Keyword Selection at Scale Using Weighted Variables

Before diving into our process, we believe that it’s important to explain what’s wrong with the way most content and SEO professionals choose keywords as it stands. What’s wrong is the fact that keyword selection is heavily based on metrics that aren’t enough to describe the value of a term from different angles.

These angles include:

• The level at which the website has built topical authority based on a certain topical category.
• How difficult it would be to create a resource that has chances of getting visibility for the target keyword from a content creation standpoint.
• What’s the estimated business value of the keyword and its close variations if we were to have visibility for it. 

Let’s put this into perspective by using a simple example. For the sake of the example, let’s assume that we’re an email outreach software and we’re interested in getting visibility for the term “outreach email templates”.

As you can see from the screenshot above, according to Ahrefs this keyword has:

• A keyword difficulty (KD) of 39
• A global volume – for search – of 350

Plus, the cost-per-click (CPC) isn’t available.

Is the above enough to form a decision about whether this keyword is a good keyword for us to go after? If yes, having limited resources — which is true for most early-stage companies — how big of an opportunity could this be for us? 

Should we prioritize it higher or lower compared to other keywords that we’ve identified as part of our keyword research process? 

Do we have enough topical authority to have a chance of getting visibility for that keyword?

Does the fact that the keyword doesn’t have a CPC assigned to it play a role and, if yes, how big of a role it should play?

How big or small is the search volume compared to the total number of monthly searches for keywords that we’re interested in?

These are questions that, unfortunately, the above metrics can’t help us with.

However, we seem to heavily rely on them when it comes to selecting the right keywords to go after that we forget any other factors we should be considering.

To understand why that’s wrong, we need to understand a few important things first.

Keyword difficulty only takes into consideration the number of linking domains to the top ten search results for a given keyword.

In other words, it tells you how difficult it is to rank for a particular keyword solely based on the number of referring domains other websites have.

This means that it doesn’t tell you how authoritative your website is — in the eyes of Google — for the topical category for the keyword you’re about to target.

Thus, you should take it into consideration, but only up to a certain point, which we’ll look at later. 

When it comes to search volume — regardless of whether you take into account the global volume or the volume for a particular country — there are, again, some limitations that need to be understood. 

The most prominent one is the fact that search volume for a keyword should be examined on a comparison basis, accounting for the total level of opportunity available for all the other keywords you’ve identified as an opportunity.

In practice, in order for us to say whether the 350 monthly searches in our example above — which is an average and not an absolute number, of course — is good or not, we have to compare it with all the other keywords we’ve identified and we’d like to have visibility for. 

Last but not least, when it comes to CPC, there are also some things we need to keep in mind. 

In our example, Ahrefs doesn’t report a CPC, which means that the CPC here may be really low.

If the CPC was high, would that indicate a high business potential for us?

Not necessarily. 

Just like with the rest of the metrics we shared with you, we believe that we should factor CPC in, but only allow it to form our decisions up to a certain extent.

Relying on the above, or other metrics, to make decisions isn’t sufficient because, as we’ve seen, these metrics not only have their own limitations but our decisions are biased. That is, we’re making choices based on what we believe these metrics mean and how much importance they have for us. 

What we can take from this short — and hopefully not boring — analysis is that:

1. Most SEO metrics have weaknesses and we shouldn’t solely rely on them when making decisions.
2. We should factor in additional metrics and variables based on what’s important to us when making those decisions.

Here’s how we do that.

Before we move forward with our process, here are a few reasons why we decided to change our approach when it comes to keyword selection:

1. We identified the weaknesses inherent to the metrics that we just saw.
2. We needed a scalable and educated way to make decisions for our clients.

By now, it may be evident that what we’re referring to is KOB analysis, a concept that was first introduced back in 2011 by Todd Malicoat.

KOB analysis – Keyword Opposition to Benefit analysis – is essentially a better way of making decisions on what keywords you should go after based on specific variables.

What we did was to take this approach a step further based on what we felt was more important when selecting keywords for our clients. 

To begin with, here’s what our template looks like:

As you can see below, we have the following three tabs:

1. Weight assignment
2. Topics
3. Search

Let’s start with the first one — weight assignment — and break down its main elements:

1. Where we set the weight of each of the six parameters we take into account and that form the Opportunity Score.
2. Where we identify the maximum values for two of our parameters – global volume and CPC – which we then use to assign values to each of the parameters based on the weight we’ve assigned and the highest values in our list of keywords. Here, we can also set a manual value for those two metrics, in case we have keywords with extremely high values that may skew our data.
3. Where we see a visual representation of the weight distribution between the six parameters we take into account to come up with our Opportunity Score.
4. Where we can select a specific keyword to analyze, that exists in the Topics tab, which works like our database.

