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Linking analytics data with design decisions

Monday, March 10th, 2014 by Servage

Today I focus on web redesigning, as the rapidly changing technologies leads many established website owners to consider a redesign, for many reasons. Generally, when a client approaches us for redesigning their site, we mainly concentrate on their needs and expectations for a new design, while taking note of the current problems they face with the old design. Another thing we look for is current design trends and their competitors’ websites.

Unfortunately, we rarely try to determine actual performance issues of the current website by looking at various analytical tools and user research. If we were to review the old data, via tools like Google Analytics, we would gain insight of great designing, that already favors our clients. Of course, Google Analytics is not only important for redesigning, but also useful in the testing of any new web design project. This is particularly true for medium to large websites that demand more long term attention and expectations for good ROI.

Why We Need Analytics in Designing

Now, the million-dollar question is how will analytics software or services help you make design decisions properly? Well, before answering this, we need to have some knowledge about the data types that analytics software provides. Data though, is always in numbers; while our inferences vary, so we can classify data in two types, quantitative and qualitative. When we draw inferences based on data that explains questions, such as who, when, what and where, the data is quantitative.

Data Types in Analytics

Contrast that to data that provides answers to questions of why and how, which results in qualitative data. In most cases, qualitative data does not tell us anything directly. Instead, you have to draw inferences indirectly, which is why they are sometimes referred to as non-numerical data. Instead of measurements and scales, you will find sense factor or defined properties. In fact, we need both kinds of data to make accurate and useful design decisions.

For instance, with numerical data you will get answers like who (visitors) has come (location, device, etc.) on your website and what they have done on it (user journey) as well as from where they came (referrals and source). However, you won’t see any words or numbers that describe why they behave in particular ways. For instance, no-data will directly tell you why some groups remain on particular pages while other groups show a high bounce rate.

How Analytics Helps

In web design, we need to take different approaches for different page types. For example, we put more emphasis on the design and content quality of a landing page than we do with the About or Contact Us page. Moreover, in designing a landing page, we have a priority of pages that we wish the audience to visit after landing, so we place prominent links and navigating information on landing pages. Therefore, such pages are sometimes termed “exit pages” because their exit rates are always higher, compared to other landing pages. In contrast, some pages are low bounce rate landing pages, because we provide in-depth information right on that page, which keeps visitors engaged longer.

In short, if you carefully review the analytics data of your site, you will find the clues about the content and design quality. Yes design quality, because if your buttons or icons are intimidating or misguiding your visitors, you will observe a higher bounce rate on your informative page.  While the reverse will happen on an exit page if your links are broken or difficult to use. Thus, the user will not arrive on your designated pages, and instead, you will find a reverse bounce rate in user journey.

References & More Reading

Data-Driven Design In The Real World
Web Analytics Segmentation: Do Or Die, There Is No Try!

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