Website analysis
UX research based on quantitative data of White Paper sites
Client
Foundever
Role
Principal UX Researcher
Summary
Following the implementation of websites for Foundever’s white papers, I was asked to assist the project team with user data analysis. Using Crazy Egg analytics, I evaluated site visits and provided insights to a team lacking an UX Researcher.
Key tasks included:
- Analyzing Crazy Egg data
- Creating a report with recommendations
- Teaching the team data analysis for future self-reliance
Data analyzed included user browsing data, device usage, and engagement metrics, and more. With limited time, I focused on one website to create a comprehensive report that could serve as an example.
The report, structured as a reusable template, covered Audience, Navigation, and Recommendations. It revealed insights such as the timing of visits and content engagement levels, alongside navigation and readability issues.
I concluded with a dozen improvement suggestions and held a session to guide the team in performing their own analyses. I emphasized qualitative research methods and the importance of continued data-driven decision-making for ongoing projects.
Achievement
Empowering Team Skills in Quantitative Research:
I trained a team of designers in quantitative research. By teaching them how to extract and analyze meaningful data using analytics tools like CrazyEgg, I equipped them with the keys to independence. With these skills, they can now optimize their decision-making based on relevant data.
Context
White paper website – 1st page full view
Project
Following the design of websites containing Foundever’s white papers, the project team asked me for help with the research and analysis of usage data.
I had access to the data from an analytics tool set up on the websites, Crazy Egg, which allowed me to analyze website visits.
Problem
The project team had a lot of data from Crazy Egg, but without an UX Researcher, they did not know how to use this data.
Objectives
The objectives were multiple:
- Analyze the data provided by Crazy Egg.
Write a report with improvement recommendations.
Teach the team how to analyze this data so they can do it themselves in the future.
Research
White paper – Some data provided by Crazy Egg
Data provided by Crazy Egg
Many different types of data
With the implementation of Crazy Egg, the project team was able to provide me with extensive data for each page of each site, including:
- Browsers
- Countries and languages
- Days and times of visits
- Devices
- Number of visits per page
- New versus returning visitors
- Referrers
- Scroll maps
- Heatmaps
- Dead clicks
- Time before clicks
And more.
Short-term Research
Choices to Make
As it wasn’t a priority project at the time, I only had one day to work on this project: conducting the data analysis and writing the report.
Therefore, I decided to focus on just one of the white paper sites, quickly review all the data for each page, and then focus on the data from the first page and one of the last pages.
This allowed me to calculate the differences in data between the first page and the last pages, in order to draw new data such as bounce rates, retention differences by device, etc.
Then I was able to produce a comprehensive report focused on one site.
White paper – Some data provided by Crazy Egg
Report
White paper – Report
A Template Report
I chose to write this report as an example, with explanations for each calculation, the reasoning behind it, and how to use this data. The goal is for this report to serve as an example, a template, so that the project team can create future reports on their own.
To achieve this, I divided the report into three main sections in which I explain what data to use and how:
- Audience
- Navigation
- Conclusion and Recommendations
Audience
Define an audience based on this data
In the first part of the report, I rely on the following data to define the audience:
- Visits by device
- New vs. Returning
- Day of the week / Time of the day
- Referrer
- Time to click & Time before leaving the page
By analyzing this data, I noticed that visitors:
Mostly visit from a computer (over 70%), on a weekday, primarily between 11:30 AM and 2 PM or between 9 AM and 10 AM, come from a Foundever link or a LinkedIn post, and partially return to the content (52% on the first page). They abandon reading before finishing the content, with an 85% drop-off rate between the first page and the end.
With this data, I was able to hypothesize the following:
Most visitors work on a computer and view this content either when they arrive at the office or during their lunch break. Some of them already know Foundever (they come from Foundever sites or have seen their posts on LinkedIn).
However, these visitors do not necessarily read the entire content.
Navigation - Data
Analysis of navigation through quantitative data
To understand visitor navigation on the site, I analyzed the following data:
- Heatmaps
- Dead Clicks
- Scrollmaps
- Number of visits per page
- Links present on the pages
- Time to click & Time before leaving the page
This data revealed some insights:
- Navigation elements are widely used, but the footer and the link to a contact page are very rarely used.
- Some texts that are too long and poorly formatted are often not read in full.
- The second-to-last page of the white paper, which contains the conclusion, is almost never read
- The last page containing the contact form is not visited.
Navigation - Review of the site
Analysis of navigation by ergonomic audit
I then conduct a quick ergonomic audit to analyze the construction of the pages, images, texts, menus, behavior in the browser, etc. I identify several problematic points such as:
- A missing favicon: the site is not easily recognizable at a glance, and the visitor has to read the name to find this tab among others.
- A lack of internal links and few links to other Foundever sites: this poses a problem for SEO but also represents a missed opportunity to encourage visitors to explore other Foundever content.
- Readability issues: cropped images, poor text hierarchy, overly long text blocks, unformatted text, etc.
Conclusion
In the conclusion of the analysis, I demonstrate how the analyzed data helps identify issues by listing them and showing how to better detect them.
I explain that analyzing the audience and their habits should aid in content design. In this case, the audience visits the site in the morning when they arrive at the office or during their lunch break, so they cannot afford to read a white paper like this one, which is over 5400 words long and takes 23 minutes to read (based on a silent reading speed of 238 words per minute).
I also explain how the eye scans texts according to text formatting, and here, long and unformatted texts lead to an F-pattern reading. Consequently, readers read less and less of the sentences as they progress through the text.
I also summarize the navigation issues and explain how to better detect them.
Recommandations
Based on all the analyses conducted, I propose at the end of the report several improvement recommendations on various aspects:
- Content
- Readability
- Navigation
- Design
UX evangelization
Report Presentation
Teaching How to Conduct Research
I suggested to the project team to have a meeting where I would present the report to them, but more importantly, show them how I analyzed the data so they can conduct future analyses themselves.
During this meeting, I went through each part of the report, explaining which data to use, when, why, how to calculate bounce rates, how to define the audience, etc.
This meeting lasted 2 hours and enabled the project team to take ownership of the subject and be able to conduct future research and analyses on their own.
What next?
Qualitative research?
During this meeting, I encouraged them to conduct more research, including qualitative research, by carrying out interviews and surveys, which can be two simple and accessible methods for this project.
Having already written documentation for Foundever on how to create a good survey, and templates for interview guides, I offered to share these with them if they wished to do more in-depth research.
I remain in contact with them and answer their questions regarding UX in general.
Future Challenges
Continuation of Future Research
The biggest challenge for this team will be to maintain this momentum and continue conducting research of this type. Since this is not part of their regular responsibilities, relying on just one person to manage it creates a risk if that individual decides to step away from the task or leaves the team.
I believe it would be advantageous to integrate this research into every project the team handles, and to rotate the responsibility among different team members. This way, everyone will have the opportunity to become familiar with this aspect of the work.
