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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 not the final result; it is the learning experience about your customers – a chance to understand what they like and dislike, which ultimately will lead to more or fewer conversions.

      The web analyst must try endlessly and learn to be wrong quickly, learn to test everything and understand that the customer should choose, not the designer or the website manager. Experimenting and testing empowers an idea democracy, meaning that ideas can be created by anyone in the organization, and the customers (the market) will choose the best one; the winner is scientifically clear.

      Following are a few tips when it comes to website testing:

      ● Testing is not limited to landing pages: It should be implemented across the website, wherever visitors are abandoning it and wherever the website is leaving money on the table.

      ● Try your tools (and your skills) with a small experiment: Sometimes it is wise to start small and then grow. Once you are familiar with your tools, try a test in an important page but for a small (or less profitable) segment. Then head for the jackpot!

      ● Measure multiple goals: While you improve macro conversions, you might be decreasing registrations or newsletter signups, which might have a negative impact in the long run.

      ● Test for different segments: Segments such as country and operating systems can have completely different behaviors, so the tests should also be segmented in order to understand those differences.

      Google Analytics offers an A/B testing feature called Content Experiments; learn more about it at http://goo.gl/HTGX2d.

6. Implement Insights

      No insight implementation is a synonym of no web analytics. If you go through all the preceding steps but cannot actually implement the results on your website, it is as if you did nothing. Following are some tips that can help you overcome implementation bottlenecks:

      ● Get C-level support: This will be essential if you come to a point where organizational priorities must be set and resources allocated.

      ● Start small: As mentioned previously, starting small helps to set expectations; people understand the tools and what is required from them.

      ● Be friendly: Being a nice person is always helpful; that's the way human nature works.

      Implementing and Customizing Your Code

      If you are implementing Google Analytics for the first time, you will see a wizard that will guide you to retrieving the appropriate tracking code to use, right after creating an account. The first choice: what would you like to track, a website or a mobile app? If you choose a website, you will get a JavaScript code to implement on it; if you choose an app, you will get links to download either the Android or iOS SDKs.

      If you miss the previous step or would like to find your tracking info at a later stage, you can find this page by logging into Google Analytics and clicking on Admin on the top of any page. This will lead you to the Administration panel where you can find an item named Tracking Info.

      While implementing the default code on your website or app will provide you with important information about customer behavior, other code customizations might be required to accommodate your business needs. In the next section, I describe the customizations that I believe to be the most important; for a comprehensive and detailed description of all customizations available, visit http://goo.gl/t1Td5T.

      Implementing Google Analytics Through Google Tag Manager

      If you are an experienced analyst/developer/marketer, you are probably asking yourself, “When is he going to start talking about Google Tag Manager?” A great question! In this chapter I focus my attention on the Google Analytics methods that should be used when enhancing your implementation, regardless of how you choose to actually implement them.

      As you might already be aware, Google Tag Manager is a powerful and scalable way to organize your Google Analytics (and other tools) implementations. It will make updates easier and cleaner, and it will transform you into a hero. Here are a few resources you should consider when implementing Google Analytics through Google Tag Manager:

      ● The official Google Tag Manager Help Center: http://goo.gl/1uXK90

      ● The official Google Tag Manager Developer documents: http://goo.gl/CPTYH6

      ● Google Tag Manager Step-By-Step Guide (Web): http://goo.gl/lBiX6t

      ● Guide to Google Tag Manager for Mobile Apps: http://goo.gl/ib3LL7

Cross Domain Tracking

      If

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