How To Implement a Keyword Strategy By Nick Eubanks

Click For Original Article

The future brings with it all sorts of exciting opportunities for improved efficiency; screens are getting bigger, microchips smaller, and everything is getting faster.

Keyword research is no different From The Future, the process has become more streamlined than ever before to get meaningful competitive intelligence into a digital vertical market – and pretty darn fast.

I’m going to take you through a process using Ahrefs Keyword Explorer to quickly pull out all the stops and:

  1. Gather a meaningful population of relevant keywords.
  2. Filter for modifiers.
  3. Quickly prioritize terms based on volume and competition.
  4. Infer and tag terms for intent.
  5. Use this data to design a short-term SEO content map.

To make this process as tangible as possible, I’m going to actually run through it for real. I’ve decided to use a very competitive head term keyword; chatbot.

Please Note

This process is not to conduct comprehensive keyword research, for that I’d encourage you to read about how to calculate a total addressable market. Instead, this is a simple (modern) process for getting moving, and demonstrates the thought process and approach to identify opportunities that can kick start your keyword strategy.

If you’re specifically looking to optimize bottom-of-funnel pages, consider reading about keyword research for conversions (it focuses a lot more on identifying purchasing intent at later stages in the customer journey.

Getting Started

To dive in you’ll need to start by selecting a “seed” keyword, ideally this is a really high-level 1 or 2 word term that represents the vertical market you’re looking to target at 50,000 feet.

Examples of seed terms across a few different verticals are:

  • Shoes – Ecommerce
  • Personal injury – Legal
  • CRM – Software
  • Student loans – Financial
  • Vet tech – Education
  • Vyvanse – Pharmaceuticals
  • Plastic surgery – Medical

Again, for the purpose of demonstrating this particular process for keyword research, I’m going to use chatbot.

Step One

Drop that bad boy into Ahrefs Keyword explorer:

In this case, the term is already the parent topic – if it’s not, re-run your search using the parent topic. This is important because at this stage we want to get the largest volume term to cover as much of the competitive landscape as possible.

Step Two

Click the “View all” link under the far left column titled “Having same terms”

Step Three

This is a very important, but nuanced step.

Instead of the more traditional approach of sorting your term population by search volume (“Volume”), at time when nearly 50% of all searches do not result in a click, instead sort sort descending by Clicks:

Step Four

As you scroll down through your list, pay attention to the large disparities between Volume and Clicks; it’s kind of incredible.

Here’s an example of terms that actually result in more clicks than searches (meaning that the searchers are bouncing back to the search results and clicking more than one result):

What you’re doing now is reviewing the Click volume across all the terms with the most clicks, to understand how big of an export pool you’re going to need.

Ahrefs shows 50 results per page, and the “Quick export” feature allows you to export the first 1,000 rows – so you need to determine if you need more than the first 1,00 rows of term data.

For the purposes of this post, I’m not interested in any keywords with less than 100 clicks/month, so by page 3 of results I’m out of the terms I want to grab data on (for now).

What this also means is that of the 38,458 keywords that Ahrefs has in their keyword index that include the word “chatbot” less than 150 of those terms get more than 100 clicks/month.

Crazy right?

Step Five

This is also really important, mostly for your credit balance (and bank account), but when you go to export your results make sure you select Custom under “Number of rows” (and set the number of rows that meet your click threshold, so for me in this case it’s 150) – and then check the box for “Include SERPs.”

If you don’t select “Custom” and limit the rows, and just let it default to the “First 1,000,” when you select “Include SERPs” you just ate up ~100,000 of your monthly export credits – which would mean in this case instead of me using just ~15,000 credits (150 rows x 100 results per SERP), I just lit ~85,000 credits on fire.

Now You Have Your Data

You’ve exported your CSV file, and now it’s time to go to work.

I personally prefer Google Sheets for data sets this tiny (again, this is only ~15,00 rows; 13,035 actually) but you may need to use Excel depending on how big your export file is.

Some of the beautiful URL-level data we now have is:

  • Keyword – The query itself.
  • Ranking URL – The current ranking URL for the query.
  • Difficulty – How difficult is is to rank for the query on a logarithmic scale of 100.
  • Volume – The estimated monthly search volume for the query on the local index.
  • Clicks – The estimated number of clicks the search query generates per month on the localized index.
  • CPS (Clicks/search) – The estimated clicks a user makes when searching the query.
  • Return Rate – The estimated number of times the user returns to the search results for the query.
  • Parent Topic – The parent topic (if applicable) the query would fall into in terms of contextual relevancy.
  • Parent Topic Volume – The estimated monthly search volume of the parent topic term.
  • Last Update – The date all of the above data was collected or calculated.
  • RDs – The number of root domains linking to the ranking domain.
  • DR – The Ahrefs “Domain Rating” score out of 100 for the domain’s authority.
  • Organic Traffic – The estimated number of organic visits the ranking URL receives per month from the query.
  • Ranking Keywords – The total number of keywords the ranking URL ranks for organically.
  • CPC – The average cost per click from AdWords for the query.
  • Position – The current position the ranking URL was in when the data was last updated.
  • SERP Features – The types of features Google is currently displaying in the results page for the query.

Initial Sanitazion

We also have some column cruft we want to dump (delete), so you can remove:

  • Country – Useless unless you’re planning on blending data from various country-specific indices
  • Backlinks – Doesn’t populate in this export
  • URL Rating – Doesn’t populate in this export
  • Top Keyword – Doesn’t populate in this export
  • Top Keyword Volume – Doesn’t populate in this export

In addition, you’re likely to have a bunch of blank URL fields in column B, and we’ll want to tag these.

