If path analysis simply outputs a "pretty"[1] graph, while it may look nice, it does not provide anything concrete to act upon.
If a large percentage of a certain cohort, people between the ages of 18 and 25, logs into an online game, creates a profile and then spends the next 10 minutes wandering around the menu page, then it may be that the user interface is not logical.
By seeing this group of users following the path that they did a developer will be able to analyze the data and realize that after creating a profile, the “play game” button does not appear.
In practice, this analysis is done in aggregate, ranking the paths (sequences of pages) visited prior to the desired event, by descending frequency of use.
Understanding how users move through an app, game, or other web platform are all part of modern-day path analysis.
Next the shop owner will reorder the shelves and products to optimize sales by putting everything in the most logical order for the visitors.
For example, if a web site offers products for sale, the owner wants to convert as many visitors to a completed purchase.
Path analysis answers typical questions like: Where do most visitors go after they enter my home page?
This approach is very helpful when analyzing how many visitors reach a certain destination page, called an end point analysis.