AI has become a key component of major search engine algorithms, including Google. Understanding AI, therefore, has a significant impact on search engines and consequently on your website’s SEO.
Artificial intelligence allows you to analyze a website’s performance and create more effective SEO strategies by comparing data to competitors and industry benchmarks.
In addition, AI provides suggestions on the most critical topics for your business and how to make your website content more effective and make research and text creation faster.
If we talk about SEO, then we can’t talk about AI. This article shows you the top 21 AI SEO tools and specific features you can use to improve your website’s visibility and get a better ranking on Google and other search engines.
In today’s rapidly evolving digital landscape, content creation has become more crucial than ever for brands to engage their audiences. With the emergence of large language models (LLMs) such as ChatGPT and GPT4, natural language processing and artificial intelligence have seen revolutionary advances. While excelling in creative content generation, LLMs face some limitations. A key challenge lies in their ability to access and integratefactual knowledge, real world experiences and above all the brand’s core values. In addition, LLMs can sometimes produce output with hallucinated or fictitious elements, adding a layer of complexity.
Knowledge Graphs (KGs) are crucial to overcoming limitations. They host structured, factual data and provide a solid foundation for training LLMs, ensuring that content is articulated and grounded in reliable information. This synergy represents a substantial step towards more authoritative content driven by artificial intelligence.
In addition, the knowledge graph enhances structured data, refining assumptions about content by infusing brand values into the model. Using an ontology for your brand, product-specific traits can be amplified. For example, when it comes to RayBan, specific materials take precedence. This goes beyond fact-checking by formalizing and operationalizing domain-specific insights.
This emphasizes the central role of ontology, making it clear that semantic data has a sophisticated purpose beyond mere fact-checking.
In this context, we have created a solution for SEOs and content marketers, enabling editorial teams to scale content production while maintaining maximum control over quality and relevance.
Whether product descriptions, restaurant profiles, or introductory text for category pages, our tool delivers reliable results. In this article, we introduce you to our Content Creation Tool and explain why it is so ahead of other AI content creation tools.
Advantages of Complementing Large Language Models (LLMs) with Knowledge Graphs (KGs)
The synergy between LLMs and KGs can significantly enhance the capabilities of content generation systems, making them more accurate, reliable, and adaptable to a wide range of applications and industries. By integrating KGs with LLMs, we can leverage the advantages of both technologies.
Indeed, integrating Knowledge Graphs into Large Language Models can help overcome some of the limitations and challenges of using Large Language Models alone, such as:
Lack of factual knowledge and consistency, such as making errors or contradictions when dealing with factual information or common sense knowledge;
Lack of interpretability and explainability, such as being unable to provide the source or justification of the generated outputs or decisions;
Lack of efficiency and scalability, such as requiring large amounts of data and computational resources to train and fine-tune the models for different tasks or domains.
One way to combine Knowledge Graphs and Large Language Models is to use the Knowledge Graph as a source of external knowledge for the Large Language Model so that it can answer questions or generate texts that require factual information. For example, suppose you ask a Large Language Model to write a biography of Leonardo da Vinci. In that case, it can use the Knowledge Graph to retrieve facts about his life, such as his birth date, occupation, inventions, artworks, etc., and use them to write a coherent and accurate text. This way, the Large Language Model can leverage the structured and rich knowledge of the Knowledge Graph to enhance its inference and interpretability.
This synergy between LLM and KG opens up new possibilities for content generation and reasoning, such as:
It generates more informative, diverse, and coherent texts incorporating relevant KG knowledge, such as facts, entities, relationships, etc.
It generates more personalized and engaging texts that adapt to user preferences, interests, and goals, which KGs can shape.
It generates more creative and novel texts that explore new combinations of knowledge from KGs, such as stories, poems, jokes, etc.
It can store newly generated content and effectively re-use archival content. A KG acts as a long-term memory and helps us differentiate the content we produce.
LLMs and KGs can work together to enhance various content-generation applications. For instance, in question answering, they can generate accurate, concise, and comprehensive answers by using information from KGs in conjunction with context from LLMs. In dialogue systems, they can produce relevant, consistent, and informative responses by leveraging dialogue history from LLMs along with user profiles from KGs. Additionally, they can generate faithful, concise, and salient summaries for text summarization by utilizing input text from LLMs alongside key information from KGs. In constructing AI agents in SEO, they can teach how to answer questions instead of predicting similar sentences.
Keeping the “Human in the Loop” in Scalable Content Production
At WordLift, we advocate the crucial role of human oversight and control, especially when content production reaches thousands of pieces.
Our approach goes beyond simple automation, focusing on meticulous modeling of the data within the Knowledge Graph (KG) and curating and refining the underlying ontology. By identifying the essential attributes used to generate dynamic prompts, we enable companies to train custom language models to maintain a firm grasp on the quality and relevance of their content while meeting rigorous editorial standards.
In our pioneering approach, we’re stepping into a critical battleground between content creators and AI tools. The current landscape is inundated with subpar content churned out by these tools, threatening the deal between search engines and content creators.
This.
All of this is the direct result of people throwing too much (AI) 💩 at the wall and Google having to wash it off. https://t.co/L0UXO55jHl
Our innovative strategy directly addresses this contentious issue surroundinggenerative AI and content creation. Furthermore, our KG-centric methodology is a game-changer. It liberates companies from relying on external data, as it ensures that internal sources suffice for robust language model training. This reflects our dedication to sustainability and underscores the ethical use of AI resources.
In addition, we uphold the implementation of validation rules, adding an extra layer of assurance for precision and error prevention. This comprehensive approach seamlessly marries the potential of AI with the human touch, culminating in content excellence, fortified editorial control, and eco-conscious practices.
In practice,we’re producing an impressive content volume catering to some of the world’s foremost fashion brands and publishers. The true challenge isn’t merely ramping up content creation but ensuring meticulous validation of each piece. To date, we’re glad to share that we’ve achieved +500 completions per minute. This achievement exemplifies our unwavering commitment to precision and quality in content generation.
There are clients who have approached not one but up to three agencies for content creation using AI before partnering with us at WordLift. This proves that our advanced workflow of content creation from the KG and a dynamic prompt that is built on the basis of the brand’s data and needs, is the cutting-edge solution for companies, giving them peace of mind and security.
Preserving Brand Tone of Voice and Intellectual Property
At WordLift, we are committed to staying at the forefront of content generation by incorporating the latest advances in AI technology. In 2023, Google introduced the helpful content system update, a series of updates that somewhat condemn the indiscriminate use of AI in creating content of little value and impact to people. What Google has repeatedly emphasized as the problem is not the tool used to create content but its quality, such that it is clearly “written for people.”
These updates align perfectly with our commitment to ethical AI, a key goal in developing our innovative content generation system at scale. Our approach goes beyond automation; we employ refined models to preserve your brand’s unique tone of voice (TOV) while safeguarding potential intellectual property (IP) issues. This process significantly elevates the quality and relevance of AI-generated content.
By setting specific validation rules within our generation flow, we can proactively detect and correct instances where the template may inadvertently quote people or brands without the appropriate rights. Moreover, our system integrates advanced fact-checking capabilities, as detailed in our article on AI-powered fact-checking, to ensure the accuracy and credibility of the information presented. This ensures that the content you generate is in line with your brand guidelines and meets legal requirements.