These are the result of Google UI features like People Also Ask and Knowledge Panels, so we want to label them as such. The easiest way to do this is to select Row 1, then click Data > Create a Filter, then click the filter in Column B > Filter by Value > Clear > select “Blanks,” and it should look like this:

Tip: drag the thick light gray cross-panes under Row 1 and between Column’s B & C to set those to sticky for this next part.

Now scroll all the way over to the right so you can filter by the Google Features and tag the URLs:

You don’t have to do this, I just don’t like blank fields in my datasets 🙂

But, make sure once you’re done you go back to the URL column (Column B) and reset the Filter to “Select All” to make sure you’re displaying all your keyword rows before you move on.

Formatting For More Efficient Consumption

Some of the data in here is going to be more important for us than others, so I like to move around the columns a bit to make it simpler for me to see the stuff I care about most.

My preferred order for columns is:

  • Keyword
  • URL
  • Position
  • Clicks
  • Type
  • Difficulty
  • RDs
  • DR
  • Traffic
  • Keywords
  • CPC
  • Volum
  • CPS
  • Return Rate
  • Parent Topic
  • Topic Volume
  • Last Update

Filtering for Modifiers

Keywords are comprised of classes and modifiers.

A query class most often times actually contains a root term and a modifier, as AJ Kohn so eloquently explains in this post, and then modifiers tend to show up a prefix (before the root term) or a suffix (after the root term), which fascinatingly enough can actually alter the intent — but that’s a discussion for another day (and another post).

What we want to do is to filter our list of keywords based on modifiers they contain, so we can tag the terms for the modifiers and begin to look for patterns to inform our overall keyword strategy.

The way we’re going to do this is one column at a time, starting with adding a new column between A & B called Brand Modifier.

Tagging for “Brand modifiers,” i.e. any instance where a brand name is being used within the keyword. These represent searchers closer to the bottom of the conversion funnel and hence need to be treated with different considerations – which I’ll get into a bit later in this post.

In the screenshot below you can see the brand modifier “streamlabs” in use:

There are significantly more efficient ways to run macro’s and other fun automation scripts to speed this whole piece up but to keep this post SUPER simple, and you’re just going to use the Filter in the Keyword row for “Text contains” and enter in 1 modifier at a time – and filter through your list.

So I’m going to run through this list and tag terms for brand modifiers when present. A sample of the brand terms I found are:

  • Streamlabs
  • Facebook
  • Google
  • Microsoft
  • Mitsuku

and a handful of others. The reason it’s important to tag for branded terms is generally speaking these are not going to be worthwhile targets for an SEO strategy.

It’s really the non-branded terms we want to uncover, and that’s where there’s a bit of nuance to be aware of…

For example, for the keyword “how to make a chatbot” that would actually break down into:

  • chatbot – root term
  • how to – modifier 1
  • make – modifier 2

The reason I’m separating “how to” out of  “how to make a” is because it’s highly likely we’ll run into other keywords that contain both of those modifiers, so they should be organized separately.

The next step is to add additional columns for the modifiers so they can all be broken out, and to continue to filter for them and tag them.

Now we want to filter down the list for non-brand terms, to see what our top of funnel opportunities might look like. I have also learned that there are far more adult applications for chatbots than I ever knew (or cared to know) existed…

But reviewing the top of funnel, non-branded, non-adult modifiers, I’m left with:

  • what is
  • website
  • visual
  • tutorial
  • tensorflow (coding language platform)
  • software
  • smartest
  • real estate
  • python (coding language platform)
  • open source
  • online
  • most + advanced (2 separate modifiers)
  • maker
  • machine learning
  • javascript
  • icon
  • how to + make/build/create
  • free
  • examples
  • definition
  • create
  • deep learning
  • commands
  • builder
  • best
  • app
  • api

This modifier set shows me that there’s still a lot of confusion in the industry about what they are, what they look like, and how to use them. I honestly expected to see more use case modifier, like real estate for example, and am especially shocked I didn’t come across “ecommerce” and “lawyers” as modifiers within this term population.

Prioritizing Your Keywords

Keeping the non-brand modifier filter on, next we want to narrow dow the field for opportunities for page 1 rankings to explore.

If you were wondering why you needed to export all that SERP data, here is where we put it use 🙂

To do this, add the following column filters:

  1. Column: Position – Filter by Condition > Less than or equal to: 10
  2. Column: Difficulty – Filter by Condition > Less than: 20
  3. Column: Traffic – Sort Z -> A

Now we add in a new column, right to the right of the Keyword column (Column A) called “Priority.”

Scroll through your list (leaving the above 3 filters and sort in place) and start tagging Priority terms by dropping a “y” into the Priority column. You’re looking for any modifier that might be applicable (in my case right now I’m literally just ignoring a mess of adult modifiers :/ ).

By pulling out all the term modifiers in your sheet, and adding some simple filters around impact (traffic) and difficulty, you’re able to really quickly find opportunities worth exploring.

Here’s what mine is looking like:

Infer and Tag for Intent

This is where it gets interesting, because in previous keyword research processes I’ve designed, I would tag for intent prior to prioritizing. What I’ve learned over the years is that while that process is still sound, you may end up with opportunities that are so difficult to rank for due to steep competition, that you don’t have a good idea of where to start.

So this flips that order on it’s head and has you think about it a bit differently.

For the purposes of tagging these terms for intent (and keeping it SIMPLE), I’m going to be using the more old school buckets of

  • Information
  • Investigation
  • Transaction

If you want to read the new school take on how to better assess intent in 2020, check out this post from Kane Jamison.

My criteria for tagging each is:

Informational

These are terms that lack almost any context, where it’s clear the searcher is at the very beginning of their information gathering journey and are looking for directional results to help them better inform and refine their query.

