When we say the web, we’re talking about how people navigate and use the Internet. More specifically, we’re often focused on how organizations interact with their users or customers’ via website.
Just about all organizations have one or more websites that they use to interact with their users or customers. One of the most common needs that organizations have, is to understand how users interact with the website itself. Questions we might want to answer include the following. How many users are coming to the website, and where are they coming from? How long do people stay on the website? How do people navigate through the website? Are people getting stuck somewhere, or is navigation fluid? How often do users access help or FAQ sections of the site? How often are certain actions, like purchasing, happening? When do they leave the website and where do they go when they leave the website? Understanding these types of patterns helps us assess whether our website is well designed and whether we are stimulating the right volumes and types of activity.
For example, let’s say we’re an online retailer. We might find the traffic to our site is very strong and that people spend quite a while browsing products and regularly add products to their online shopping cart. But for some reason those products aren’t being purchased. By looking closely at exactly where customers are falling out of the process, we might find that a point in the navigation path is broken or confusing. So how do we typically get this type of information? It turns out that most websites have measurement mechanisms built into the site itself. Usually each page or element on a page contains a small piece of code that generates a message when your user arrives or clicks on something.
These messages are written to logs and can be interpreted using a web analytic services like those provided by Google’s, Adobe, IBM and Web Trends among others. Driven directly into an organization’s data environment, we can incorporate the data from website into a variety of descriptive productive prescriptive analysis. And there are a number of common web analytics matrix that are used to characterize behavior. Two very common types of analysis you might see are funnel analysis and path analysis.
Funnel analysis traces how many customers move from one major stage of a process to the next. For example, from the website homepage to a shopping cart, to a registration process, to an actual purchase payment. The visualization of this type of analysis tends to resemble a funnel. Hence the name. Path analysis is a bit more involved and quantifies the ways that customers navigate through a website which is usually not linear. Here’s a simple example of how path information might be represented based on web data.