With WordLift’s content generation workflows, you can be confident that your content will consistently resonate with your audience, embodying your brand identity and values. We are committed to pushing the boundaries of ethical AI to provide you with content solutions that are effective and responsible.
Three Steps to Setup a Generation Project
Our user-friendly dashboard provides a seamless experience for setting up a generation project tailored to various use cases. Whether it’s introductory text, product descriptions, or restaurant content, our three-step process simplifies the setup:
Data Source: define the project name, select the knowledge graph you want to use, and select whether you want to use a customized or present template. To extract the data, you will use a GraphQL query.
Customize the Prompt: Set the attributes and parameters that will be used to generate dynamic prompts. This lets you control and align the generated content with your brand’s messaging.
Validate and Refine: Establish content validation rules and review the generated content to ensure it meets your quality standards. Continuously refine the AI system’s rules to improve accuracy and relevance.
Discover how to use our Content Generation to generate high-quality content tailored to your enterprise’s specific needs.
After completing all the steps, you can save the project and initiate the generation process. The generated completions undergo the following processing and categorization:
Valid: This status signifies that the completions have successfully passed the validation process based on the rules you established earlier.
Warning: This status is assigned to generations that have adhered to ‘recommended’ rules but fall short of meeting ‘required’ ones.
Error: This status is assigned when validation errors arise due to missing words or attributes you specified for inclusion. These incomplete completions can be regenerated automatically or rewritten and approved manually.
Accepted: This status applies to all generations you have reviewed and confirmed as satisfactory.
Conclusion
The unification of LLM and KG presents a promising roadmap for content generation. Leveraging both technologies’ strengths, WordLift enables brands to create engaging and informative content at scale. With our user-centric approach and refined templates, we ensure the preservation of brand TOV and compliance with intellectual property regulations while leveraging AI and cutting-edge technologies.
This tool isn’t available to everyone yet, but it’s available to a select group of clients. There are many tools that promise to produce content on a large scale, but there are no others on the market that are able to validate that same content in a way that corresponds to the characteristics of the brand. So if you want to know more, please contact us.
More frequently asked questions
How to do quality assurance when dealing with LLMs in SEO?
Ensuring quality when working with Large Language Models in SEO is a top priority for WordLift. We take a multi-tiered approach to quality assurance. First, our process involves using refined models specifically trained to preserve the brand’s unique tone of voice (TOV). This helps us generate content that is perfectly aligned with brand guidelines. We also implement rules within our generation workflow to detect and correct instances where the template may inadvertently quote people or brands without the appropriate rights, thus protecting against potential intellectual property (IP) infringement. This meticulous approach minimizes the chances of content discrepancies and ensures that generated content maintains high standards of quality and relevance.
How to ensure originality and unique brand voice when dealing with LLMs in SEO?
Maintaining the originality and uniqueness of the brand voice is a crucial goal, achieved through refined templates that are trained on specific datasets (specifically on the Knowledge Graph) tailored to reflect the brand’s style and messaging. This process ensures that the content generated meets brand guidelines and resonates authentically with the target audience.
By establishing rules within our generation flow, we can proactively identify and address potential originality-related issues. This means that the content produced maintains the brand’s distinct voice, providing a consistent and authentic experience for the audience. In addition, our commitment to ethical AI ensures that the content generated is effective and in line with responsible content creation practices. In this way, WordLift provides a reliable solution that maintains the integrity and individuality of your brand.
What is the AI technology WordLift uses for the content generation?
The platform we developed is model-agnostic and we actively experiment with different technologies. We directly work with both Azure and OpenAI team on fine-tuning. We work directly with Hugging Face, Open AI, and Azure and our existing clients are working with fine-tuned modelsthat are specific for their domain.
Is our data private and safe?
Yes, ensuring the privacy and safety of client data is our top priority. We implement a robust data protection strategy that revolves around Azure – one of the most secure cloud platforms available.
As this post is quite extensive and packed with information, we’ve organized it into smaller, more digestible chapters for your convenience. Whether you prefer to start from the beginning and read through to the end or dive straight into the sections that pique your interest the most, feel free to navigate as you please. Enjoy exploring!
In this article, we’ll outline a strategy to optimize your internal link network, enhancing your ability to streamline SEO efforts and direct attention toward crucial money pages on your website by using the WebKnoGraph framework.
Who can Benefit From Debunking the Case for Topic Cluster SEO?
Ideally, you’re familiar with link graphs and versatile in joining data from multiple sources. You understand and experiment with internal linking optimization and are willing to explore computer science and engineering concepts to improve yourself further.
WebKnoGraph Framework: Executive Summary of the SEO Link Graph Engineering Design Solution
The finalized algorithm will gather website data that includes crawlability, linking (using PageRank & CheiRank), and conversions, merging them into a unified metric through a weighted formula. However, it’s crucial to thoroughly review the specifics and nuances discussed in the next sections. During the subsequent phase, we will leverage this weighted formula to compute the significance of nodes in relation to their respective neighborhoods. Ultimately, this data will be employed to adjust our link graph network, optimizing the search experience and topical authority for a particular website. Engineering the graph topology, WebKnoGraph, is the focus of this article.
How Mixing SEO, Computer Science, Engineering and AI Background Help
I’ve been involved with SEO since 2016, having been part of the computer science and engineering community since 2012, which adds up to about a decade of experience. Throughout this time, I’ve engaged both within and outside of WordLift: I’ve worked agency-side on two occasions, spent two stints in-house, and freelanced for over 4 years. My endeavors have spanned various industries, primarily focusing on small and medium size businesses (SMEs) poised for significant growth.
My tenure at WordLift and previous engagements with other stakeholders (albeit to a limited extent) taught me the importance of championing users’ needs and speaking candidly, even when it’s uncomfortable, directly to top-level executives. We also emphasized collaborating across different skill sets to tackle problem-solving, as our clients’ demands often required such a multifaceted approach. Ultimately, we aimed to deliver top-notch work to our clients while ensuring mutual benefit.
I’ve gone through numerous articles discussing the topic cluster SEO, yet what I found is that there’s a lack of a detailed exploration of the scientific and engineering principles underpinning this concept. The engineers at Omio and Kevin Indig, with his TIPR model, came closest to what I aimed to achieve, but there are still nuances and aspects that need further refinement and exploration.
Topic cluster SEO is essential for small and medium size businesses (SMEs) aiming to expand their content strategies via programmatic SEO. It’s crucial to lay a solid and well-thought-out foundation beforehand. The approach I will outline is tailored for SEO-savvy ventures with the resources to develop and refine this methodology internally. This is precisely why I’m open-sourcing my idea: to foster collaborative learning and encourage contributions for an increasingly knowledgeable SEO community.
Why Contextual Internal Linking and Topical SEO Matter?
I have a strong passion for SEO, but what excites me even more is creating a sense of predictability in an industry that’s often unpredictable. I firmly advocate for taking complete control and ownership of our strategies, and one area that holds significant importance is internal linking. By structuring your website logically and making it user-friendly, we empower people to easily discover what they’re seeking and swiftly navigate to your key pages.
The synergy between Topical SEO and internal linking is pivotal in achieving this goal. However, I won’t delve into the general approach and definitions of topical hubs, also known as topic clusters or authority hubs. I believe there are numerous excellent articles out there where my SEO and engineering colleagues have done an outstanding job of covering these topics. Instead, I’d like to share some case studies and valuable links that I personally found to be most enlightening:
There are numerous others, and each one of them is truly fantastic!
