Beyond Keywords- A Systems Theory approach to SEO and GEO.
Why a focus on Generative Engine Optimisation as well as traditional Search Engine Optimisation?
Search is evolving. Google remains the dominant driver of global organic traffic, but generative engines are rapidly emerging as a new search paradigm. While traditional search still accounts for over 90% of total queries, adoption of AI-driven platforms for informational, navigational, and transactional tasks is accelerating. To remain competitive, businesses must extend their SEO strategies to include Generative Engine Optimisation (GEO) strategies, optimising for visibility within generative engines such as Perplexity and ChatGPT- this becomes more important, as user behaviour shifts toward these AI-driven environments.
A powerful asset for advancing both SEO and GEO strategies is a deep application of systems theory.
What is systems theory?
Systems theory is an interdisciplinary framework that studies the interrelationship and interdependence of components of entities and systems. The study of isolated components of a system fails to account for the properties and augmentation that can occur from interconnectedness. Ludwig von Bertalanffy’s General System Theory (1968) is widely recognised as the formal foundation of this framework, articulating that one must examine the relationships and feedback loops that allows a system to become interconnected.
The main ideas of system theory we will research in this article include:
Wholeness and Interdependence: The system is greater than the sum of its parts. Individual components interact to produce emergent properties that cannot be predicted by studying them in isolation.
Hierarchy and Nested Systems: Systems often exist within larger systems (supra systems) and contain smaller subsystems, reflecting multiple layers of organisation.
Feedback Mechanisms: Both positive and negative feedback loops regulate system behaviour, maintaining equilibrium or promoting change.
How does systems theory relate to SEO?
Search engines often get described as digital ecosystems, I believe this is an accurate metaphor. The digital landscape is multifaceted and requires balancing technical, content and behavioural components to ensure organic ranking and indexing in search results. Many SEO practitioners study these techniques as isolated contributors to an SEO campaign. However, applying systems theory offers a more holistic perspective. By emphasising wholeness and interdependence, hierarchy and nested systems, and feedback mechanisms, systems theory provides a framework for developing adaptive, resilient SEO strategies that align with the complexity of search engine algorithms and user behaviour.
Much like living systems, SEO performance emerges from the dynamic interaction of numerous interdependent factors. This paper explores three central systems theory concepts: wholeness and interdependence, hierarchy and nested systems, and feedback mechanisms, this article will demonstrates their relevance to optimising for search engine and generative engine rankings.
Wholeness and Interdependence
In systems theory it is suggested that the total value of an entity or system is more than the sum of the value of the parts that operate within it, the added value comes from the interconnectedness and interaction of the parts to augment the final result.
This principle maps directly onto SEO, where technical optimisation, content, link equity, and user experience operate interdependently to drive visibility. A site may publish authoritative long-form content, maintain a strong internal link structure, and attract high-quality backlinks, but without technical foundations e.g. schema markup, optimised robots.txt, or an XML sitemap. It risks poor crawlability and indexation. In systems terms, the failure of one subsystem diminishes the effectiveness of the whole. Even with strong assets, the site’s visibility collapses if googlebot cannot efficiently discover, interpret, and index its content. This highlights the wholeness and interdependence of a digital ecosystem and why it is so crucial to SEO that each subsystem is working in tandem.
Natural Language Processing (NLP) advancements in search engines also demand synergy between keyword targeting, topical authority, and structured data. This is because modern natural language processors are becoming increasingly able to discover and categorise context rather than just relying on keywords. Instead natural language processors will look for synergy between these three features in order to gain a contextual understanding of the content and it's relevance to search queries.
Hierarchy and Nested Systems
Systems theory emphasises that systems exist within larger systems and contain smaller subsystems. SEO mirrors this structure through its layered approach. At the page level, on-page SEO techniques such as optimised headings, E‑E‑A‑T signals, and canonicalisation act as subsystems. These pages combine within site-level systems that require advanced strategies like crawl budget optimisation, XML sitemap structuring, server log analysis, and content silos for topical depth. Beyond the website lies the supra system: search ecosystems shaped by algorithm updates, SERP features, and generative engines. Strategies such as international SEO (hreflang), brand authority building, and API-driven integrations with Google Discover or Google News demonstrate the necessity of optimising across nested layers. Recognising and aligning these hierarchical systems allows practitioners to create cohesive strategies that scale from micro-level optimisations to macro-level visibility.
Feedback Mechanisms
Feedback mechanisms also are extremely important to SEO, this is because feedback loops overtime can build up and can create a snowball effect, where one positive signal can directly encourage others. Positive feedback arises when improved rankings drive more traffic, which in turn amplifies user engagement metrics such as dwell time, click-through rates, and brand searches-all reinforcing higher rankings. Negative feedback occurs through signals such as Core Web Vitals failures, spammy link profiles, or declining topical authority, leading to reduced visibility. Advanced monitoring techniques, including AI-driven log file analysis, predictive analytics, and continuous A/B testing of meta titles and schema, allow SEO professionals to interpret these signals with precision. Feedback also operates through off-site factors: content amplification via social signals or earned backlinks reinforces positive loops, while algorithmic penalties or disavow mismanagement trigger negative cycles. A systems-oriented SEO approach embraces these mechanisms, leveraging analytics, search console data, and third-party crawlers to iteratively regulate and enhance performance.
