Michael Beasley

Traffic Analysis: Learning How Users Got to Your Website

You can learn about who your users are through visitor analysis and begin to understand why they may have come to your website through traffic analysis. These analyses are exploratory in nature—you describe the categories that your users fit into in terms of size and how those different categories behave, and look for patterns that emerge. Web analytics allows you to delve in to how many times users visit your website, and how many days have passed since their last visit. You can find out how much time users spend on your website and how many pages they view, where they are located, and what sort of device they use to access your website. This chapter discusses two approaches to analyzing the keywords that your users searched for: the quick way of analyzing the most frequently searched keywords (the “head” of the long tail), and how to approach the full breadth of keywords.

Learning about Users

You can learn about who your users are through visitor analysis and begin to understand why they may have come to your website through traffic analysis. These analyses are exploratory in nature—you describe the categories that your users fit into in terms of size and how those different categories behave, and look for patterns that emerge. Web analytics allow you to delve in to how many times users visit your website, and how many days have passed since their last visit. You can find out how much time users spend on your website and how many pages they view, where they are located, and what sort of device they use to access your website.

Chapter 16 – Web Analytics in the Near Future

Web analytics should be a core competency for UX professionals. These tools are useful now, but will certainly grow in power in the years ahead. Usage of mobile applications can be measured with web analytics technology, and as the number of mobile applications grows, so will the power and quality of mobile application analytics. Users currently may use the same website with multiple devices, and this behavior is likely to become more common, and web analytics vendors will try to find better ways to measure channel switching. Web analytics tools will get better at measuring on-page behavior and integrating data from other sources. It is likely that, for now, Google will continue its dominance of the web analytics scene.

Measuring the Effects of Changes

This chapter is about looking at the analytics data for your website before and after a design change to determine the effectiveness of that change according to something that web analytics can measure. The key to measuring the effectiveness of design changes is thinking about a broader set of user actions as though they were conversion rates. Before you can measure whether a design change was successful, you must decide what to measure—what aspect of users’ behavior did you hope to influence with a design change? You also must choose two time periods for your analysis: before and after the design change, with the former being the longest time period possible without any major changes to the website. The amount of time after the design change will depend on traffic volume and how drastic the change was. You will frame the question as, for a given time period, how many users had the opportunity to take the desired action, and how many actually did. You will then compare this rate before and after the design change.

Web Analytics Approach

This chapter introduces the analysis approach used throughout this book: posing a question, gathering data, transforming data, analyzing, and answering the question. It also covers the need to balance time constraints against the desire for deeper analysis, and documenting one’s work so it can be recreated and verified by others. Web analytics tools are ill-suited to helping you understand how a website is laid out and how pages link to each other, so you should get to know what your website looks like outside of the web analytics tool. Analyze data in context—you can only interpret numbers in contrast to historical data on the same website, and trends and proportions are more important than absolute values. Always remember that even when the data contradict your theory or do not provide a conclusive answer, you have at least learned what approach not to take.

Regular Reporting and Talking to Stakeholders

This chapter focuses on using web analytics to communicate with stakeholders. A UX team can continually monitor the state of the user experience on the website and report on this status to business stakeholders as part of a regular communication plan. You must select a handful of metrics that provide the best summary of how well the website is serving users. Reports using analytics data should be concise and include enough context to make the numbers meaningful. Web analytics data can help you make the case for doing usability activities by showing stakeholders how many users will be affected by your work, and perhaps even allowing you to estimate how many additional users will benefit from this work.

Chapter 12 – Measuring Behavior within Pages

This chapter describes ways to analyze the behavior of users within a page in contrast to their movement between pages. It first looks at the out-of-the-box functionality in Google Analytics. The “In-Page Analytics” report annotates the page you are looking at with the portion of clicks each link has received in the selected time period. At the time of writing, it only displays clicks on links to other pages and can’t differentiate between multiple links to the same destination page or on JavaScript or HTML form elements. This chapter then briefly looks at tools that can record anywhere that users click on a given page. These tools record the coordinates of every place that users click on a page, whether it is a link or not, as well as users’ keystrokes. They then allow you to either view, on a page-by-page basis, the total number of clicks at each coordinate for a given time period, or to play back an individual user’s session on your website as they move from page to page, recreating their clicks and keyboard input. Lastly, it is possible to configure page tagging tools to measure practically any interaction users have with your website through the addition of the right code. The last approach, though the most technically complex, may offer you the most rewarding possibilities for analysis because the data will be integrated with the rest of your web analytics data.

Chapter 9 – Segmentation

Segmentation is the filtering of data according to metrics and dimensions so you can analyze specific subsets of your website’s users. It allows you to answer more in-depth questions about user behavior by allowing you to focus only on particular users who exhibit certain behavior or traits, or to compare one set of users against a different set. There are basic kinds of segmentation in standard analytics reports, like dividing up usage metrics by what keyword users searched for, but this chapter covers how to create your own segments. After a guide to Google Analytics’ advanced segments feature, this chapter surveys useful ways for segmenting data to analytics questions, such as “How did whether or not users viewed a specific page affect their behavior?” or “How does the behavior of users who fit my primary persona differ from other users?”

Chapter 4 – Goals

In web analytics tools, goals are a way of measuring actions that users can take that indicates the website is successfully serving the needs of the organization. Rather than constantly measuring every possible metric, goals are strategically chosen to indicate whether the website as a whole is working. It is essential for the credibility of UX professionals that we show how our work affects the performance of our organizations by ensuring that their web analytics goals are aligned with user needs and then tying UX work to the improvement of these metrics. A major concept for effectively using Google Analytics is the conversion rate, the amount of users who successfully complete one of those strategically chosen actions divided by the total number of users in a population. By measuring how this rate changes over time we can understand how changes to a website affect users’ ability to complete important actions. This chapter explores reports and methods for studying this metric.

How Web Analytics Works

Google Analytics is a web analytics tool that works by page tagging—that is, adding a piece of JavaScript to the HTML of a website. This code sends information about the page to a database that assembles the data. Page tagging tools won’t capture every single page load, but they are consistent enough that we can still find web analytics data useful for analyzing trends. More importantly, they are much easier to set up and use than tools that pull data from web server logs. Data about users and visits are captured as metrics and dimensions—metrics are aspects of users’ behavior that can be measured as numbers, such as how many pages they look at, and dimensions are attributes of users and their visits that can be used to categorize users, such as what keyword they searched for or whether they are new or returning.