We’re deep in the midst of the age of the customer. If there’s one thing we’ve learned as the Ogilvy Experience Design practice, it’s that a customer-centric approach to websites is key to a successful digital transformation strategy. That’s why it’s critical for businesses to take very fine and calculated steps to ensure the best user experience (UX) possible.
Why does user experience matter? With the abundance of options available to consumers, they will quickly abandon websites that are difficult to use or are missing key information or functionality in favor of those that are more friendly and efficient. This applies not only to B2C experiences but to B2B experiences as well. So how do you know whether your site is addressing user needs? The most effective way is to use qualitative and quantitative data to inform and validate your UX design process.
DEFINING QUALITATIVE AND QUANTITATIVE DATA
Before we dive into how to use these two data types together successfully, let’s define them.
In the world of UX, qualitative (qual) data is nonnumerical, a direct assessment of a product or interface. It is collected through observation and is good for identifying the problems of a product or interface. Examples include usability testing, ethnography, and focus groups. The strength of this method lies in the ability of interviewers to ask follow-up questions after a notable interaction occurs. This allows us to drill down to the root cause of an issue and therefore come up with a solution to the problem. The nature of this data type allows us to dive deep to understand the “why” behind user behavior.
Quantitative (quant) data for UX applications is collected indirectly, often in the form of predefined actions taken by users, such as task metrics (time on task, success/fail rate, etc.). In most cases, website analytics are the most common way of collecting quant data at scale to give analysts a macro view into how a site is performing and how users interact with the site. This data type allows us to understand “what” actions users are taking.
While each type of data has its strong suit, it’s important to note that each also has its limitations. In the case of website analytics, quant data can identify usage behavior trends but requires data from large sample sizes collected across a time period to get statistically relevant results. Although it can show which designs or website pages are performing better than others, it cannot determine what changes need to be made to increase satisfaction or performance.
This is where qualitative data comes into play. Qual data can be gathered faster with much smaller sample sizes (typically five to eight interviews) and can illuminate the specific problems of an interface and inform design decisions. However, because of the conversational nature of qual research methods, representative user groups must be recruited and interviewed to yield relevant insights. This can be challenging and expensive with groups that are specialized or difficult to reach.
In short, qual data can be thought of attitudinal, whereas quant data is behavioral. To design easy-to-use experiences that meet user needs, both types of data are needed. Say for example that the site analytics (quant data) from an e-commerce site reveals that a large number of users are dropping off during checkout at a certain page. Although this is certainly useful to know, we can’t make meaningful changes to the page without first knowing what is causing the drop-off. Performing usability testing (qual data) of the page identified in the site analytics allows us to gather insights regarding the problems users are encountering on the page and drill down to the root cause of the issue.
Armed with qual and quant insights, we can make updates to the page to fix the problem and reduce drop-off.
Let’s dive a little deeper and take a look at a recent website redesign project.
The Experience Design team at Ogilvy recently worked with a financial services company that wanted to transform its website in a more user-centric experience, rather than having the content and structure be business-driven. After aligning with the client regarding the objectives and outcomes of the engagement, we dove into the analytics of the existing website. By analyzing users’ paths through the site as well as conversions to email subscriptions and contact forms, we were able to uncover how users traversed the site and where they were dropping out of the site.
As we culled our way through the data, a few key trends emerged:
- Blog and thought leadership content were key drivers of traffic on the site. More than half of users arriving at the site did so during topical research not specific to the brand of the company.
- Users often left the site after only one or two page views, with the majority of bounces happening on pages that displayed daily financial statistics.
To give us more insight into the quant findings, we conducted user interviews to determine the cause of this behavior. It became clear during this research that users were coming to the site not only to engage with specific content (such as a blog post or white paper) but also to find out how they could apply the learnings to their businesses and reach out to someone at the company.
Because of the siloed design of the site, it wasn’t easy to view more content or learn about the company’s products or solutions related to the content. A key objective of the site was to generate leads and educate users about the company’s products, two things it wasn’t doing well in its current state.
With these insights, we worked with the design team to come up with a road map to increase engagement with other areas of the site, specifically product pages and email subscription sign-ups. Some of our suggested updates included linking high-traffic content (such as blog posts) to relevant product offerings as well as including easy-to-access subscription and contact calls to action (CTAs) across the site. This allowed us to take advantage of the high-traffic areas of the site by offering users a next step that was relevant to their needs that we uncovered in the qualitative research.
When undertaking a task as large as redesigning a website, it’s important to understand quantitatively how the current site is performing and qualitatively whether it meets the needs of users. If not, where are the deficiencies and pain points? What is causing them? How can we create a new experience that reflects what the user needs?
Using the strengths of both qual and quant data is much more effective in identifying the problem areas of the site, diving deep into the root cause of problems, and empowering design teams to develop solutions that would resonate with users.
Author: Bryan Manis, Senior Experience Researcher
As a Senior Experience Researcher at Ogilvy, Bryan uses a wide range of methodologies to help clients understand user needs, build personas, align stakeholders, and evaluate concepts. He has worked with Fortune 500 companies both domestically and internationally across a wide range of industries including consumer goods, finance, automotive, and healthcare.