Is your SEO Foundation Resilient?
Internal linking is super important for high-growth companies and SEO teams that aim to scale their SEO game. However, bad foundations crack after some time, so the right moment to ask yourself whether you’re ready to scale is now. Should you revise your current implementation? Was the current implementation mindfully developed with SEO in mind?
… bad foundations crack after some time
Solid Foundations are Crucial
When discussing data quality, building effective data pipelines, or constructing link graphs for SEO purposes, the significance of a robust foundation cannot be overstated. Scaling with programmatic SEO is only possible with such a sturdy base. The critical question is: what constitutes a solid foundational link graph structure?
Let’s set this aside momentarily (I’ll address this comprehensively below, I assure you) and instead concentrate on the disparity of the actions of certain companies and those of others.
Innovative Mindsets Change the World for the Better
From my experience, we don’t require a time-travel machine to visualize the past or foresee the future. Companies have reached such a high level of maturity in SEO that they’re at least a decade or more ahead of their counterparts. Conversely, others are lagging, stuck in a state akin to wishing “Happy New Year 2010.”
As a holistic marketing leader, your role isn’t solely about strategizing for present trends. It’s about anticipating the forthcoming two decades and structuring your roadmap to align with these future needs. Your responsibility lies in envisioning what’s next and preparing your strategies accordingly.
Let’s embark on this journey together and delve deeper into every aspect and intricate detail of topic cluster SEO.
Managing Expectations
As I’m prioritizing the engineering aspect of topic cluster SEO, specifically internal linking and link engineering, I’ll proceed under the assumption that the following prerequisites have been met:
This article forms the basis for further discussion and can be adapted through user feedback.
Topic cluster SEO represents an internal linking challenge. To streamline and concentrate on the central issue of internal linking, a data lake or warehouse is available for merging SEO and UX data from various 1st party data sources.
Link issues are fixed beforehand: 4xx + 5xx errors, sync between sitemap and robots.txt on blocking. Also, I presume Javascript links can be detected and crawled appropriately.
Your topic cluster SEO project is already prioritized on your company’s roadmap.
Focus on First-party SEO Data
I’ve reiterated this point several times, yet I can’t stress enough how crucial it is to prioritize the data you own or that Google provides directly to build around it. First-party data is a cornerstone in every SEO strategy, and many SEO practitioners fail to leverage it effectively before falling prey to the allure of the latest SEO software.
How SEO and Business Models Help Each Other for Long-Term Success
A successful, fully implemented, or partially executed solution hinges on understanding what constitutes an effective internal linking network from the start. Kevin Indig, in his fantastic SEO piece, wrote that:
“The best internal linking structure depends on your business model.”
Kevin Indig
One type of site leads all users to one or a few landing pages. The other has users sign up on almost every page. Therefore, the first phase of the problem is to understand what internal linking stands for, recognizing that internal links aid in discovering all pages while also functioning as supplementary ranking signals.
We’ve learned that comprehending the ideal structure of a high-quality internal linking network hinges on the specific business model. To recap Kevin’s perspective, there are two kinds of business models regarding link graphs and networks:
Centralized:This model revolves around key pages that significantly drive profit and user journeys. The entire architecture is centered around these pages because most signup options and calls-to-action (CTAs) are concentrated there.
Decentralized:CTAs and signups are evenly spread across all page templates in this model. Therefore, it becomes crucial to meticulously design the flow of PageRank and CheiRank across the network since conversions are happening everywhere. But are genuinely all pages built equal?
There are even more nuances than this. The key takeaway here is that an SEO expert needs to function strategically, similar to how SEM experts operate today. It involves adjusting the priorities by bridging the gap between the online audience and the specific requirements of the organization. Depending on the situation, the SEO expert can determine whether it’s more beneficial to focus on link depth and indexation or prioritize revenue generation on a bi-weekly or monthly basis. The platform will then handle the remaining tasks by sorting through vast amounts of data to determine the best way to organize the links.
Let’s add some additional important factors that we’ll need to juggle with:
Winning and modern websites follow the “topic over keywords” principle. The message is clear: semantic relatedness is essential!
We also know that frequent crawl logs indicate that a page is important. In other words, periodic crawl logs are correlated to increased page authority.
How do we engineer a good foundation for user-centric journeys, considering topical contextuality & relevance, quality link distribution, and balancing business objectives like conversions?
Let’s remind ourselves of this picture once again:
… bad foundations crack after some time
It’s important to remain positive: applying a computer science and engineering approach to a new field, such as SEO, can provide us with a potential solution. The learning is to keep your SEO-house clean:
… but a solid SEO can be built on a resilient foundation
Now, let’s go back to the original problem:how to engineer a good foundation, assuming we have the first-party data? My mum always used to say that “a problem well-stated is a problem half-solved” (attributed to Charles Kettering, who was head of research at General Motors).
It’s all about asking the right questions. Therefore, I will ask a new question: what is a website, essentially and technically speaking?
A Website is a Directed Cyclic Graph and a Subgraph of Word Wide Web (WWW)
The first intuition is to realize that a website is a Directed Cyclic Graph!
Let’s explain this further:
Websites represent directed graphs because links have a specific direction from the source page to the destination page.
Websites represent cyclic graphs since there are loops or cycles in the graph. For example, the second-level nodes (links) from the picture above link to each other (also the leaves back to the homepage).
Website graphs are weighted graphs since links have different weights or costs associated with them. For example, external links can be seen as more valuable than internal links (this is just an assumption; I’ll focus on what we can control in this article, for instance internal links).
Here’s a website graph visualization (sampled data) from the WordLift’s website:
Each page is a node on the graph representation. There are multiple super-nodes that link to many important pages and are not properly organized. This is a good optimization opportunity. A super-node is either a topic cluster or a super-page that has links from important pages and links to multiple important pages, based on a weight formula.
Supernodes are visualized in the following picture:
Content writing and content differentiation can be challenging too: if the writing is not distinct enough, many pages will be bucketed into the same topical cluster.
I am not the first one to speak about this. I expect Mike King from iPullRank to cover these topics in his upcoming book “The Science of SEO: Decoding Search Engine Algorithms“. Kevin Indig spoke about this to some extent in his TIPR model articles and Omio engineering covered graph theory approaches for applied SEO on their engineering blog.
Applied SEO: Algorithm System Design for Optimal Internal Linking
Our initial challenge is to establish the importance of each node, which is a blend of the node’s inherent value and its connections (links) to other significant nodes within the graph. We must create a weighted formula that allows us to represent the business value of each node!
I hold Kevin Indig in high regard, and this piece wouldn’t have come together without the ideas he previously shared in his blog articles. I’m fulfilling my duty and possibly pursuing Kevin’s desire to expand further, analyze, and enhance his work. It’s a privilege to undertake this endeavor, so let’s dive in!
The True Internal PR (TIPR) model is an excellent initial framework. However, the challenge lies in the lack of a link to the business aspect, specifically conversions. Instead of focusing solely on developing the logic for backlinks, I propose integrating conversions into the formula. This approach provides a more comprehensive understanding, offering contextual relevance and helping prevent negative user experiences. Conversions, apart from being a sales/business metric, also function as a UX metric – a purchase often signifies satisfaction with the service. Admittedly, it’s not as straightforward as it sounds, but let’s summarize it that way for simplicity’s sake.