Based on my list of non-branded chatbot modifiers, this includes:

  • visual
  • machine learning
  • deep learning
  • examples
  • app
  • website

Investigational

These are term modifiers that show a more specific use case, or at the very least lend a bit more context to specifically what the searcher is looking for.

Based on my list of chatbot modifiers, this includes:

  • tensorflow
  • real estate
  • python
  • commands
  • alice
  • javascript
  • software
  • open source
  • most

Transactional

These are modifiers that indicate the searcher knows exactly what they’re looking for and is ready to make a decision, so for my list these terms are:

  • tutorial
  • how to + make a + python
  • setting up + {brand}
  • {brand} + scripts
  • how to + use + {brand}
  • icon

If you’re curious why I chose to tag any of the specific modifiers as the intent I did, drop me a comment and I’ll respond with more details 🙂

Design Your Short-term Content Map

Designing your SEO content map is just another way of saying find themes that have organic search volume / clicks that you can target with well executed content.

Aside from updating existing pages to improve keyword targeting, expanding your keyword footprint by creating new content is ultimately how you win at direct rankings and even execute on the SEO monopoly strategy.

This all comes back to the word themes. In other words, where can you find terms that have enough semantic overlap that you can build your requirements for a piece of content to target as many relevant, valuable terms as possible.

Identify Thematic Patterns Among Your Priority Keywords

SEO targeting is no longer just about keywords, because like all modern practices, it has evolved.

Modern SEO requires a broader focus on concepts that are represented by topics. SEO has become far more about addressing all of the content needed to create relevance, than it is about stuffing loads of terms into a page to try to rank for them and their counterparts.

This means you need to think more so in terms of themes that you can fit your target keywords into, and use these term groupings to design your content.

Looking across the term modifiers I uncovered throughout the example in this post, and grouping them into “themes” leaves me with:

  • Chatbot + App + Examples
    • Visual
    • Machine Learning + Deep Learning
    • Tensorflow
    • Python
    • ALICE
    • Javascript
    • Open Source
  • Chatbot + Commands
    • Streamlabs
    • Facebook
    • Microsoft
    • Google
    • Mitsuku
  • Tutorial + How to make a chatbot
    • Setting it up
    • Software
    • Scripts
    • API

Then there’s some outlier content you could create if you needed to around free, best, smart, and icons.

But the thematic groups above identify opportunities for 3 large pieces of content that have a solid chance to rank (if properly executed) for the biggest opportunities in this vertical based on our process for breaking down search trends using term modifiers and competition.

Bonus

If you want to grab the source data file I used to write this post, here you go

Click For Original Article

How To Implement a Keyword Strategy

Click For Original Article

Before you begin, in order to get the most out of this information – the following assumptions must be met:

  1. You’ve completed thorough keyword research for your website and market, analyzing the data I recommend for carving out keyword targets; namely you’ve dissected page one rankings on Google and have identified at least 100 low hanging fruit (LHF) keywords.
    1. Page 1 Position – This is a number ranging from 1 to 10 showing the current
    2. Ranking URL for each position on the first page of Google. For each keyword in your list there should be up to 10 rows in your spreadsheet.
    3. Keyword – the keyword you’re targeting
    4. Monthly Search Volume – pretty self-explanatory, but the averagemonthly search volume for the keyword in column B.
    5. Word Count – the number of words on the page for the URLcurrently in this row’s ranking position.
    6. Keyword In – One column for each of the following binary keywordinclusion points;
      1. Domain – Is the keyword in the domain?
      2. URL – Is the keyword in the URL?
      3. Title – Is the keyword used in the page title?
      4. Headers – Is the keyword used in any of the header tags on the page?
    7. Domain Links – The number of total links to the domain.
    8. Page Links – The number of total links to the ranking URL.You have a spreadsheet with the following data;
  2. You have a basic working knowledge of Excel and are familiar with (or are at least willing to figure out) how to use super basic functions like sum and average, as well as being able to turn on Data > Filter and performing some basic sorting within your data columns.

All good on the assumptions?

If not, make sure you circle back and get your keyword research done.

If so – awesome! Here we go.

Prioritizing Your Keyword List

While going after the giant volume head terms is the shiny object that usually lures people into SEO, more times than not these terms end up being prohibitively difficult and end up setting you p for disappointment.

So, let’s set some realistic priorities based on proven concepts like rank potential.

Start with your LHF keywords, and sort this short-list by the lowest competitive ranking factors, so least number of links to the domain and page, least number of words on the page, and lowest usage of the target keyword across the domain/URL/page title/on-page.

Take your LHF keywords, and sort by the absolute easiest. There will be a basic threshold based on your site’s current level of authority (let’s use DA as our metric for this), some of these keywords you will be able to crack page 1 for simply by creating a page that meets the bare minimum requirements… so let’s define those.

First thing we’re looking for is ranking URL’s that have zero (0) links to them, as this is a great sign that Google is not currently placing a heavy weight on page- level links for this keyword.

So what I’ve highlighted above are 2 ranking URL’s for the same keyword; “no trespassing signs.”

The context here is I’m considering getting into the traffic signs business (it would dovetail nicely with the existing traffic safety business) and have been using this SERP data as market research to not only determine which signs to possibly bring into inventory – but to understand which rankings are attainable in the short, mid, and long term.

At first glance above it looks like I may be able to crack a position 4 ranking for this term, until you move onto the next item for consideration beyond the page- level; the domain level.

The domain here has 59 million links, yes MILLION, because it’s Etsy.com.

But all hope is not lost; the #9 ranking URL (displayed in the row right above) has only 5,152 links to the domain.

