Keyword rankings refer to your web page’s position within the search results of a search engine when a user searches for the given keyword. And because people are generally lazy and click on the first relevant result they see, the closer you can rank to the first position, the more traffic you’ll likely receive. So ranking your website for a given website as quickly as possible is often the name of the game. There are many factors that determine your search engine rankings, but here’s a starting point:
While there are a number of SERP tracking tools available, nothing is more accurate than Google Search Console. GSC is Google’s own portal for helping website owners manage everything to do with their search engine, including rankings.
To view keyword data, go to Performance, then click Queries. Here you’ll see all the keywords you rank for, how often you’re appearing, the position you rank, how many clicks you’re getting and your click through rate.
While you can refine the results in Google Search Console, a much more user friendly and insightful way to track, read and visualise your SEO rankings is to build a reporting dashboard using Google Data Studio.
Google Data Studio is a simple to use tool for creating reports in the cloud. To build a customer SEO ranking report, you can simply connect your GSC account to Datastudio then use one of their pre-made templates, or build and edit your own to suit your needs. You can also link Data Studio to your other Google Analytics, Youtube and Ads accounts, as well other third party platforms.
Keyword rankings are valuable because the higher you rank, the more impressions and clicks you’re getting to your website, without having to pay for the advertising. You’ll have to invest in optimising your website and content in order to rank highly enough to be seen, but the rankings (and therefore the return on that investment) can often be ongoing, so the benefit compounds day after day. That said – your keyword rankings can fluctuate by the second, user by user, region by region – so you shouldn’t stress too much about it day to day.
Step 1: Brainstorm a list of important keywords related to your product or service. These are referred to as ‘seed keywords’. Generally a good start would be to think about what your customers will be typing in when searching for what it is you offer, then branch out from there using a keyword suggestion tool (like Google Keyword Planner).
Step 2: Organise the keywords into buckets, which are also known as content silos. For instance, if you’re an Accountant, you might silo your keywords into ‘Tax Accounting’, ‘Bookkeeping’, ‘Outsource CFO’ etc.
Step 3: Use a keyword research tool (see below) to expand the list of related searches for each content silo so you end up with a long list of all the possible keywords and phrases your customers may search when looking for your product or service.
Step 4: Sort to the top the ones that you already rank close to the first page for, have high volume, indicate a high intent to purchase (rather than irrelevant research), the ones with low competition, and if you’re the one producing the content – the ones you are most interested in so you enjoy creating the necessary content.
Step 5: Repeat step 3 for the top 25 keywords which should give you a very niche list of related keywords – also called semantic keywords. These outliers can often have less competition, and thus, easier to rank for.
Step 6: Scroll back up to the ‘What is the fastest way to rank a keyword?’ section of this article for how to turn this list of keywords into actual search engine rankings.
While there are plenty of awesome tools available, for doing your own keyword research, it’s best to go straight to the source, which is Google’s Keyword Planner. Of course, Keyword Planner won’t show you the rankings of your competitors, that’s where you can benefit from using a tool such as SlangScribe, SEMrush, Ahrefs, SpyFu or Serpstat… but keep in mind – these tools are looking at search results from an outside perspective and they may have their own calculations applied to the data sets, so take it with a grain of salt.
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