In addition to exploring the TIPR model, I came across the work of Murat Yatagan, who employed a comparable approach, albeit not structured as a definitive formula or model for direct emulation. Take a moment to examine the screenshot below:
Take note of the triangulated crawl, log files, and user visit data. It bears a resemblance to what Kevin intended to accomplish but presents some distinctions. However, is user visitation a reliable indicator for SEO success?
PageRank and CheiRank Explained
PageRank and CheiRank represent two distinct link analysis algorithms utilized for gauging a webpage’s significance. These algorithms find application in search engine operations to determine website rankings in search results. Additionally, website owners employ them to fine-tune their sites for improved search engine optimization (SEO).
PageRank
PageRank, conceived by Google’s founders Larry Page and Sergey Brin, is a link analysis algorithm rooted in the concept that a page holds importance if it garners links from other significant pages. Calculating a PageRank score involves evaluating both the quantity and quality of links directed towards a page. Links originating from high-quality pages carry more weight compared to those from lower-quality pages. The sum of all PageRank values is one.
CheiRank
CheiRank, created by Hyun-Jeong Kim and a team of collaborators, is a link analysis algorithm that determines the relative importance of URLs by assessing the weight of outgoing links in the link graph. A high CheiRank serves as a reliable indicator of an essential hub-page. Notably, the sum total of all CheiRank values equals one.
Modification of the TIPR Model for SEO
I’d modify the approach for PageRank and CheiRank to create UniversalPageRank (identifying all incoming links regardless of their origin—whether from the same or different websites) and UniversalCheiRank (identifying all outgoing links regardless of their destination—whether to the same or different websites). I’ll also switch from user visits to conversions.
How to Define Conversion for Different Industries
The definition of conversion differs across industries, shaped by their unique business objectives. In e-commerce, it typically refers to finalized purchases or different stages within the sales process. In the realm of SaaS, it could involve actions such as registering for trials or subscribing to services. Content publishers often prioritize activities like gaining subscribers to their newsletters, while lead generation sectors assess metrics like form submissions or webinar registrations. For the travel sector, conversions are typically bookings, whereas nonprofits focus on donations or recruiting volunteers. Customizing conversion definitions to match industry aims requires a comprehensive grasp of the business and the tracking of key performance indicators (KPIs) to establish pertinent goals.
An interesting article that follows a directed graph and probabilistic model approach and utilization of Markov Chains applied to conversions was generously provided to me by Max Woelfle. However, I think it’s crucial to understand that there’s no universal solution for everyone. You’ll have to tailor your conversion metrics according to your specific industry and business model.
Visualizing the Modified TIPR Model for SEO
Explore the link model provided below:
The second intuition, after figuring out that this is a directed cyclic graph, is to develop a weighted function that will take the following parameters:
1. Universal PageRank
2. Universal CheiRank
3. Conversions
4. Crawl logs data
Please be aware that our current discussion only delves into the link graph aspect, and we haven’t addressed the semantic aspect yet.
Also, feel free to choose different metrics that are specifically relevant for your industry. For example, indexability and crawl efficacy are very important for news platforms and metrics that I would play with in my weighted formula.
It’s important to note that the metrics you choose should “meet certain characteristics. They must be measurable in the short term (experiment duration), computable, and sufficiently sensitive and timely to be useful for experimentation. Suppose you use multiple metrics to measure success for an experiment. Ideally, you may want to combine them into an Overall Evaluation Criterion (OEC), which is believed to impact long-term objectives causally. It often requires multiple iterations to adjust and refine the OEC, but as the quotation above, by Eliyahu Goldratt, highlights, it provides a clear alignment mechanism to the organization.”
I took this definition from Ron Kohavi’s book on “Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing”. The section “Metrics for Experimentation and the Overall Evaluation Criterion” should be particularly interesting for SEOs because it demonstrates a scientific approach to choosing your metrics wisely. Short enough that you can test them and impactful enough that they will drive long-term business growth. Once you develop your weighted formula, assign weights to each metric and tweak them accordingly based on testing and observations. Here’s an example weight function below:
It’s important to note that the total sum of the weights should be equal to one.
You need to apply this weight formula to each node to determine its value:
A Debate and An Open Question on Picking Independent Variables in the Weight Formula
In addition to the previously mentioned criteria, it’s advisable to opt for independent variables that aren’t interdependent or mutually influential to avoid bias in logical modeling and prevent arriving at inaccurate conclusions. When variables are correlated, they can significantly impact the outcome. For instance, consider the relationship between crawlability (or, as Kevin highlighted, server log files) and conversions. Given the lack of transparency in Google’s algorithms, some assumptions need to be made based on informed judgment:
Google indexes a page because it perceives value in its content. Conversions could influence this perceived value since the page effectively fulfills users’ commercial intent.
Simultaneously, we consider conversions to emphasize the page’s importance for the same commercial intent. However, the initial measurement might result from the latter, potentially deviating from the intended objective.
Think about picking valuable, independent variables for a second and take this with you as a home assignment.
The third intuition is to identify super nodes in the graph.
The fourth intuition is to use the supernoder-alike solution to identify super-nodes and reorganize them: you can “break them” or link from them to reorganize the graph neighborhood.
“Relationships originating from high-scoring nodes contribute more to the
score of a node than connections from low-scoring nodes. A high eigenvector
score means a node is connected to many high-score nodes”. Super-nodes are connected to many vital nodes based on the defined weight formula.
There are different centrality measures available besides Eigenvector Centrality that might be more suitable for your needs. In my case, Eigenvector Centrality works best because it emphasizes the influence of the nodes or links. However, there are several other options to consider:
Degree centrality: This depends on the number of connections coming in (In-Degree) and going out (Out-Degree) from a node. In-degree refers to the number of incoming connections, while out-degree refers to the number of outgoing connections to other nodes.
Closeness centrality: This measures a node’s importance by how close it is to all other nodes in a graph.
Betweenness centrality: This assesses a node’s significance based on how often it falls along the shortest paths between pairs of nodes in a graph. For instance, in the Omio’s Graph Theory SEO case, this measure might be crucial.
Should I Perform Automatic, Semi-automatic or Manual Graph Link Restructuring?
After you’ve finished crunching the numbers and identified those super-nodes, I highly suggest employing hierarchical clustering or automatic semantic linking to restructure the graph. Wondering why?
While I understand that automating internal linking can pose challenges in specific situations, it’s an undeniable truth that well-crafted topical maps offer flexibility, all while efficiently managing automated data pipelines and DevOps workflows behind the scenes.
However, utilizing custom embeddings can be even more effective in capturing the unique specifics of the content. Is relying solely on a link-graph-engineered approach truly the way to go, or should we consider alternatives to avoid a one-size-fits-all solution?
It’s worth noting the following: having worked extensively as a technical writer and SEO analyst for several years, I’ve recognized a critical content creation and differentiation issue. As content creators, we sometimes need to allocate more time to craft unique content that stands apart from our previous works. As a result, many seemingly unrelated articles can end up in the same topic cluster. This can make hierarchical clustering a questionable solution. However, I still find using semantic similarity as a definitive solution to this problem though. If you’re only interested in semantic contextuality and looking for a quick, practical solution, try Lee Foot’s BERTlinker tool. It’s a great tool I’ve used on several occasions (thank you, Lee, for everything you do for our SEO community). It was featured as an App of the Month by Streamlit. What an achievement!
