It’s no mystery that in the SEO industry there is a desire to abandon the “SEO” label due to negative perceptions and skill-limiting connotations. Professionals are trading in the term “SEO” for newer, fresher titles like “inbound marketer,” “earned media marketer,” and even sticking strictly to “content marketer.” While some are abandoning the three letters we’ve come to love so much, and shifting to something that sounds good for the moment, my personal strategy has always been to invent my own way. This tranformation comes as natural progression from what I’ve learned over the past 3 years, as well as what is sought from me in my professional environment. Here I’d like to explain what exactly that is, and what it means to be a Search Experience Architect.
As Gianluca said so eloquently in What The Heck Is SEO? A Rebuttal “It is not simply ‘search engine optimization’ (we don’t optimize search engines), but more ‘search experience optimization.’ Users are the main focus of real SEO, and that is something Google itself is preaching to all site owners (not just to SEOs).”
While I agree with this statement, I do think the term “optimization” hints at a more abstract concept, than what I am going for. I prefer the more scientific term of “architecture” as it implies a more objective and data-driven discipline. Additionally, it implies the building or construction of an experience, rather than endlessly modifying one. After all, each search intent, task, or goal is an experience in and of itself. This fact does not necessarily warrant constant optimization, but rather construction. Just as you identify content gaps, one can also identify intention gaps which are fundamentally a precursor to content itself. In addition, the shift in attention to landing pages (because most keyword data is not provided) works with this model of experience construction.
There are 4 main components of a Search Experience Architect: SEO, UX, IA, and Analytics/Data Science.
Everything is founded on the skills of a seasoned SEO expert. The classically trained SEOs start with the foundational elements, including the technical skills to diagnose problems, configure sites properly, keep tabs on algorithm changes, perform keyword research and competitive analyses. This also includes the newer skills of content marketing, social media (and social search), community building, and mobile search. At its core, the SEO discipline is a study in how people search, and most importantly, how to get your answer in front of them. I don’t need to elaborate on what classical SEO is here, but I should reiterate these individuals are still hard to come by. It all start with SEO, because as Peter Morville said, “Findability precedes usability, in the alphabet and on the Web. You can’t use what you can’t find.” Plain and simple.
Once you’ve achieved organic traffic, the focus then shifts to retaining visitors in order to achieve the long click. This, in Google’s eyes, is the most important factor of UX. Keep people from returning to the search results, and thereby signal a satisfactory answer to the user’s queries. Even beginners know Google’s focus is shifting to one based on user experience than it is about anything else.
Designing the user experience is a logical next step after doing market research, keyword research, creating a search-friendly platform, and becoming fully engulfed in the customer mindset. You know what your customers need in order to make a buying decision, now go build it. This includes content strategy, content requirements, HTML sitemap, user stories, use cases, and personas. Because organic search and site search have many of the same principles, I’m also including it within the search experience. Information seeking is information seeking, no matter the platform. All of the skills of a UX professional are applicable here, for the end-goal of positive engagement metrics (again, stemming from organic search). Of the 4 disciplines in this post, UX is the one I need to catch up on the most.
The classical forms of IA apply as well. How information is organized within the site and within each digital asset, metadata, and more, is basically user experience for robots and crawlers. I’ll be succinct and present the definition Lou Rosenfeld uses in his book: 1. The structural design of shared information environments. 2. The combination of organization, labeling, search, and navigation systems within web sites and intranets. 3. The art and science of shaping information products and experiences to support usability and findability. 4. An emerging discipline and community of practice focused on bringing principles of design and architecture to the digital landscape.
IA concentrates on data, information, and content in regard to structure and organization. So, I’ll also place structured data here. Although the main advantage might be the 31 pieces of SERP flair, it is a type of building material for a website, like sheet-rock or plywood in the construction of a home. By defining bits of information into recognizable classes, search engines and crawlers have an easier time determining the types of data being presented. Data types help determine the instances for it to be served in context with search queries, devices, locations, or even modifiers that suggest intents. With this operational definition of structured data, Authorship and Publisher(ship?) also belong here as they serve a similar function.
Analytics and Data Science
Without analytical skills, the classical SEO practitioner is nothing more than a glorified copywriter. And, let’s face it, our data sets aren’t getting any smaller. While we have the luxury of small data right now, eventually our data needs will become so large we’ll be doing the same tasks people in the fields of healthcare, astronomy, and other ‘big data’ disciplines are doing now. I see this beginning already for us, with more link data, more brand mentions, more social media data, more onsite data, more market research, etc. So, I say this; tomorrow’s web analytics is today’s data science.
I’ve begun this effort by enrolling in a class through the University of Washington on the subject, and was excited to see both PageRank and latent semantic analysis on the syllabus. The first assignment was to write a Python script to classify the sentiment of incoming tweets through the API, and slight variations of this. While it might not have any real-world value, the ability to scrape data, convert it into a unified format, query large data sets, run scalable analyses, return “data products” and communicate findings (both visually and summatively) into business intelligence is absolutely critical. Although the term “big data” is tossed around so often, it truly is something to start learning about now.
As a Search Experience Architect, data science helps uncover patterns in site usage, off-site research methods, and do testing on a massive scale. Let’s face it; if you work on a site with millions of daily visitors, you need the precision of a surgeon (data quality) and the habits of a hoarder (data quantity) to tease out statistically significant, actionable insights. Sample sizes are so large, and simultaneous site releases produce so many overlapping variables, that this skill is an overwhelming necessity.
My change in label is also a change in mentality. As a marketer, this is direct statement that I’m stepping up my game. The rules may be different but the goals are the same: organic search traffic, positive engagement metrics (onsite and off) and meeting/exceeding business goals. If you’re up for it, I invite you to join me in the emerging field of Search Experience Architecture.