SEO & Digital Marketing
SEO is one of the disciplines that has been part of my professional path for the longest time.
For many years it was a central part of my work, and it still influences the way I design platforms, assess content and study search systems.
I have never seen it as a collection of checklists and best practices.
For me, SEO is largely about observation, experimentation, intuition and being willing to change your mind when the data tells a different story.
An empirical discipline
Search engines are complex, opaque systems that constantly evolve.
A large part of the work therefore consists of forming hypotheses, running tests and understanding which variables actually produced an effect.
Not everything can be inferred from official documentation, and something that works on one project will not necessarily work on another.
I continue to test structure, content, linking, rendering and crawling to separate what actually works from what gets repeated simply because “that’s how it’s always been done”.
Some of the most interesting parts of SEO are precisely those where there is not yet a definitive answer.
Method, intuition and context
Method remains essential, but it is not enough on its own.
Data and tools matter, but experience helps identify where it is worth looking.
Sometimes an apparently minor change can produce significant effects; at other times, technically correct changes produce no meaningful result.
After years of working in the field, certain anomalies stand out before you know exactly what caused them.
That is still one of the aspects of SEO I find most interesting.
Technical SEO
My background in computer engineering naturally led me towards the more technical side of SEO.
Over the years I have worked on crawling, indexing, information architecture, rendering, performance, internal linking, redirect management, canonicalisation, duplication, structured data and platform scalability.
Technical SEO becomes much more complex when it is applied to non-trivial platforms.
CMSs, custom applications, JavaScript frameworks, CDNs, caching systems, cloud infrastructure and deployment pipelines can all directly influence how a platform is discovered and interpreted by search engines.
Whenever possible, I prefer to address these issues during architectural decisions rather than fixing them once the platform is already complete.
Spam and result quality
One of the most interesting aspects of SEO is its continuous interaction with spam.
Every ranking system inevitably creates incentives to manipulate it.
Over the years I have seen mass-generated content, site networks, artificial links, cloaking, expired domains and many other manipulation techniques.
The ongoing struggle between search engines and spammers is one of the main forces driving algorithmic evolution.
Studying these dynamics helps reveal which signals search engines strengthen, reduce or reinterpret over time.
The boundary between optimisation and manipulation remains one of the topics I follow most closely, partly because it reveals a great deal about how search engines attempt to identify abuse.
Content and authority
Alongside the technical side, I have also worked extensively on content, authority and search intent. A piece of content can be perfectly optimised and still be of little use.
It has to help someone, be understandable and be verifiable.
On large sites, however, individual pieces of content are not enough: structure, authors, review processes, sources and updates also matter.
On large projects, results rarely come from a single change.
Data and measurement
Experimentation only has value when it is accompanied by measurement.
Analytics, server logs, crawl data, Search Console, monitoring systems and internal analysis make it possible to observe the real behaviour of platforms.
Rankings, impressions, CTR and conversions describe different things; looking at a single metric can easily lead to the wrong conclusions.
Even a failed test can be useful: it removes a hypothesis and shows where it is no longer worth looking.
SEO and artificial intelligence
Artificial intelligence is rapidly changing the way information is searched, summarised and presented.
I am particularly interested in the intersection between SEO, retrieval and generative models.
In recent years, several new acronyms have also emerged to describe this evolution:
- GEO — Generative Engine Optimization
- AEO — Answer Engine Optimization
- AIO — AI Optimization
The definitions are still fluid and often overlap with one another and with traditional SEO.
I do not yet see them as clearly separate disciplines. The underlying question remains similar to SEO: how can a system find, understand and correctly use a piece of information?
Search engines, AI assistants, RAG systems and agents are increasingly offering different ways of accessing the same information.
I am interested in understanding which sources are retrieved and cited, how structured data is interpreted and which characteristics make content easier to use as a source.
On these topics, I prefer testing before drawing conclusions.
Beyond the acronyms, the continuity with SEO is clear to me: observe the system, form hypotheses and verify what actually happens.
SEO as a cross-functional skill
SEO is no longer the centre of my professional role, but I still work on it directly, particularly on the technical side.
It has also left me with a working method I continue to use: observing opaque systems, forming hypotheses and testing them with data that is often incomplete.
That approach has stayed with me well beyond SEO.
DoctorSEO
DoctorSEO is where I want to continue documenting the experimental side of this work.
The aim is to collect tests, real-world cases and observations on how search is evolving, including AI-based systems.
I would also like to document tests that do not work: they are often the ones that help eliminate the wrong hypotheses.
After so many years, I still work on SEO precisely because it never stays still long enough to become predictable.