How Should I Optimize my Link Graph After Identifying Super-nodes for SEO?
To avoid risking quick-link-graph-change penalties, you can choose an intelligent approach to connect the pages between themselves. This approach allows SEOs to focus on their work and be mindful about strategy, instead of choosing which sections of pages to link to manually. In theory, we could take a semi-automated route by inserting automated HTML sitemaps as suggested links in the HTML sitemap section and then giving them a manual once-over. However, the downside of this approach is that it pulls your attention away from the crucial aspects. You end up knee-deep in technical tasks and manual labor, which, let’s face it, can eat up all your time.
Finally, based on my hands-on experience, I’ve found that a completely automated, intelligent solution that empowers SEO professionals to fine-tune the overall parameters and devise strategies is my preferred choice.
The Interplay Between a Link Graph and a Knowledge Graph Optimization
On one side, we’ve got the link graph, and on the flip side, there’s the knowledge graph. What defines our work is the interaction between these two elements. Once we’ve assessed the parameters and assigned weights, we turn to semantic recommendations to ensure coherence in our content. Without an ontology (or at least a robust taxonomy), there’s a risk of creating isolated sections and missing out on a more naturally engaging experience.
Let’s say you lack a structured framework – you might unintentionally segregate content. We can, for instance, connect different clusters of pages with links simply because they cater to the same audience, regardless of where they stand in the link graph. This highlights why semantics play a crucial role.
While many in large publications or e-commerce focus solely on the linked graph, our attention is on both links and semantics. Semantic understanding allows us, especially in industries like fashion, to view content in terms of outfits, following a sequence (often from top to bottom and then accessorizing). This philosophy lies at the heart of our approach: the specifics are site-dependent and related to the overall taxonomy, but the essence remains consistent.
What Challenges Are Presented by This Approach and Algorithmic Framework?
That’s a fantastic question. Firstly, I want to express my gratitude to you for reading the article and everyone who took part in our collaborative brainstorming session on Twitter and contributed to the discussion either in the thread or via DMs.
I’d like to extend my gratitude to Petar Tonkovic, ex-CERN, and Ariola Lami from the Technical University of Munich for their invaluable reviews of the initial drafts.
There are instances where you might encounter a high volume of bad-quality pages containing numerous links. This situation can result in an excessive consumption of crawl budget and a mismatch with user intentions, ultimately failing to drive conversions. Consequently, these pages may inaccurately inflate their importance in Google’s assessment.
It’s essential to address these issues promptly. Using a quality crawler, like Botify, can be immensely helpful in segmenting data at a folder or cluster level, allowing you to identify areas where you can optimize your SEO efforts. I highly recommend checking out Oncrawl’s excellent case study on this topic for further insights.
How Does WordLift Solve Link Graph Restructuring and Internal Linking Architectural Problems?
We deploy human-focused, yet business-backed, personalized approaches for our clients which helped us develop our innovative dynamic internal links solution (thus the name WebKnoGraph framework). In most cases, we’re helping serious SEO e-commerce players and SEO-ready companies who are ready to embrace the power of ethical generative AI with a human-in-the-loop (HITL) approach.
Ready to supercharge your SEO strategy with interconnected, marketable content? Connect with our team today to explore how the WebKnoGraph framework can elevate your online presence!
We’d like to extend our gratitude to the editor and reviewers for their insightful comments. Their feedback has been instrumental in enhancing the quality of our research significantly. Cheers to a more knowledgeable SEO community!
SEO automation is the process of using software to optimize a website’s performance programmatically. This article focuses on what you can do with the help of artificial intelligence to improve the SEO of your website.
Let’s first remove the elephant in the room: SEO is not a solved problem (yet), and while we, as toolmakers, struggle to alleviate the work of web editors on one side while facilitating the job of search engines on the other, SEO automation is still a continually evolving field, and yes, a consistent amount of tasks can be fully automated, but no, the entire SEO workflow is still way too complicated to be entirely automated. There is more to this: Google is a giant AI, and adding AI to our workflow can help us interact at a deeper level with Google’s giant brain. We see this a lot with structured data; the more we publish structured information about our content, the more Google can improve its results and connect with our audience.
In the realm of SEO automation, a pivotal role is played by AI SEO Agents. These intelligent systems are designed to streamline and enhance various SEO tasks through automation. An AI SEO Agent can significantly reduce the manual effort involved in SEO by taking over repetitive and time-consuming tasks. For instance, it can conduct comprehensive keyword research, identifying not just high-volume keywords but also long-tail phrases that might be overlooked. This ensures that your content strategy is both broad and nuanced, capturing a wider audience.
Moreover, an AI SEO Agent excels in content optimization. It can analyze existing content to suggest improvements, such as where to naturally incorporate keywords, how to structure articles for better readability, and even recommend topics that are currently trending or missing from your content strategy. This ensures that your website remains relevant and highly engaging for your target audience.
Another critical area where AI SEO Agents make a significant impact is link building. By analyzing vast amounts of data, these agents can identify potential high-quality backlink opportunities. This not only enhances your site’s authority but also drives targeted traffic, contributing to both your SEO and overall marketing goals.
By integrating an AI SEO Agent into your SEO workflow, you’re not just automating tasks; you’re also aligning your strategies more closely with the sophisticated algorithms of search engines like Google. This synergy between AI-driven SEO efforts and Google’s AI can lead to more effective and efficient optimization, ultimately improving your website’s visibility and engagement.
When it comes to search engine optimization, we are typically overwhelmed by the amount of manual work that we need to do to ensure that our website ranks well in search engines. So, let’s have a closer look at the workflow to see where SEO automation can be a good fit.
Technical SEO: Analysis of the website’s technical factors that impact its rankings, focusing on website speed, UX (Web Vitals), mobile response, and structured data.
Automation: Here, automation kicks in well already with the various SEO suites like MOZ, SEMRUSH, and WooRank, website crawling software like ScreamingFrog, Sitebulb, etc., and a growing community of SEO professionals (myself included) using Python and JavaScript that are continually sharing their insights and code. If you are on the geeky side and use Python, my favorite library is advertools by @eliasdabbas ? .
On-Page SEO: Title Tag, Meta Descriptions, and Headings.
Automation: Here is where AI/deep learning brings value. We can train language models specifically designed for any content optimization task (i.e., creating meta descriptions or, as shown here by @hamletBatista, title tag optimization). We can also use natural language processing (like we do with WordLift) to improve our pages’ structured data markup ?.
Off-page SEO: Here, the typical task would be creating and improving backlinks.
Automation: Ahrefs backlink checker is probably among the top solutions available for this task. Alternatively, you can write your Python or Javascript script to help you claim old links using the Wayback machine (here is the Python Package that you want to use).
Automation: Here we can create and train a custom Knowledge Graph that makes your on-site search smarter. So, when a user enters a query, the results will be more consistent and respond to the user’s search needs. Also, through Knowledge Graph, you will be able to build landing page-like results pages that include FAQs and related content. In this way, the user will have relevant content, and their search experience will be more satisfying. By answering users’ top search queries and including information relevant to your audience, these pages can be indexed on Google, also increasing organic traffic to your website.