When it comes to the metrics — also called parameters — we use the following six:

1. KD (Ahrefs)
2. Global volume (Ahrefs)
3. CPC (Ahrefs)
4. Topical authority — a proprietary metric that’s measured on a scale from 0 to 3, with three being the highest. It describes how authoritative we believe we are based on a specific topical category.
5. Business value — a proprietary metric that’s measured on a scale from 0 to 3, with three being the highest. It describes what the hypothetical business value is for a specific keyword.
6. Difficulty score — a proprietary metric that’s measured on a scale from 0 to 3, with three being the highest. It describes the relative difficulty of creating a page that targets a certain keyword from a creation standpoint. 

Author’s Note: Metrics three to six are under no circumstances “perfect” metrics, but we believe are necessary to help us make decisions at scale. After all, as we saw earlier, even the metrics that most professionals and businesses use to form their decisions are far from perfect.

The metrics that are directly proportional to the Opportunity Score — meaning, the higher they are, the higher the Opportunity Score — are the following:

1. Global volume (Ahrefs)
2. CPC (Ahrefs)
3. Topical authority
4. Business value

The metrics that are inversely proportional to the Opportunity Score — meaning, the higher they are, the lower the Opportunity Score — are the following:

1. KD (Ahrefs)
2. Difficulty score

As we make adjustments to the weight of each of our parameters, the Opportunity Score changes. 

The Opportunity Score is plotted on a scale from 0 to 100, with 100 being the highest.

When it comes to assigning weights to each of the variables we take into account, the total of the weights should be 100. 

If the sum exceeds that number, we get the following message displayed and there’s no Opportunity Score assigned to any of the keywords in our database.

The same applies when the sum is lower than 100.

A parameter can have a weight of 0, in which case we don’t take it into account for the whole database when it comes to forming the Opportunity Score.

At the same time — even though it doesn’t really make sense, since we explained our logic earlier in this post — a parameter can have a weight of 100, as long as all other parameters have a weight of 0.

Through the Weight Assignment tab, we can also search for a particular keyword from our database, as shown below.

Once we select a keyword – in our case that’s “what is email marketing” – we can see what the Opportunity Score is for that keyword.

Making adjustments to any of the weights will result in a different Opportunity Score, not only for this keyword but also for all the other keywords in our database.

The reason why this all is important is that different metrics will have a certain level of contribution to the final Opportunity Score and we’re the ones who decide what that level is.

That’s the best way to inform our decisions and make sure that we don’t rely upon metrics that — as we saw earlier — don’t give us the full picture.

Our database is in the Topics tab and is where we keep all the data, define content taxonomies and also keep some columns that are important for us as an agency.

The Search tab allows us to filter results, sort by the six metrics/parameters that we take into consideration to form the Opportunity Score – as well the Opportunity Score itself – plus we can search for keywords in certain topical categories or by using certain criteria.

In our Search tab, we have the following options:

1. Filter the results – the default is “All” – based on what we want to see.
2. Sort the results based on a specific metric that’s important to us.
3. Search for specific keywords that include terms we’re interested in.

As you can see below, there are several filtering options available for us to see exactly what we’re interested in.

That may not be as important if you’ve identified only a handful of keywords to target for a particular website. Imagine if you’ve identified thousands of keywords as part of your keyword research, though. 

In that case, you need a scalable way of making decisions about what keywords to go first, based on certain metrics/parameters that have a level of importance that you’ve defined earlier in the process.

Below, you can see the area where you can enter a search term, e.g. “content”, that’s of interest to you and that may be included in some of the keywords in the database.

There are other options to search for as well. 

Last but not least, when it comes to sorting the results, we have the option of sorting based on all the metrics that are included in our keyword database.

With that, you’ve seen our process of conducting keyword selection at scale using weighted variables. 

Is our process 100% accurate? Definitely not. 

Is it way better than what’s already out there? Definitely yes.

With our process, you define the relative importance of the metrics you take into consideration to form decisions about how big of an opportunity a specific keyword is for you.

That may not be important if you’re targeting just a few keywords, but as we saw earlier, it’s extremely important when you’re targeting thousands of keywords.

Let’s bring this to a close.

This was our proprietary process of using weighted variables to make keyword selection at scale. We hope that this process will help you make better decisions and understand the importance of controlling how important different SEO metrics are, and should be when making decisions for what keywords to cover.

In general, we don’t give access to our template other than to present a view of doing something different when using it. However, if you believe that it’ll really benefit you and your business, please send us an email at hello@minuttia.com using the subject line Keyword Selection and explain why and how you’ll use it. If your reasons fit our purpose, we’d be happy to make a copy and share it with you.