Blog 3: Web Analytics (Module 2)

For this post, I wanted to analyze the concepts from Module 2 and apply them to real-life examples that my classmates and I might encounter. My first example, the web analytics cycle and the five W's of web analytics, comes from my experience in logistics. While I’ve primarily applied these concepts in a different field, I now see the advantages of using them in my current role, especially for optimizing specific tasks. My second example, related to types of web traffic, draws on the recent election, a time when many individuals experienced various forms of targeted communication, such as phone calls, ads on YouTube, and personal research to learn more. For the task completion rate, I was inspired by a quiz question, which made me reflect on how many email subscriptions I’ve signed up for to save 10%, or how often I find myself searching for coupons while checking out online. I also thought about how some stores don’t offer the most user-friendly mobile websites. Applying these concepts through these examples has helped me better understand the material covered in Module 2.

Web Analytics Cycle & Five W’s of Web Analytics

In Module 2: Web Analytics, we explored the Web Analytics Cycle and the Five W's of Web Analytics. The Web Analytics Cycle follows a continuous process of Set Goals → Measure → Report → Analyze → Optimize, which can be applied across various industries, including logistics.  I wanted to apply this concept as an example of how this cycle might play out for a logistics team aiming to improve their technical manuals on their internal website.

• Set Goals: The goal for the logistics team is to increase the number of manual downloads by 15% in the next quarter and reduce the time users spend searching for the correct manual.
• Measure: The team tracks key metrics, such as: Exit rates on pages that are frequently visited but lead to users leaving the site without downloading any manuals.
• Report: If after collecting data, the team discovers that users are spending a long time on the page listing various HR safety guidelines, but the exit rate is high, meaning they aren’t downloading the manual. This might suggest users are struggling to find the right information or aren't engaged enough to continue.
• Analyze: The team now has the opportunity to look deeper into this behavior and realizes that the safety manual page is too cluttered, and users are unable to easily locate the manual they need. Furthermore, the page doesn’t have a clear action, like a “Download Now” button, which could be discouraging them from completing the task.
• Optimize: Based on this analysis, the logistics team decides to reorganize the safety manual page and add more obvious download buttons for each manual.

Now, using the same logistics example, I wanted to apply the Five W's:

• What are people doing on the website? In this example, users are navigating to the safety manual page, but they are not downloading the manuals, indicating that something in the user experience needs improvement.
• Who are the users visiting the manual pages? By segmenting the audience, the team might find that certain user groups (warehouse staff) are accessing the manuals more often than others (delivery drivers), which can guide targeted updates and trainings for specific groups.
• When are users visiting the manuals? If there’s a spike in visits at specific times (during a load delivery and large equipment needs to be used), the logistics team could optimize the manual content to be more accessible during those peak hours or integrate features like “most downloaded” for specific purposes/times.
• Where are users coming from? The team looks at the traffic sources and realizes that a significant portion of users is coming from a specific email link shared by supervisors. If this is a high-traffic source, they might consider adding sharing features or further promoting the manuals through channels easily accessible to all users.
• Why are users leaving the manual pages without downloading? The high exit rate suggests that the content is not easily accessible or that users are frustrated with the design. By testing on different layouts and improved functions, the logistics team can determine that maybe lack of clear action was causing the issue.

Types of Web Traffic

In today’s digital age, political campaigns rely heavily on web traffic to engage with voters, spread their message, and ultimately influence election outcomes. I wanted to apply this concept to political campaigns giving examples on types of web traffic that we might have all experienced from the recent election. From direct visitors who already know a campaign’s URL to paid ad traffic, each type of web traffic offers unique insights into how potential voters are interacting with digital content. 

• Direct traffic occurs when users enter the URL of a website directly into their browser. These are visitors who are already familiar with a specific campaign, through past engagement already knowing the website and its content.
• Organic traffic refers to visitors who find websites through unpaid search engine results. This type of traffic is generated when people search for relevant terms or topics in search engines like Google, and campaign websites appears in the search results.
• Referral traffic comes from other websites that link to content. These could be social media platforms, external blogs, news articles, or other websites that mention or direct users to campaign’s sites.
• Campaign traffic is generated through paid marketing efforts, such as Pay Per Click (PPC) ads, email marketing, or social media advertisements. This traffic is the result of targeted ads that direct users to websites.

Task Completion Rate

Another concept I want to analyze from the lecture is Task Completion Rate. Task Completion Rate measures the proportion of visitors who successfully complete a specified action on a website, such as registration, making a purchase, or signing up for an email subscription. This metric is essential for understanding how effective a website is at guiding users toward completing a desired outcome. A great example of Task Completion Rate that most individuals can say they have experienced as a consumer is signing up for email ads to be offered discounts and promotions for a purchase on the website. For example, many of us have visited retail websites, like H&M, Anthropologie, or REI, and encountered pop-up forms offering 10-15% off or $10 off your next purchase if you sign up for their email list. These types of incentives are designed to improve sale rates by motivating users to complete a task (signing up for the email list). If many users abandon the form without completing it, it could be due to several factors like the form being too long, not mobile-friendly, or the user losing interest. Identifying these issues through analytics provides valuable clues for website redesigns to improve user engagement and sale rates. An example below is from the Hollister website offering $10 off ant $40 purchase if you join their emailing subscription for free.