This is actually an achievable ranking. Better still, the total word count for that position #9 ranking URL is only 408 words… so I know for a fact I can obliterate the relevancy on that page by designing more semantically contextual content.

For a detailed dive into how to select realistic keywords, go check out that post on Rank Potential or review Chapters 4-7 in Master Keyword Research.

So now that you have a sense of what we’re looking for in terms of possible LHF’s from a realistic stand-point, let’s actually get into the components that make up *good* keyword targets.

Minimum Viable Volume

Finding competitive holes in the SERP’s does you no good it you only stand to gain a small handful of visits for those terms each month. While I’m a huge proponent of going after extremely qualified, low volume terms – I tend to focus on long-tail keywords that will not take significant investment to pick up.

If you are going to embark on a specific content or link building campaign aimed at acquiring rankings that require deeper investment – say anything over $300, you need to make sure these terms hit a minimum volume, or the economics simply don’t make sense.

A few general rules I follow:

  1. Higher funnel terms (informational and investigatory navigational) need higher volume than their more qualified lower-funnel counterparts to justify deeper investment.
  2. Invest first in higher intent, lower volume terms as these will often result in higher conversion rates.
  3. Initially, avoid terms with less than 10 searches/month or less.

[UPDATE] Consider paying closer attention to clicks vs. search volume. 

Relevancy Match

Stop and ask yourself, does this keyword *really* make sense for what I’m trying to do.

It may have great volume, be penetrable at a top 5 ranking, and is a great vanity term – but is the traffic from this term going to move the needle for the strategic goals you’ve set for your keyword campaign?

A good litmus test I like to use for this is to pose this simple question to myself when considering this aspect:

If visitors come in from Google on this keyword, what do I anticipate them doing?

Competitive Difficulty Threshold

At this point you should have already set the relative competitive thresholds you know you must operate within – at least at this point in time.

These are based on where your website is at right now in terms of authority (and ability to rank).

One nice thing about Google is we know you can un-seat rankings for more authoritative sites by punching above your weight, and you do this with trust, relevancy, and QDF.

It’s still important to be acutely aware of what’s realistic here. For example if your site is a Domain Rating of 22 you can realistically target rankings for sites with a DR up to say 45 (especially at the page level), but it would be foolish to build a strategy around targeting rankings for domains with DRs between 50 and 65.

Domain Expertise

You may be thinking “I don’t need an understanding of what my site’s about – I’m not my target audience” or “this doesn’t apply to me because I’m simply going to outsource my research and content.”

Stop it.

The ability to at least create an outline from stream of consciousness writing is what separates payback periods in months versus years.

Before you embark on a specific keyword campaign, you need to understand your audience – know what they do for fun, for work, where they hang out online, where they shop offline, their language, their metaphors, their colloquialisms… this is crucial to creating content that will success – and RANK.

Now, the most important aspect of developing pages designed to rank…

Mapping Keywords to Content

Your list of keywords is useless unless you understand exactly what to do with it.

The answer is NOT add them onto a bunch of pages on my site, change the anchor text in my footer, and put them at the beginning of my titles tag.

Yes, those teeny weeny elements can matter – but it’s not the Wild West days of the 1990’s any longer… SEO has grown up, and so your SEO strategy needs to as well.

To get started with the process of creating a content map to create the right kind of pages for the rankings you want – we need to start with an important question:

Have you done a content gap analysis?

As far as I see it there are 2 routes to get this done:

  1. The Content Strategist’s Route – hugely impactful, requires deep analysis and a LOT of time. Here’s a great resource from Moz to get you started.
  2. The SEO’s Route – pulling together a whole bunch of data you already have (or can easily get) and using it to quickly punch holes. Here’s a more advanced resource from Traffic Think Tank.

Each one can be painted with the brush of the other, however, for the purposes of this guide we’re going to focus on the approach that lets us win with data (and is also much faster).

The quickest way to get this done – to JFDI and keep moving, is to first go pull down all the keywords you currently rank for from SEMRush, by going to:

https://www.semrush.com/info/{yourdomain.com}+(by+organic)

Export these all out to Excel

Now take your total target keyword list, and paste it into a new column called “Target Keywords” and then create a new, empty column and title it “Content Gaps.”

Next we will use a simple Excel function called VLOOKUP. This function lets you quickly search for information within your spreadsheet, like the keywords you want to target but don’t currently have rankings for 🙂

So as not to bloat this guide with too much Excel nerdery (especially since I imagine many of you are already familiar with this function) here’s a step-by-step walk-through on how to set up VLOOKUP in your spreadsheet.

Some additional resources

OK, back to what we’re all here for – Content Mapping

I’ve created a mapping rubric covering the most common (and effective) content – at least in my opinion, for the 6 most common types of websites on the internet:

1. Ecommerce
2. Lead Generation
3. Publishers
4. Forums
5. SaaS
6. Professional Services

How to Use This Rubric

At this point you should have completed your content gap analysis and now have a list of target keywords that you know you need to create content for.

Furthermore, since one of the assumptions of getting started with this guide was that you had already completed “thorough keyword research” according to my guidelines, you should already have a column in your master keyword file where you have all the intent tagged – so this becomes infinitely easier.

The next step is to identify which top-level “website type” you fall into – some will be easier than others; you know right off the bat if you’re selling software, products, or running a forum.

Where this may get a bit more ambiguous is if you have a blog… you need to decide what the purpose of your blog is?

If you write because you love it and you enjoy ad revenue, you’re likely a Publisher, however if you write about Design, Development, SEO, etc. and your blog is a source of consulting leads – then you’re in the lead generation bucket.

What you’re looking at in the above rubric is the actual “content types” that I’m recommending you create for your target keywords based on the cross-section of the intent of your selected keyword and your specific type of website.