SEO strategy: Traffic pattern analysis, A/B testing, and future predictions.
Automation: here also we can use machine learning for time series forecasting. A good starting point is this blog post by @JR Oaks. We can use machine learning models to predict future trends and highlight the topics for which a website is most likely to succeed. Here we would typically see a good fit with Facebook’s library Prophet or Google’s Causal Impact analysis.
Will Artificial Intelligence Solve SEO?
AI effectively can help us across the entire SEO optimization workflow. Some areas are, though, based on my personal experience, more rewarding than others. Still, again – there is no one size fits all and, depending on the characteristics of your website, the success recipe might be different. Here is what I see most rewarding across various verticals.
Automating Structured Data Markup
Structured data is one of these areas in SEO where automation realistically delivers a scalable and measurable impact on your website’s traffic. Google is also focusing more and more on structured data to drive new features on its result pages. Thanks to this, it is getting simpler to drive additional organic traffic and calculate the investment return.
Here is how we can calculate the ROI of structured dataHere is a concrete example of a website where, by improving the quality of structured data markup (on scale, meaning by updating thousands of blog posts), we could trigger Google’s Top stories, to create a newflow of traffic for a news publisher.
Finding new untapped content ideas with the help of AI
There are 3.5 billion searches done every day on Google, and finding the right opportunity is a daunting task that can be alleviated with natural language processing and automation. You can read Hamlet Batista’s blog post on how to classify search intents using Deep Learning or try out Streamsuggest by @DataChaz to get an idea.
Here at WordLift, we have developed our tool for intent discovery that helps our clients gather ideas using Google’s suggestions. The tool ranks queries by combining search volume, keyword competitiveness, and if you are already using WordLift, your knowledge graph. This comes in handy as it helps you understand if you are already covering that specific topic with your existing content or not. Having existing content on a given topic might help you create a more engaging experience for your readers.
Here is where I expect to see the broadest adoption of AI by marketers and content writers worldwide. With a rapidly growing community of enthusiasts, it is evident that AI will be a vital part of content generation. New tools are coming up to make life easier for content writers, and here are a few examples to help you understand how AI can improve your publishing workflow.
Creating SEO-Driven Article Outlines
We can train autoregressive language models such as GPT-3 that use deep learning to produce human-like text. Creating a full article is possible, but the results might not be what you would expect. Here is an excellent overview by Ben Dickson that demystifies AI in the context of content writing and helps us understand its limitations.
Two points: (1) it isn’t very good, and (2) that’s *after* editing by professionals. GPT3 is genuinely impressive but not general AI, and any meaning associated with it is *attributed* to it by *us*. https://t.co/AfNG1HpZ8q
There is still so much that we can do to help writing be more playful and cost-effective. One of the areas where we’re currently experimenting is content outlining. Writing useful outlines helps us structure our thoughts, dictates our articles’ flow, and is crucial in SEO (a good structure will help readers and search engines understand what you are trying to say). Here is an example of what you can actually do in this area.
I provide a topic such as “SEO automation” and I get the following outline proposals:
What is automation in SEO?
How it is used?
How it is different from other commonly used SEO techniques?
You still have to write the best content piece on the Internet to rank, but using a similar approach can help you structure ideas faster.
entice the user to click through to your page from the search results.
It’s a magical skill that marketers acquire with time and experience. And yes, this is the right task for SEO automation as we can infuse the machine with learning samples by looking at the best titles on our website. Here is one more example of what you can do with this app. Let’s try it out. Here I am adding two topics: SEO automation and AI (quite obviously).
The result is valuable, and most importantly, the model is stochastic, so if we try the same combination of topics multiple times each time, the model generates a new title.
Improving an existing title by providing a target keyword
You can optimize the titles of your existing content by providing a target keyword in order to rank on Google and search engines based on it. For example, suppose I take “SEO automation” as my target keyword for this article and I want to optimize my current content title. Here is the result.
Generating meta descriptions that work
Also, we can unleash deep learning and craft the right snippet for our pages or at least provide the editor with a first draft to start with for meta description. Here is an example of an abstractive summary for this blog post.
Creating FAQ content on scale
The creation of FAQ content can be partially automated by analyzing popular questions from Google and Bing and providing a first draft response using deep learning techniques. Here is the answer that I can generate for “Is SEO important in 2021?”
Data To Text in German
By entering a list of attributes, you can generate content in German. For example, in this case, I’m talking about The Technical University of Berlin, and I’ve included a number of attributes that relate to it and this is the result.
DISCLAIMER: Access to the model has been recently opened to anyone via an easy-to-use API and now any SEO can find new creative ways to apply AI to a large number of useful content marketing tasks. Remember to grab your key from OpenAI.
Images that accompany the content, whether news articles, recipes, or products, are a strategic element in SEO that is often overlooked.
In multiple formats (1:1, 4:3 and 16:9), large images are needed by Google to present content in carousels, tabs (rich results across multiple devices) and Google Discover. This is done using structured data and following some essential recommendations:
Make sure you have at least one image for each piece of content.
Make sure the images can be crawled and indexed by Google (sounds obvious but it’s not).
Ensure the images represent the tagged content (you don’t want to submit a picture of roast pork for a vegetarian recipe ?).
Use a supported file format (here’s a list of Google Images supported file formats).
Provide multiple high-resolution images that have a minimum amount of pixels in total (when multiplying one size with the other) of:
50,000 pixels for Products and Recipes
80,000 pixels for News Articles
Add the same image in the structured data in the following proportions: 16×9, 4×3, and 1×1.
AI-powered Image Upscaler
With Super-Resolution for Images, you can enlarge in and enhance images from your website using a state-of-the-art deep learning model.
WordLift automatically creates the required version of each image in the structure data markup in the proportions 16×9, 4×3 and 1×1. The only requirement is that the image is on the smaller side by at least 1,200 pixels.
Since this isn’t always possible, I came up with a workflow and show you how it works here.
The code is fairly straightforward so I will explain how to use it and a few important details. You can simply run the steps 1 and 2 and start processing your images.
Prior to doing that you might want to choose if to compress the produced images and what level of compression to apply. Since we’re working with PNG and JPG formats we will use the optimize=True argument of PIL to decrease the weight of images after their upscaling. This option is configurable as you might have already in place on your website an extension, a CDN or a plugin that automatically compresses any uploaded image.
You can choose to disable (the default is set to True) the compression or change the compression level using the form inside the first code block of the Colab (1. Preparing the Environment).
2. Loading the files
You can upload the files that you would like to optimize by either:
A folder on your local computer
A list of comma separated image URLs In both cases you are able to load multiple files and the procedure will keep the original file name so that you can simply push them back on the web server via SFTP.
When providing a list of URLs the script will first download all the images in a folder, previously created and called input.
Once all images have been downloaded you can run the run_super_res() function on the following block. The function will first download the model from TF Hub and then will start increasing the resolution of all the images x4. The resulting images will be stored (and compressed if the option for compression has been kept to True) in a folder called output.
Once completed you can zip all the produced files contained in the output folder by executing the following code block. You can also change the name of the zipped file and eventually remove the output folder (in case you want to run it again).
To discover the results achieved in our first test of this AI-powered Image Upscaler, we recommend reading our article on how to use the Super-Resolution technique to enlarge and enhance images from your website to improve structured data markup by using AI and machine learning.
