Maximize every click with search journeys7 min well spent

I’ve been writing more about the difference between commodity content and content moats lately. But one topic that feels underserved in the “content is important” conversation is keyword research.

Keyword research is a staple of SEO but doesn’t seem to have evolved with the rest. In 2018, I wrote about a social-driven approach to keyword research and recently about tuning content to rank for more keywords.

Today, I want to write about structuring keyword research to reflect search journeys.

Maximizing every click

“Maximizing every click” is the name of a strategy I employed at G2, intending to capture users when they landed on our site.

I’ve been a big advocate of the notion that it’s harder to come by traffic. First, the barrier to content creation is lower than ever before, which means we see more competition. Second, Google uses more machine learning to understand real user satisfaction, making it more complex for SEOs to control every ranking aspect. Lastly, Google answers a lot of queries directly with SERP Features. So, how can we make the most out of a click once we got it? How can we keep users on the site and lead them to convert faster?

The solution is to anticipate the users’ next step in their search journey. Every search is a journey that brings users from problem realization to solution. On the far end, “Single Search Journeys” start and finish within a single search. More complex journeys can span over hundreds of searches (example follows in a moment).

Forget everything you know about the marketing funnel. Today, people are no longer following a linear path from awareness to consideration to purchase. Instead, they are narrowing and broadening their consideration set in unique and unpredictable moments. People turn to their devices to get immediate answers. And every time they do, they are expressing intent and reshaping the traditional marketing funnel along the way.

Think with Google: how intent is redefining the marketing funnel

All too often, we get lost in the notion that a funnel leads users in a straight line from awareness to decision. However, most search journeys are messier. In a paper from 2016 (!), Think With Google shows how many digital touchpoints users have when going through more complex search journeys. Google used clickstream data to analyze searches, clicks, visits, and video views of several people when planning a trip. In one of the case studies, “Snapshot of a traveler’s decision-making journey,” the participant has over 850 digital touchpoints over the course of 3 months! She performs 166 searches total, 24% of interactions happened on mobile, and 24% of interactions happened on maps (19% on Search).

Most of these interactions are difficult to track: users switch devices, visit many sites to compare results, come back irregularly, etc. I think we’ll understand journeys better with machine learning solutions in the future, but today they can be challenging to analyze.

One workaround is Google’s related searches, which you can find at the bottom of the SERPs.

Search Journeys with Related Searches

Google’s Related Searches evolved from a collection of links to images (example below) and sometimes even videos and knowledge graph features.

We can use Related Searches to find the next steps in Search Journeys.

Look at the example “How to start a business”:

When we throw these queries into Ahrefs, we find quite a bit of search volume: 22,000!

I’ve been critical of search volume in the past. It’s by no means a great metric, but at least Ahrefs provides us click estimations.

Now, you can either create content for all queries or narrow it down to the ones that fit your business model. Each of them would probably qualify for a separate article. We can verify that by comparing the results and seeing if one page ranks for many queries or just one. The latter could indicate that Google prefers separate pieces of content.

A quick way to do that is using Ahrefs “traffic share by pages” feature:

You notice that no page ranks for all nine queries. Official government sites like sba.gov or usa.gov rank for 5-6 at most, and not even that well.

The top page, https://www.sba.gov/business-guide/10-steps-start-your-business, gets 79,000 clicks a month, according to Ahrefs’ Site Explorer feature (make sure to select “exact URL”).

The page ranks for two keywords on position one and on page 2 or lower for the other four.

That tells us that we can’t meet all user intents with a single page, but we can rank for “how to start a small business” and “steps to start a small business” and get over 3,600 clicks a month. So, a single piece of content can target a few queries with the same user intent here, but it looks like we need one content piece for almost every query.

We can now go deeper into the journey by clicking on the related searches and seeing other Related Searches pop up. In the example above, “how to start a business,” I clicked on “How to start a business without money.”

Here are the related searches:

Once again, more content ideas with decent search volume (and high intent).

You can also use Ahrefs’ “Parent Topic” to decide what queries to target with a piece of content.

Again, Traffic Share by Pages shows us how to cluster queries:

What’s the takeaway?

To summarize, we should probably aim to connect our content pieces to form a journey that reflects the real user journey as good as possible. Doing that should keep users on your site longer, which means you “squeeze” more out of a click than before. Blogs probably aren’t the best format for that unless you can control what article the user sees next.
There are many paths to do keyword research, whether you use information from social networks, search volume, or SERP Features. In fact, Google gives us a lot of information right in the SERPs! The search engine wants to be “helpful” for users, which is best done by either providing a direct answer or guiding users along their Search Journey as good as possible. We, as SEOs, can use the SERP to learn from Google, which learns from user behavior in return.