This may still seem confusing – and to be honest, it definitely is; we’re doing the nuts and bolts part of putting your keyword research to work for you, this is the step that 95% of people doing SEO completely miss!

In order to provide some context and hopefully help you get your arms around this, I’m going to give you some real world examples of some of these types of content at work in the wild.

Mapping Content for Ecommerce

  • Head terms = How To and Resource Documents –> Informational
  • Shoulder terms = Product category terms –> High volume + High conversion
  • Body terms = Sub-Category keywords –> Commercial Investigation
  • Long-Tail terms = Product queries –> product pages

Mapping Content for Lead Generation

Whether you’re generating leads for your business or as a lead broker; this is probably the largest segment of website types that are investing in SEO.

As a general rule of thumb – there is no one size fits all when it comes to mapping Lead Gen content to your keywords. You need to think about what the ideal experience is for each individual query, and then look at what Google thinks an “ideal experience” is.

Take note of the types of content that’s ranking.

  • Evergreen – stronger focus on head terms and more semantic body content
  • News – stronger focus on specific shoulder and tail terms for immediate relevancy on results that are being given a short-term “boost” based on search velocity around a specific keyword set.

Lead Generation for Local Service Providers

Build service pages that provide content designed to rank for specific pain points;

If you’re a plumber focus your main plumber keywords on the homepage and about pages. These are info queries.

Use your service pages to hit on specific problem queries;

    • Drain clogged
    • Fish stuck in the toilet
    • Hair clog
    • Overflowing Toilet
    • Toothbrush down the drain

Use these as middle of the funnel pages since people searching are already looking for a solution.

Keyword Content Examples

The following are examples of content that fits one of the above rubric intersections, serving a specific intent for a specific type of website:

Informational Content for Ecommerce

Example: How to Get Speed Bumps in Your Neighborhood

Navigational Content for Lead Generation

Example: Personal Injury and Health Law (Update: They’ve added a slick in-content contact form)

Navigational / Commercial Investigation for Publisher

Example: Link Building Outreach Platforms Compared

Commercial Investigation for Forum

Example: Holiday Marketing for 2015

Commercial Investigation for SaaS

Example: How to Build a Keyword Research Plan

Transactional for Professional Services

Example: Emergency Plumbing Services in Philadelphia PA

Moving Beyond Text Content

As consumer research continues to increase in sophistication, so must the content that is being used to rank and engage customers. Here are examples of other content formats used to rank and acquire those qualified eyeballs.

Events (webinars)

Example 1: How to Make Webinars Your #1 Acquisition Channel

I love that this is a landing page for a recorded webinar. What I love even more is that results 3 and 4 on the SERP for “webinars as acquisition channel” are the Vimeo video of this webinar and then the SlideShare deck.

Example 2: Webinar Marketing: 15 Steps to Revenue Generating Webinars

Awesome text-based guide that ranks for “how to use webinars for marketing”

Presentations

Example: How to Grow Taller 3 Inches in One Week

Ranks #2 for the keyword “how to grow taller” and 824 other related keywords that have an aggregate total average monthly volume of over 100,000 searches/month. Don’t discount presentations OR Slideshare for that matter.

Videos

Example: How To Create A Website – Make A Website in 15 Minutes

This is actually a great example of “flip content” , i.e. it flips the users intent from the informational keyword they came in on to commercial investigation; and then uses affiliate links to push the transaction.

Podcasts

Example: Crushing It With Amazon Affiliate Sales With Chris Guthrie of MakeMoneyOnTheInternet.com

over 140 rankings for keywords related to “amazon affiliate” with over total aggregate monthly search volume of over 40,000 searches/month.

Designing Content for Organic Performance

Now that we’ve covered the different types of content that can be created to support mapping your target keywords to your content production process – the next consideration is to design this content for maximum performance.

There are 2 considerations at work here:

1. Where does the content need to live in order to stand the best chance to be ranked, and
2. How do you design architecture for your site or page to build the most scalable foundation for rankings.

Jumping into the first point, I want to explore how to leverage pages and sites to quickly rank for moderate to difficult keywords. Sometimes you can build pages on your existing website in a way where they will be able to quickly crack rankings for related terms, and sometimes you will need to use other sites root authority to get your content in front of the eyeballs you’re after.

NOTE: One key consideration here is what SEO is really about, which is getting your message (your content) in front of the right people.

Rankings are the road to doing this – but you need to break away from the idea that you need to be the owner of the website; you just need to be the crafter of the message.

Back to deciding where content needs to live on your site to stand the best chance to rank for related terms.

Tenant SEO

If you’ve ever rented an apartment you immediately recognize the word tenant, and can relate to the idea of renting your space.

In some instances what I’m referring to is called Parasite SEO, where you stand on the shoulders of giants and take advantage of their massive DA to rank your

content – the difference is I want to explore some additional routes to get the rankings you want; even if it’s not ranking pages on your website.

There are 2 ways to leverage the parasite strategies;

1) Pure black-hat, as reviewed in depth here, where you are essentially hacking other people’s websites to inject your content and links without their permission.

I am 1000% against this, and then

2) There’s finding sites that already rank well in the SERP’s you’re targeting that host 3rd party content; your content. These can be smaller niche publications or big behemoths like YouTube, Wikipedia, Facebook, Yelp, etc.

There’s a great write up on how to identify these pages for your keywords here.

How to Determine If You Should Be a Tenant vs. an Owner?

Same way you would decide if you’re ready to buy a house – time and budget.

If you don’t have the $50k+ for a down payment and aren’t ready to make the long-term commitment to attack a SERP with an average DR of 70, then you need to look at your rental options.

