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Many large sites overlook the power of internal search and behavioral data.
Internal search provides crucial insights into what your customers want. Here’s why it’s an indispensable tool for your SEO strategy.
Internal search and behavioral data provide the context that unlocks the “why” behind the “what.”
It helps you move beyond the naked keyword and toward a fuller understanding of customer needs and intent.
This process is like a treasure hunt.
Search queries contextualized by refinements, co-occurrence, sorting, location, time of day, week or year, and more provide the necessary clues to elevate your SEO game and give you the competitive edge you need.
Many third-party keyword tools can be a go-to for smaller sites looking for growth opportunities.
However, for a well-established website, external tools will rarely match the behavioral footprints left by your customers on your platform.
Internal search data will provide far more complete, accurate, up-to-date, and meaningful insights that are actionable and relevant to your SEO efforts and overall business objectives.
It’s tempting to chase after a few high-volume terms, and it certainly feels good to outrank the competition in high-visibility areas, but optics alone are not enough for a winning SEO strategy.
Focusing on various aspects of search behaviors to improve understanding of what users want could prove far more effective in the long term.
Even in Google, a good bulk of daily searches are brand-new, never-seen-before queries that present new opportunities.
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Any SEO newbie knows that search engine optimization is about attaining top rankings and getting maximum traffic from search engines.
The web is full of beginner case studies showcasing Google Analytics screenshots with increases in organic sessions, presented as undisputable wins, with no further commentary on the quality or relevance of that traffic.
On the other hand, seasoned SEO professionals understand that the real challenge is capturing eyeballs and clicks that drive value for your business.
And, if your site is tremendous, it also pays to understand which clicks will not drive value and consider whether reducing this unwanted traffic is desirable.
Building an SEO strategy centered around value and business objectives is rarely possible without understanding your audience and how their clicks translate into profit.
At the very least, an internal data-based strategy should consider demand, supply, and location trifecta. If you have what your user needs, where they need it, you’re already halfway there.
Location information can provide enough context behind a search and help determine overall search and purchasing behaviors, seasonal trends, and much more.
This data, often internally available at various geographical levels such as country, state/province, city, township, village or even neighborhood, enables a more tailored, effective SEO strategy.
Take this example: while conducting keyword research for a local marketplace or a classified site, you discover a significant search volume for “firewood” in Canada.
That’s great news, so do you create a top-level informational landing page about firewood, competing with Wikipedia?
Do you go for a well-rounded product page and compete with Amazon and Wayfair?
You’ll likely rule out creating a localized page optimized for “firewood in Canada,” understanding that it’s unlikely that many people would be looking for firewood across Canada, especially from a local marketplace.
This is where internal search data becomes invaluable.
It can provide the precise context needed to understand when and where in Canada firewood-related searches occur – predominantly in rural locations, beginning in mid-summer and reaching a peak in the fall.
This level of insight is enough to initiate the creation of targeted, localized content.
However, people’s willingness to travel varies greatly depending on what they seek.
While some might cross the country for a rare find like a vintage single-engine airplane, services like flower or pizza delivery tend to be local.
For a large site with robust internal search, it’s possible to take the guesswork out of determining the appropriate location level and instead draw insights from how users interact with refinements and sorting features.
Your users will tell you how far they are willing to travel by the way they interact with proximity sorting.
Finally, it’s worth mentioning that not all locations are created equal, and population density, inventory, competition, and geography should all shape the localization aspects of your SEO strategy.
A location entity in a city might cover a relatively small radius – a level of a neighborhood might be good enough to provide a competitive advantage for certain types of searches.
Meanwhile, in regions like Canada’s north, a location might cover vast stretches of hundreds or even thousands of kilometers.
Search behavior is far from random, and almost on every large site, some keyword searches will consistently appear in the same session as other searches.
For instance, users searching for cutlery might also be searching for towels, cutting boards, and can openers.
This might signal opportunities around content that covers kitchen products and furnishings more broadly.
Likewise, an online bookstore that finds that customers searching for “Harry Potter” also often search for “Lord of the Rings” might look into broader fantasy-related content to attract more upper-funnel traffic relevant to the business.
Similarly, recurring combinations or sequences of filters or attributes provide important signals that can be leveraged for SEO.
A word of caution: It might be tempting to auto-generate a maximum number of pages, pairing every category or search with every possible location to see what sticks.
But it’s highly unlikely that everyone is searching for all of your products from every location, and you always have the inventory in supply.
Overdoing it runs the risk of over-indexing, diluting page authority, and producing massive volumes of duplicate and thin content with no real value.
Not to mention the risks of combinatorial explosion and site outages when crawlers go wild – an SEO nightmare.
Internal search and behavioral data can guide the shaping of a site architecture that benefits user experience and SEO.
The idea is an internal linking structure based on a dynamic site taxonomy that factors in local search demand, existing inventory, as well as seasonality and emerging market trends.
Even when full automation is unattainable, striving to keep your data-driven information architecture up-to-date will ensure that crawl paths are optimized to boost the most relevant content to users and search engines.
A robust, well-linked localized taxonomy responsive to search trends and seasonality can help you capture emerging trends before your competition.
Leveraging internal search data is about understanding your customers and their needs.
It enables you to move beyond decontextualized keywords and focus on a cleaner, smarter SEO that will drive value for your business.
As a complex and multidimensional tool, internal data should be indispensable in your SEO arsenal, helping you gain the competitive advantage you need.
Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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