How does this apply to GEO?
Generative engines represent a paradigm shift in information retrieval, moving from keyword-based indexation toward contextual, conversational outputs powered by large language models (LLMs). Visibility within these engines depends less on keyword density and more on clarity, structured information, topical authority, and alignment with natural language processing (NLP). GEO is thus an adaptive system, operating within the broader digital ecosystem of AI and search. Systems theory provides a useful lens for understanding and enhancing GEO, as it underscores the interconnected, hierarchical, and feedback-driven nature of optimisation.
Generative engine optimisation is a much newer and undiscovered discipline than traditional SEO. The signals and factors that improve AI impressions and rankings within generative engines are still becoming clear, however Cornell University researchers have published an article that suggested that generative engines have, "a systematic and overwhelming bias towards earned media (third-party, authoritative sources) over brand-owned and social content, a stark contrast to Google's more balanced mix." This means that generative engines place emphasis upon content that is endorsed or linked to by extremely authoritative sources, for example The New York Times citing your article or a reputable blogger/ researcher linking to a guide your company has published, would improve AI impressions largely. This can be recorded as an external subsystem in systems theory, it contributes to the internal system and can create positive feedback mechanisms.
Wholeness and interdependence for GEO
Generative engines rely on contextual embeddings, semantic relationships, and trust signals to determine which sources to surface in their responses. Forming synergy between entity linking, structured data, natural language, authoritative sourcing, technical markup and high quality long/short form content- is the only way to improve AI impressions of your company. To ensure your source is surfaced first following an AI query a company must ensure that all subsystems are operating and working together to contribute to the emergent value, in order to be able to master AI visibility.
Hierarchy and Nested Systems for GEO
Like SEO, GEO operates across multiple hierarchical levels. At the page level, subsystems include optimised headings, enriched entities, citations, and contextual metadata. At the site level, GEO strategies involve knowledge graph integration, content clustering, source transparency, and consistent attribution across pages. At the ecosystem level, generative models such as ChatGPT and Perplexity act as supra systems. Here, signals such as brand authority, domain-level trustworthiness, and alignment with LLM training corpora influence whether content is referenced. Recognising these nested systems ensures that GEO practitioners address micro-level optimisations without neglecting macro-level factors that determine ecosystem visibility.
Feedback Mechanisms for GEO
Feedback loops in GEO manifest differently than in SEO. Positive feedback arises when content is cited by generative engines, driving user recognition, backlinks, and greater inclusion in AI responses. Negative feedback occurs when low-quality, unstructured, or unreliable content is excluded from generative results, reducing visibility over time. Monitoring GEO performance requires new forms of analysis, including tracking mentions in AI-generated answers, monitoring knowledge graph updates, and assessing entity prominence across multiple generative platforms. By continuously iterating based on these feedback signals, GEO strategies can adapt and remain competitive in rapidly evolving AI-driven search environments.
Conclusion
Taken together, the application of systems theory to both SEO and GEO underscores that optimisation cannot be reduced to singular tactics on their own; it is an orchestration of interconnected subsystems functioning together. Wholeness and interdependence demonstrate that technical, semantic, and authority-driven practices must reinforce each other to generate emergent visibility.
Hierarchical and nested systems reveal how micro-level optimisations, such as entity linking and canonicalisation, scale upward to site-level strategies and influence ecosystem-level (supra system) recognition by search engines and generative models. Feedback mechanisms illustrate how performance signals: whether rankings, backlinks, or AI citations- create adaptive loops that regulate visibility and inform continuous refinement.
Critically, systems theory also highlights the expansive potential of adding subsystems: both internal and external-to strengthen the overall network. Internally, this includes layered content architectures, structured metadata, and entity-based schema that clarify meaning. Externally, earned media, authoritative backlinks, and integrations with knowledge graphs allow for extra contribution towards the system as a whole. The result is a spider-web-like structure in which each strand-whether a blog post, schema markup, knowledge panel citation, or backlink-contributes to the emergent value of an SEO or GEO campaign. This distributed system augments reach, while ensuring quality across subsystems improves visibility in both traditional search and generative engines.
Therefore, systems theory not only provides a lens to understand SEO and GEO but also a blueprint to advance them. By deliberately constructing interconnected webs of content, authority, and structured data, practitioners build adaptive systems that thrive within the evolving digital ecosystem. The future of discoverability will belong to those who can engineer these networks holistically, ensuring that every subsystem contributes to the emergent properties of trust, authority, and visibility.
References
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