To be clear, when I’m saying rental here I’m not necessarily saying you will only find sites where you need to pay an on-going monthly fee to keep the rankings (although that may be the case in some verticals), instead I’m simply talking to the idea of you propping up rankings on sites you don’t own.

For Ecommerce – this could be on Amazon, Fab, or Etsy.

For Lead-Gen – this could be on Yelp, E-How, or YouTube. Especially for Lead gen this is going to come down more so to your specific vertical – and who the larger 3rd party aggregators are in your space.

Let’s say you’re a Carpenter in Austin, TX – besides getting your own Google My Business page up to snuff, you’ll want to make sure you have prominence on the big 3 sites that host Tenants like your business; Thumbtack, Yelp, and Houzz:

Or if you’re a personal injury attorney in Chicago, you want to make sure you have a presence on FindLaw, Thumbtack, and Avvo:

For Bloggers – this could be on a larger publisher in your space; a great example I have from doing this myself is the ranking for reduce bounce rate, where I realized due to the competitive level of the SERP, I would need to lean on a larger authority site if I wanted my content to have a shot at the organic rankings for related terms…

I’d say it worked:

A good general rule I like to use to decide if I should target rankings with one of my owned properties or it would be better to use Tenant SEO is a very simple formula:

If the average Domain Authority of the SERP is more than 15% higher than the current DR of my owned property – it’s a good fit for Tenant SEO.

Approaching Information Architecture for SEO

Each type of website has i’s own nuances when it comes to establishing relevancy and being scored high enough against its competitors to gain the rankings that will move the needle. In the next section I go through the nuances of each with specific examples of how’s it working in the wild.

For Ecommerce

Information architecture is perhaps the most important consideration for Ecommerce websites, more than any other website type.

The biggest wins I have seen have come not from applying a specific set of rules to all sites – because not all products and services are searched for the same way, thus no 1 set of “rules” is a blanket solution.

Instead, and especially for Ecommerce, you need to look at the specific nuances of the product catalog and customer behavior around researching purchases.

To do this, you need to organize your keywords into topical and product based buckets, and then analyze them, dissecting the queries to uncover patterns – and ultimately using THIS data to design your architecture.

You may come to find that specific product sub-categories have little to no search volume, and would be better served as results in other existing sub-categories.

Or, this could mean creating new sub-categories all together if your research identifies that there are nuances between products that are driving search behavior.

For example, let’s say you’re selling widgets, and your whole site is dedicated to a mix of these products.

Let’s say the names of these product groups of widgets have different names so:

  • mechanical widgets
  • commercial widgets
  • aluminum widgets
  • steel widgets

And then within those product groups (or categories) there are sub-categories of colors:

  • Mechanical widgets
    • Blue
    • Red
    • White
  • Commercial widgets
    • Blue
    • Red
    • Black
  • Aluminum widgets
    • Brushed
    • Polished
  • Steel widgets
    • Coated
    • Unfinished

And then to take it even one step further, the actual products require a specification at a 3rd architecture level for the length of the widget.

Remember this is just an example, so don’t get tied to the idea of length, but instead consider some configuration attribute at the actual product level, so height, weight, size, etc.

Your Keyword Research Identifies

That while people are searching for “blue mechanical widgets” and “polished aluminum widget” there is no registered search volume for the lengths, so 7” polished aluminum widget” is not actually a good keyword to be targeting with a dedicated URL.

Believe it or not, this happens all the time.

Here’s how to best address this scenario in terms of architecture design; design your URL’s to support the terms that actually have search volume, and then use behavioral design to pick up all the functional configurations that customers *need* but don’t search for.

Nick, what the hell does that mean?

I’ll show you.

For Ecommerce

Here’s how I would design the URL’s for the above scenarios, putting a keyword emphasis specifically on the category and sub-category levels:

  • /mechanical-widgets
    • /mechanical-widgets/blue
    • /mechanical-widgets/red
    • /mechanical-widgets/white
  • /commercial-widgets
    • /commercial-widgets/blue
    • /commercial-widgets/red
    • /commercial-widgets/black
  • /aluminum-widgets
    • /aluminum-widgets/brushed
    • /aluminum-widgets/polished
  • /steel-widgets
    • /steel-widgets/coated
    • /steel-widgets/unfinished

Ok so we’re putting a strong targeting effort at the head and tail terms for the product categories… let’s add another common element to the product configuration; brand.

So let’s say our Ecommerce site has 5 brands of widgets; Brand A, B, C, D, and E.

We need to target the brand terms but looking back at our keyword matrix we see that the brands are used almost exclusively in 2 scenarios:

  1. For product specific searches, like “Brand B brushed aluminum widgets,” and
  2. For branded category searches, like “Brand A steel widgets” or “Steel widgets Brand C”

So how do we extend this architecture to also target these 2 important keyword buckets simultaneously?

We target them for what they are; a category term and a product term. So we create a category to go after the category terms, something like:

  • /brand-a
  • /brand-b
  • /brand-c
  • /brand-d
  • /brand-e

Placing those, like any properly optimized category directory, in the root directory of the website, and then building branded product content on those pages that then links down to the product categories.

There’s 2 good ways to do this:

  1. Having the branded product categories live underneath the brand directories, for example; /brand-a/commercial-widgets
  2. Or having the brand be a parameterized URL that has a static page title, for example; /commercial-widgets?brand=brand-a

Then to target the product keywords, we place the products somewhere in the architecture where they can live harmoniously between many categories and sub- categories (if necessary) – so in the root.

Something like /brand-a-brushed-aluminum-widget-[SKU]/

Circling back to that 3rd level of product attributes

For this example we had said that these widgets also had to have the length specified, but that this product option should not be represented in the URL (at least not in an indexable way).

