Important decisions should be informed by data. And one of the most common ways of displaying data is by using graphs to better visualize relationships. Graphs can be powerful tools to compactly illustrate patterns. But the type of graph and the visual elements selected can lead to (usually) unintentional misinterpretation. For example, we have written
To understand problems on a website, nothing quite beats watching users. The process provides a wealth of information both about what users can or can’t do and what might be causing problems in an interface. The major drawback to watching users live or recordings of sessions is that it takes a lot of focused time.
In the era of big data, the issue is less about not having enough data, but about deriving enough meaning from the data you have. Techniques and tools that help quickly identify patterns and insights to help improve user experiences are becoming increasingly important. For years we have helped companies benchmark the overall website user
Understanding who your users are and what they think about an experience is an essential step for measuring and improving the user experience. Part of understanding your users is understanding how they are similar and different with respect to demographics, psychographics, and behaviors. These groupings are often called clusters or segments to refer to the
Visualizing data can make digesting numbers easier and ideally lead to more efficient and accurate decisions. Graphs and visualizations aren’t without their risks, though. The choice of scales, graph types, and styles can all have an effect, for better or worse, on how your audience interprets the findings. Here are 10 best practices (and what
People are wary of statistics. And if people think you can show anything you want with statistics, then this cynicism certainly applies to statistics graphs, too. For example, a few years ago the following graphic made its way around the Internet as an example of graphic abuse. Readers balked at what they saw as a
One of the best ways to make metrics more meaningful is to compare them to something. The comparison can be the same data from an earlier time point, a competitor, a benchmark, or a normalized database. Comparisons help in interpreting data in both customer research specifically and in data analysis in general. For example, we’re
Surveys are one of the most cost effective ways of collecting data from current or prospective users. Gathering meaningful insights starts with summarizing raw responses. How to summarize and interpret those responses aren’t always immediately obvious. There are many approaches to summarizing and visually displaying quantitative data and it seems people always have a strong