Automating product description creation
AI writing technology has made great strides, especially in recent years, dramatically reducing the time it takes to create good content. But human supervision, refinements, and validations remain crucial to delivering relevant content. The human in the loop is necessary and corresponds to Google’s position on machine learning-generated content, as mentioned by Gary Illyes and reported in our web story on machine-generated content in SEO.
Right now our stance on machine-generated content is that if it’s without human supervision, then we don’t want it in search. If someone reviews it before putting it up for the public then it’s fine.
GPT-3 for e-commerce
GPT-3 stands for Generative Pre-trained Transformer. It is an auto-regressive language model that uses deep learning to produce human-like text. OpenAI, an AI research and deployment company, unveiled this technology with 175 billion language parameters. It is the third-generation language prediction model in the GPT-n series and the successor to GPT-2, created by Microsoft-funded OpenAI.
You can use GPT-3 for many uses, including creating product descriptions for your e-commerce store.
How to create product description with GPT-3
GPT-3 can predict which words are most likely to be used next, given an initial suggestion. This allows GPT-3 to produce good sentences and write human-like paragraphs. However, this is not an out-of-the-box solution to perfectly drafting product descriptions for an online store.
When it comes to customizing GPT-3’s output, fine-tuning is the way to go. There is no need to train it from scratch. Fine-tuning allows you to customize GPT-3 to fit specific needs. You can read more about customizing GPT-3 (fine-tuning) and learn more about how customization improves accuracy over immediate design (learning a few strokes).
Fine-tuning GPT-3 means providing relevant examples to the pre-trained model. These examples are the ideal descriptions that simultaneously describe the product, characterize the brand, and set the desired tone of voice. Only then could companies begin to see real value when using AI-powered applications to generate product descriptions.
Here is how you can proceed when approaching SEO automation. It is always about finding the right data, identifying the strategy, and running A/B tests to prove your hypothesis before going live on thousands of web pages.
It is also essential to distinguish between:
Deterministic output – where I know what to expect and
Stochastic output – where the machine might generate a different variation every time, and we will need to keep a final human validation step.
I believe that the future of SEO automation and the contribution of machine/deep learning to digital marketing is now. SEOs have been automating their tasks for a while now, but SEO automation tools using AI are just starting to take off and significantly improve traffic and revenues.
Are you Interested in trying out WordLift Content Intelligence solutions to scale up your content production? Book a meetingwith one of our experts or drop us a line.
The image we used in this blog post is a series of fantastical objects generated by OpenAI’s DALL·E new model by combining two unrelated ideas (clock and mango).
Leverage Advanced Tools for Enhanced Content Creation
While exploring the vast landscape of SEO automation, it’s crucial to recognize the pivotal role of content in driving SEO success. High-quality, engaging content remains at the heart of SEO, attracting organic traffic and improving search rankings. To this end, embracing advanced content creation tools can significantly amplify your SEO strategy.
Our content creation tool offers a sophisticated solution, harnessing the power of AI to generate compelling content that resonates with your audience. By integrating this workflow into your SEO automation process, you can streamline content production, ensuring consistency and relevance across your digital presence.
Discover how our content generation can revolutionize your content strategy and help you achieve your business goals. Explore the possibilities and start creating captivating content that captivates your audience and drives results. Contact us!
The Future of SEO Automation
Based on the latest trends in SEO automation for 2024, as highlighted in our SEO Trends for 2024 article, it’s clear that the integration of artificial intelligence (AI) and the use of structured data are becoming increasingly important. The ability to leverage AI for in-context learning, instructions-based prompts, and other emerging behaviors of foundational models is crucial. This is where the concept of an AI SEO Agent becomes particularly relevant.
An AI SEO Agent represents a significant evolution in the field of SEO automation. It acts as a specialized AI assistant that can automate and optimize a wide range of SEO tasks with greater efficiency and accuracy. From conducting sophisticated keyword research and content optimization to executing strategic link-building efforts, the AI SEO Agent leverages advanced algorithms and machine learning techniques to enhance SEO strategies. This automation not only saves time but also ensures that SEO practices are aligned with the latest search engine algorithms and user search behaviors.
This is anchored to an extensible data fabric and brand authority, which allows for more sophisticated and effective SEO strategies. Additionally, the introduction of product knowledge graph panels by search engines necessitates that merchants continuously adapt their strategies to keep pace with these evolving trends.
A prime example of the importance of staying ahead in SEO through innovative strategies is seen in our case study with EssilorLuxottica. This collaboration showcases how the use of generative AI, structured data markups, and the strategic implementation of an AI SEO Agent can revolutionize SEO strategies, driving unprecedented innovation and significantly enhancing online visibility and revenue at scale.
More Questions On SEO Automation
What is SEO automation?
You can automate SEO tasks by using AI-powered tools and software that help in various aspects of SEO, including content creation, keyword research, link building, and performance analysis. These tools can significantly reduce the manual workload by providing insights, suggestions, and automating repetitive tasks.
Are there risks associated with SEO automation?
While SEO automation can streamline many tasks, it’s important to be aware of the risks. Over-reliance on automation without human oversight can lead to issues such as non-compliance with SEO best practices, creation of irrelevant content, or even penalties from search engines if the automation is perceived as manipulative. It’s crucial to balance automation with manual review and intervention.
Which SEO tasks can be automated, and which ones require manual intervention?
Tasks like data analysis, keyword research, and certain aspects of content optimization can be effectively automated. However, tasks requiring creative input, strategic decision-making, and nuanced understanding of content quality and relevance still require manual intervention. It’s essential to identify which tasks can be automated to save time without compromising the quality of your SEO strategy.
What are the best SEO automation tools?
The best SEO automation tools are those that offer a comprehensive suite of features to address various aspects of SEO, from keyword research and content optimization to link building and performance tracking. Tools that leverage AI and machine learning to provide actionable insights and automate repetitive tasks are particularly valuable.
Strategic integration of AI and e-commerce SEO is the key to success for companies aiming to drive change and innovation. Under the leadership of its SEO and editorial teams and working closely with WordLift’s team of experts, EssilorLuxottica has achieved remarkable progress in various aspects of its operations.
By examining its journey, your organization can discover valuable insights and opportunities to move forward. This strategic implementation goes beyond a single e-commerce platform, encompassing multiple channels. With a professional and results-oriented approach, your company can mirror EssilorLuxottica’s success, leveraging the power of AI and SEO to establish itself as a pioneer in your industry.
EssilorLuxottica commands a global presence as a preeminent force in creating, manufacturing and disseminating ophthalmic lenses, frames, and sunglasses. With operations across 150 countries, the company is an open network enterprise, extending access to high-quality vision care products for industry stakeholders. Its esteemed portfolio features eyecare and eyewear brands, including Ray-Ban, Oakley, and Persol. Complementing these offerings are advanced lens technologies such as Transitions, Varilux, and Crizal, alongside essential prescription laboratory equipment and pioneering solutions, all designed to address the diverse needs of customers and consumers worldwide.
The Main Goal
In our collaboration, we have embarked with the EssilorLuxottica team on a journey of commitment to industry innovation and progress.
The goals set included:
Infusing innovation into workflows.
Aligning with the latest industry trends.
Providing ongoing professional support to the Group’s in-house SEO team.