So the way I’ve seen this handled before is for Ecommerce websites to simply add another level or architecture to their URL’s, something like:

  • /steel-widgets/unfinished/7-inches
  • /steel-widgets/unfinished/9-inches
  • /steel-widgets/unfinished/15-inches
  • /steel-widgets/unfinished/18-inches

But remember, this has no search implication, and by implementing this way we’re actually diminishing the amount of content that “lives” at the sub-directory, and instead forcing it down to these sub-sub-categories…

A Better Solution

Would be to either push these filtering options outside of the indexable URL parameters, into hashtags for example; /steel-widgets/unfinished#7-inches – OR to simply use AJAX or JSON to not render them at all.

So as they are toggled on and off in a faceted navigation of checkboxes in one of the sidebars, the user sees the total results change at the sub-category level BUT Google simply sees all the beautiful sub-category specific content on this page versus dividing it up over 4 weaker pages.

For Lead Generation

The concept of “siloing” for SEO is not new, and in a lot of cases this strategy for organizing content can work really well for building out large sets of vertically integrated content, especially when targeting head and body keywords.

Siloing for those unfamiliar is the practice of picking a couple very important keywords and then simply having all the content build on the entire site live in those few buckets.

I’ll actually give you a real world example from one of my old lead gen sites in the attorney space.

The site was divided into 5 core target keywords; big important – super difficult keywords:

  1. Divorce attorneys
  2. Personal injury lawyers
  3. Workers compensation lawyers
  4. Patent attorneys
  5. CriminalLawyers

So those became the top-level categories for the site, and then all articles and resource pages lived underneath them, so for example the pages in the “Divorce Attorney” category had URL’s like:

  • /divorce-attorneys/21
  • /divorce-attorneys/22
  • /divorce-attorneys/23
  • /divorce-attorneys/24
  • /divorce-attorneys/25

…you get the point.

However, like all things in SEO, there is no hard and fast rule that this architecture design will work for your website. It’s not only keyword specific, but more so, driven explicitly by your competitive landscape.

In each vertical market, as SERP’s shift and sites gain importance and relevance over time, the benchmark of those individual SERP’s evolves.

The best set of competitive data points for any SERP is to first look at who and how the current ranking sites are built.

So for lead generation, I want to look at a national site here in the USA that is killing it leveraging a localized architecture in one of SEO’s most competitive verticals; cars for sale.

The site is CarGurus.com.

This team has decided to silo their entire top-level architecture under a master /Cars/ directory. Personally I find it very interesting that they force the capital “C” vs. the standard best practice to force lowercase.

The next architecture element that jumps out is their pattern design; “new cars” focuses on the “new” modifier where “used cars” instead focuses on “for sale.”

This is definitely due to the differences in behavioral intent behind people searching for “new” cars where as people using the “for sale” modifier are more often looking for used cars.

All of the child directories build under the master parent directory and then localize using GeoIP to dynamically change content results AND the page title. At the deepest level of localization (e.g. car model + city/town name) the architecture supports a detailed end point and no longer considers the semantic needs of the keyword architecture.

Let me give you some examples:

  • Used cars: http://www.cargurus.com/Cars/forsale
  • Used {brand} car: http://www.cargurus.com/Cars/l-Used-Mercedes-Benz-m43
  • Used {brand} {model} car: http://www.cargurus.com/Cars/l-Used-Mercedes- Benz-C-Class-d66
  • Used {model} car {listing}: http://www.cargurus.com/Cars/l-Used-Mercedes- Benz-m43#listing=120143718

*Note use of hashtag to not build erroneous additional level of navigation at listing level.

Geo listings based on user input: http://www.cargurus.com/Cars/inventorylisting/viewDetailsFilterViewInventoryListing.action

Geo listings based on search query: http://www.cargurus.com/Cars/l-Used- Mercedes-Benz-Philadelphia-m43_L30895

At first look the URL right above seems to have a US postal zip code appended, but since the zip code in Conshohocken, PA is 19428 and 30895 is for Elkhart Indiana, this is not the case and is instead more likely a static localize ID assigned to the Philadelphia area.

For Publishers and Bloggers

For people who need to maximize the total number of visits and pageviews, traffic is the holy grail of success.

SEO for publishers is a different beast; the vast majority of traffic for publishers comes from a mix of time sensitive (news focused) stories and then more evergreen, explainer and how to content.

Architecture for these types of sites can vary significantly from being completely flat (like Wikipedia) or more category driven architecture, like SearchEngineWatch.com (SEW).

Since most publishers tend to operate with some form of category architecture, let’s take a closer look at SEW.

Top level directory focused main navigation;

  • http://searchenginewatch.com/category/industry
  • http://searchenginewatch.com/category/seo
  • http://searchenginewatch.com/category/ppc
  • http://searchenginewatch.com/category/analytics
  • http://searchenginewatch.com/category/social
  • http://searchenginewatch.com/category/local
  • http://searchenginewatch.com/category/mobile
  • http://searchenginewatch.com/category/video
  • http://searchenginewatch.com/category/content
  • http://searchenginewatch.com/category/development

Then they actually silo ALL content within each of these top-level categories into a set of topic driven categories, abandoning their core category architecture at the individual article level:

Post in SEO category
http://searchenginewatch.com/sew/how-to/2436232/what-are-the-busiest-times-for-stores-on-black-friday

Post in PPC category
http://searchenginewatch.com/sew/review/2433998/lowe-s-the-customer- journey-from-search-to-checkout

Post in Analytics category
http://searchenginewatch.com/sew/study/2435601/are-thanksgiving-traditions-a-relic-of-the-past-a-google-trends-investigation

Post in Social category
http://searchenginewatch.com/sew/opinion/2423532/5-steps-for-infiltrating-communities-for-links

Post in Local category
http://searchenginewatch.com/sew/news/2427721/on-page-link-signals-the-most-important-local-ranking-factors

So from a quick look at the URL’s in play above, beyond the /sew/ parent folder that, to be honest, I can’t quite figure out – notice the topic driven folders?

