Prioritizing the pages will help us look more quickly

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Suppose we start with /products/, /tools/ the beginners guide pages (love those), and a few others that jump out. Here are a few pages from that list: Target page list From here, we would compile a list of competitors based on top keywords for each of the pages. That will let us compare average metrics across the top competitors to the metrics for Moz’s pages. This dataset represents the top 10 competitors from the top 10 keywords for each of Moz’s pages.

Once compiled, we’ll have 90-100 rows of competitor data, give or take, depending argentina consumer mobile number list on where Moz ranks for each page in the list. We can average the competitor data to make it easy to compare, and spot-check from there to look for outliers, or filter out branded or stray keywords we don’t want to compete for anyway: Spreadsheet of Moz competitor data. Now it’s time to look for opportunities. We can eye-ball the metrics in a shortlist like this, but if we’re looking at hundreds or thousands of pages (even after filtering it down), this gets a little cumbersome.

through the list and find the best opportunities. In a scenario where it’s a short pilot program, some of these competitors have scary-high linking root domains, and we’re going to have an idea of a monthly budget to set our pilot up for success by not biting off more than we can chew. So, we’ll add a couple columns to help some of these stand out. To help find the low-hanging fruit, we might look at the relationship to the gap in linking root domains of the competition and our potential campaign page, and the search volume from those top 10 keywords: Moz spreadsheet with LRD Gap highlighted.