to collect website or app data, two questions should be asked:

• Is my data accurate? If your data is not accurate, it is like building an empire in the sand; your foundations can be shaken too easily.
• Am I collecting all the data that I need? If data is not collected, you will not be able to understand customer behavior properly.

You will learn more about Google Analytics data collection techniques in the following sections, so I will keep this step succinct.

4. Analyze Data

Data analysis is a rich field, which goes from simple filtering, sorting, and grouping to advanced statistical analysis. In this book you will learn about ways to analyze data using several Google Analytics reports and features, but the following are some general ideas that can help you go from data to insights:

• Segment or die: Segmentation is an essential technique when it comes to analyzing customer behavior. By segmenting your customers into meaningful segments, you will be able to optimize their experiences more easily and effectively.
• Look at trends, not data points: It is critical to look at your metrics over time to understand if the website results are improving or not.
• Explore your data with visualization techniques: You can chose from an endless pool of graphs and tools to visualize numbers. Exploring data with charts will uncover patterns and trends that are hard to find by crunching numbers.

It's important to note that data analysis can lead to three different outcomes (as shown in Figure 1.1):

• To discover an insight for implementation, such as a bug or a page that does not convert for an obvious reason.
• To develop a hypothesis regarding a low converting customer touch point that will lead to a split test.
• To come to an understanding of a data collection failure: Important data can be either missing or inaccurate.

5. Test Alternatives

There is an African proverb that says, "No one tests the depth of a river with both feet." In the same spirit, it is very unwise to change your website without first trying with the tip of your toes. When you test, you lower the risk of a loss in revenue due to a poor new design, and you bring science to the decision-making process in the organization.

But the most interesting outcome of experimenting is...