There’s 5:

  1. How to
  2. Review
  3. Study
  4. Opinion
  5. News

This architecture approach supports a cross pollination approach where the information architecture of the site actually runs separate from the URL architecture.

SEW is using global on-page links (like the main navigation) to drive top-level relevance downstream inside their main content categories (all digital marketing related) versus the cross structure of topical related categories.

If I had to guess, my knee jerk reaction here is that they did this so they could cross-file posts in multiple categories without any need for canonical tags, but poking around there site for a bit – I can’t find any obvious examples of posts that live in more than one category.

Designing a Keyword Funnel

A keyword funnel is exactly what it sounds like; it’s a content-driven experience that pushes your visitors toward conversion.

The best keyword funnels solve a specific problem at each stage – and the stages of your funnel are best designed to match the stages of your sales cycle.

So, if you have a pre-qualification questionnaire for prospects to determine if they have the right kind of pain to pay for your solution – as your very first step, then this is the first step in your funnel.

Keyword funnel’s can range from simple content pieces that nurture visitors through a series of micro-conversions, dropping them into an educational series or drip feed to ultimately pull them into being customers –or– they can be slick web apps that quickly pull emotional triggers to drive sign-ups and get them deeply invested.

When talking keyword funnels for SEO, it’s sketching out the stages of your sales cycle, scoring them based on intent – and then pruning the right keywords from your matrix to make sure you are serving the right content at the right time.

I like to use examples from my own websites whenever possible, so I’ll show a dead simple approach we take on one of my Ecommerce websites that has content for each stage of our conversion funnel – based on keyword intent.

The site I’m going to dissect for this is TrafficSafetyStore.com.

The head terms for this site, like most Ecommerce sites, are used as the categories, so let’s take a look at traffic cones:

The page targeting all of the variations for the main target keyword “traffic cones,” lives at trafficsafetystore.com/traffic-cones, and it currently ranks #1 (organically) for all of the 1-word head synonym terms, including, traffic cones:

Moving onto the body terms, for this product group these are comprised of colors and sizes, like “orange traffic cones” and “28 inch traffic cones.”

Which we target at the sub-category level, that lives at trafficsafetystore.com/traffic-cones/orange-lime, which (unfortunately) we don’t currently rank #1 for – as this is a commercial investigation term, and thus garners significantly more competition… from some big players:

And then there are the transactional keywords, where we want to get visitors to a product page, like for the query “28 inch traffic cones”

In this example, the architecture mirrors that in my widgets example from above, where there are core product attributes that have enough search volume to have implications on the URL – but then there are additional configuration options for the products, that drive user behavior; but have no search volume.

An Production For example weight, stencil, and reflective collars:

None of which influence the indexable URL;

  • URL for 10 lb. cone: trafficsafetystore.com/traffic-cones/28#CR28HD
  • URL with custom stencil: trafficsafetystore.com/traffic-cones/28#CR28SS
  • URL for reflective collars: trafficsafetystore.com/traffic-cones/28#CR28SRC64

Not only does this architecture maximize the importance of only the keywords that matter in terms of SEO, it also decreases the number of pages you need to build links to by an order of magnitude.

Create Multiple Points of Entry

As important as it is to thoughtfully map out how your website experience needs to map content based on keyword intent to your shopping funnel – you need a strategy to maximize the number of entry points for each stage.

For each stage of the funnel, you must leverage content connectivity to move visitors down the funnel, baking in multiple layers of qualification along the way; so called “micro conversions” like:

  • Engaging with a how to
  • Signing up for a guide
  • Joining an email list
  • Downloading a PDF
  • Registering for an event

Or more specifically for Ecommerce:

  • Adding a product to their cart
  • Creating an account
  • Creating a wishlist
  • Posting to social media
  • Saving their cart
  • Getting a shipping rate

So all of your funnel content needs to be aimed at serving one of the following specific purposes:

    1. Advance visitors downward through the funnel.
    2. Engage users and capture the information needed to get back in front of them.
    3. Leverage re-marketing opportunities to re-engage, and pull users back into the funnel in the next stage.
    4. Drive minimum commitment conversions, like signing up for something(even if it’s free) or getting a minimum financial commitment (even if it’s$1).
    5. Pull an emotional trigger, create urgency, capitalize on FOMO, or translate enough value to acquire a new customer.

Final Notes

I’d like to take a moment and thank you again for taking the time to dive into this guide. As much as I appreciate your business, I appreciate your interest in SEO.

I wanted to leave you with a few thoughts

The Role of Links & The Social Graph

In addition to having a bulletproof information and URL architecture – links still matter.

I completely agree that the role of links in the overall signaling and scoring algorithm has diminished in recent years; however we are not yet at a point where you can discount links completely.

Time on site factors and 3rd party engagement metrics (e.g. social signals) also play a role in SEO, in that these are likely used to affirm trust.

Content that deserves of attention and high rankings needs to show additional trust signals in addition to gaining high trust links; these come in the form of:

  • Social signals
  • Comments
  • And even unlinked citations/mentions

Google pays attention to these factors – this is why, as once said by Ian Howells

“shit that’s popular tend to rank well.”

I want to invite you to have a real conversation about this, drop any comments you have below, and if you found anything in this post valuable (even if only slightly) – please consider sharing it 😀

Click For Original Article

Deja una respuesta

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *