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Using Page Value in Google Analytics to Determine Influential Pages on Your Website

As we mentioned in our first Analytics blog post, the sheer amount of raw data available in your Google Analytics account is enough to drive you crazy. At Adster Creative, we stand firmly in the “actionable data over big data” camp, and because of this we have had to figure out how to break down data to guide our decision making– and ultimately drive as much quality traffic as possible to our clients’ websites.

Website Analytics Image SEO and PPC

While UTM strings are helpful to keep tabs on your inbound marketing efforts, Page Value is a direct indication of which pages on your website are most influential to users. This allows you to monitor which pages are making you money, and which ones may need a closer look.

First, let’s start with Google’s formula for determining Page Value:

Page Value = (Transaction Revenue + Total Goal Value) / Unique Pageviews for the page

This formula can also be used to figure out the page value for a group of pages, but in our case we are focusing on individual page value. Also keep in mind that this formula does not include all revenue for the visit. It will only include the conversions and transactions that happen after the page is viewed, not before.

*Note: you must have previously assigned values to your goals for page value to display in your campaign. You will also want to keep in mind that certain “action pages”, such as a contact form submission pages, will have a higher value due to the fact that it is a goal page. Filtering out these types of pages will allow you to focus on interior, non-goal pages if you wish. 

You will find page values by navigating Behaviour > Site Content > All Pages.

I like to use the Advanced filter to remove pages with a value of 0 or less, and to filter out blog posts.

Page Value Advanced Filters.png

These filters allow me to focus on the pages on the site that actually convert, as well as removing the numerous blog posts in our archives. Hundreds of filters can be applied in different combinations, and I encourage you to play around with them until you figure out how you are most comfortable filtering your raw data. You could also use the filters to search through only your blog posts, to see which posts are your most valuable. Since this exercise is for website pages only, we have filtered out blog posts. 

Now that our filters are in place, lets start to analyze the data. On the far right-hand side of the data table will be a column labeled Page Value. The first thing that I like to look at are the pages with lots of pageviews, but a low page value.

Page Value 4.png

This allows me to take a cursory glance at which pages may need more attention or refinement, and which ones are converting very well, but this alone is not enough. To know for sure, we need to dive deeper, and filter the data even more.

Next, I use engagement data to see how user interaction will influence the page value. Usually, this is where I lean on the Bounce Rate metric.

Page Value 2.png

Although I don’t agree that you can live and die by Bounce Rate, it is an incredibly helpful metric when it comes to measuring page value. With this view, I can see that even though there is a 100% bounce rate from our Marketing Process page, it has a very high page value–because people tend to convert after visiting this page.

There are a lot of different ways to filter and analyze this data, but I would recommend using the following steps when you are analyzing your site’s page value:

1. Start with looking at the pages with the highest amount of pageviews, noting the pages with a low page value (as described above).

2. Then, use your engagement metrics (bounce rate, time on page, exit rate) for a deeper understanding of how users interact with that page. How do each of these metrics relate to the page value?

3. Now look at the pages with the lowest pageviews but high page values. How are people interacting with these pages? Is there a specific call to action on these pages that aren’t found on the lower valued pages?

4. Lastly, figure out how people are finding the pages with low views– are they coming from social media, or adwords campaigns? Is this traffic organic or paid? Are there sites that are referring users to this page?

Again, there are thousands of ways that you can slice this pie. Just remember that, at the end of the day, this exercise in data mining is only useful if you take the data insights and turn them into a better-optimized website.