What ‘Complexity Theory’ Can Teach Us About SEO

Complexity theory is a subdivision of theoretical computer science that deals with the difficulty of solving computational problems. It might not be immediately apparent, but complexity theory (and its many facets) has a relationship to SEO.

For our purposes, it applies, conceptually, to the ever-increasing complexity of performing SEO on a website. Where once we dealt with title tags, meta descriptions, maybe a keyword tag, an H1 tag, and some on-page keyword placement, we now face a massively complex, interconnected web of factors, weights, and impacts.

And today's world of search lives on sliding scales. Item A matters X% if one condition exists. Change that condition and the percent changes, redefining the weight of Item A. And it's safe to say that today the entire alphabet (and then some) represents discrete elements across the multitude of algorithms in play.

In fact, Danny Sullivan from Google just recently alluded to this when he said, "...And of course, as we all know, there's multiple systems that all come into play…" Echoing this concept of complexity and highlighting the need to remain in touch with the bigger picture, Google's John Mueller said, "For a lot of the guidance that we have, it's a lot more now about the bigger picture, because it feels like from a technical point of view, things are often pretty reasonable."

You can use the concepts from Complexity Theory to help you explain to others in your organization, in more detail, how interrelated the factors impacting ranking are. How your program needs the required support, how teams need to work together and to help prioritize work items. Not every issue needs to be perfectly solved, but almost all issues need a solution.

Let's take a look at a couple of examples of how CT overlaps SEO work.

SEO is the process of improving a website's visibility on search engines, with the goal of increasing organic (non-paid) traffic — and, consequently, potential conversions.

Now, think of SEO as a vast, intricate problem that needs resolution. In complexity theory, problems are classified according to their difficulty level, especially as the input size increases. This aspect is termed as "computational complexity," and this is where a direct connection to SEO appears.

Keyword optimization is one of the primary SEO tasks. You're given a set of keywords, and you need to find a subset that maximizes the visibility of your site, where each keyword holds a certain 'value' influenced by variables such as search volume or competition level. As the keyword pool grows, solving this issue becomes progressively more challenging. (We classify this problem under NP, which stands for "nondeterministic polynomial time" – a class of problems where we can verify a solution quickly (in polynomial time), but we don't know if we can find that solution as quickly.)

Similarly, the task of acquiring backlinks, which are inbound links from other websites to yours, parallels the "Traveling Salesman Problem," a renowned NP-hard problem. In this case, the objective is to find a route among different websites that offer the highest 'value' (in terms of traffic, reputation, etc.). However, no website should be repeated in this path, mimicking a salesman who must visit each city only once. This example represents a kind of optimization problem, where the aim is to discover the best solution out of all potential solutions.

Another facet of complexity theory is "space complexity," which relates to the memory an algorithm needs for execution.

Let's connect this to search engine spiders (also known as crawlers, robots or bots), which crawl and index billions of web pages. The challenge of managing this colossal amount of data is akin to space complexity problems: how to store and access this data in the most efficient way possible. This partly explains why, when a page is indexed, different components may be stored in different "areas." (The way to retrieve words most efficiently doesn't necessarily work as well for retrieving images, for example.)

Moreover, there are specific algorithms in search that might not provide the absolute best outcome but rather a 'good enough' one within a reasonable timeframe. These are heuristic methods often used to analyze user behavior data and inform SEO strategies. This principle reflects a common approach in dealing with NP-hard problems: where finding the absolute best solution is computationally too exhaustive, a 'good enough' solution is acceptable.

In summary, complexity theory provides a lens to understand the inherent challenges within SEO, shaping how practitioners approach their work. As we've seen, tasks like keyword optimization or backlinking are computationally complex — mirroring various problems studied within complexity theory.

As SEO continues to evolve, grasping the fundamentals of complexity theory will become increasingly crucial, guiding us in the development of more efficient and effective solutions.

Similar to energy transfer and interference — where dropping a rock in a pond creates ripples, but dropping a second rock exponentially increases the number of ripples as energy intersects and reflects — SEO is highly complex. Today's SEO spans far beyond the work we were doing just 10 years ago.

Fundamentally, we still need to manage the baseline: data, site structure, usability, and more. Additionally, we must also account for consumer behavior, mass reactions to online/offline stimuli, and new vectors of influence (social media, wearables, voice inputs, etc).

But as we move forward, the work will only grow more complex — and it will require us to know not just about what is current today, but about what is evolving for the future. Theories like this one can help us get there.

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