Special attention was paid to the safe integration of generative AI into critical SEO activities, such as structured data implementation, knowledge graph creation, and refinement of product descriptions. All of these initiatives were pursued to foster continuous innovation, drive scalable revenue growth, and optimize workflows for greater efficiency. This collaborative effort underscores the profound impact that the strategic integration of AI and SEO can have on business operations in the industry.
Challenges
In the collaborative journey of WordLift and EssilorLuxottica, the pursuit of enhancing SEO and refining content strategies has been a dynamic and rewarding process. Despite the successes, the path to optimization had its share of challenges. These challenges, though formidable, served as crucibles for innovation and adaptation, ultimately strengthening the collaborative bond between the two teams.
One initial challenge encountered was managing and processing a large volume of data, an essential aspect of improving SEO. The teams met this challenge by implementing efficient data management strategies, ensuring the use of all the information to its full potential. As campaigns developed and stock availability fluctuated, teams needed agile adjustments to accommodate the dynamic nature of specific campaign periods and stock availability.
The continuous evolution of search engine algorithms posed another challenge. Staying ahead of these changes required constant vigilance and adapting quickly. Teams demonstrated responsiveness to algorithmic updates, ensuring their SEO strategies aligned with the latest search engine trends. At the same time, changes in AI models and technologies emerged as crucial considerations, requiring a proactive approach to adaptation.
In the context of a large SEO team, the complexities of development and implementation further increased the challenges. Coordinating efforts and maintaining cohesion in such an environment require strategic planning and effective communication. The teams faced this challenge head-on, ensuring the collective experience was leveraged to its full potential.
In the face of these challenges, the collaborative effort between WordLift and EssilorLuxottica demonstrated resilience and highlighted the teams’ ability to turn obstacles into opportunities. This harmonious collaboration enabled the teams to overcome challenges and paved the way for creating a robust and adaptable SEO strategy. The resulting online presence and visibility improvement set the stage for a deeper exploration of the solutions implemented during this transformation journey.
Solutions & Results
Responding to the client’s request and considering the challenges explained above, we built a strategy of various solutions that addressed specific needs. The individual solutions adopted are part of an SEO strategy aimed at the innovation and growth of the brand and business.
Create an Eyewear Ontology
Ontology in SEO refers to the structured representation of knowledge that aids search engines in understanding the context and meaning of search queries. This enables them to deliver more accurate and relevant search results. By incorporating an ontology into your SEO strategy, you enhance the visibility of your content. This means search engines can better comprehend and connect it with user queries, resulting in higher rankings and increased traffic to your website.
In the journey with WordLift, EssilorLuxottica achieved a significant milestone by introducing the inaugural release of an Eyewear Ontology. This meticulously designed framework serves as a comprehensive map, capturing eyewear products’ intricate nuances. Through the implementation of this ontology, EssilorLuxottica now possesses an exceptional capability to ingest, describe seamlessly, and query product attributes directly from their Product Information Management (PIM) system. Furthermore, the ontology empowers them to automate the generation of product descriptions with precision and efficiency.
By constructing prompts tailored to the specific demands of the task at hand, EssilorLuxottica has harnessed the potential to produce content that not only meets but exceeds expectations. This, coupled with the ability to validate generated content, ensures accuracy and relevance that distinguishes EssilorLuxottica in the digital landscape. The Eyewear Ontology is a testament to their commitment to innovation and an unwavering pursuit of excellence in every facet of their partnership with WordLift.
Build the Product Knowledge Graph
TheProduct Knowledge Graph (PKG) is an e-commerce-specific form of knowledge graph designed to improve product discoverability and end-user experience by enriching a brand’s content with data.
Leveraging WordLift’s expertise in semantic search technologies and content optimization, EssilorLuxottica experienced a revolutionary transformation in how their product information was structured and presented online. A pivotal breakthrough came with implementing an advanced workflow integrating data from Google Merchant Center and the SFTP Feed. This innovative approach brought numerous advantages, most notably the rapid acceleration of data import into the knowledge graph.
EssilorLuxottica can import and validate about 1000 products per minute, catapulting efficiency to unprecedented levels. Furthermore, this workflow expedites the resolution of data inconsistencies between Structured Data and Merchant feeds, all while introducing automatic schema validation, ensuring a seamless and error-free user experience. This groundbreaking enhancement streamlines processes and significantly amplifies the brand’s visibility in the digital realm.
Through the implementation of a Product Knowledge Graph (PKG) workflow, we witnessed a significant boost in organic traffic across their various brands. Over the last three months, there was an impressive 16% increase in clicks per website compared to the previous year’s period. This demonstrates the tangible impact of our innovative approach to enhancing online visibility and engagement for EssilorLuxottica.
Experiencing The Power Of Dynamic Internal Links
Dynamic internal links are crucial in enhancing the SEO performance of product listing web pages (PLP). By automating the process of recommending links to users, we facilitate their navigation through similar category pages, ultimately reducing click-depth and improving user experience. Additionally, these internal links provide invaluable signals to search engines, aiding them in comprehending the organizational structure of categories. This, in turn, contributes to higher rankings in search results.
Furthermore, the strategic use of anchor text optimization can further bolster the visibility of specific queries. From a business perspective, recommending categories empowers us to prioritize sales campaigns, promote key product categories, and appropriately manage those with lower business impact, such as out-of-stock items.
Implementing dynamic internal links has proven to be a strategic move for EssilorLuxottica in enhancing user engagement and optimizing content focus. The initial experiment on a retailer website in the US yielded promising results, with a noticeable increase in clicks. Furthermore, the data analysis revealed a reduction in the number of queries per page on the variant, indicating a higher level of content precision and relevance. This not only enhances the user experience but also strengthens the overall SEO strategy.
30% clicks (April-September YoY)
20% increase in average position
Encouraged by these positive outcomes, we are currently rolling out the same dynamic internal link experiment on other websites to replicate and build upon these advantageous results. This initiative aligns with EssilorLuxottica’s commitment to employing innovative solutions to continually refine and elevate the digital experience for their customers.
Generate FAQs by Using AI
We developed an innovative FAQ workflow designed with precision and efficiency in mind. It encompasses improved sourcing methods to prevent query cannibalization, refined content summarization through the utilization of fine-tuned models, and the unique ability to instruct the model using existing page content. Additionally, our process allows for seamless extraction of pertinent questions directly from the content, ensuring a comprehensive and accurate FAQ section. This tailored approach sets the stage for an efficient and user-friendly experience on EssilorLuxottica’s platform.
Through the implementation of AI-powered technology, EssilorLuxottica has achieved remarkable advancements in generating FAQs for 5 e-commerce websites.
22% increase in clicks on lifted PLPs with Generated Question&Answers + FAQpage markup (January-December 2023) YearOverYear Comparison
By implementing a specialized reporting dashboard through ourLooker Studio Connector, we’ve equipped EssilorLuxottica with a powerful tool to monitor and evaluate performance metrics across its wide range of offerings. This empowers them to make informed decisions and refine their strategies based on data-driven insights. The integration of AI-driven solutions has significantly elevated EssilorLuxottica’s efficiency, precision, and strategic acumen in their digital marketing endeavors.
Generate Content at Scale using LLM and KG
Employing a meticulous process of data preparation and model building, EssilorLuxottica has achieved a significant milestone in content generation.
Our approach to AI-generated content revolutionizes the content creation landscape, bridging the gap between human creativity and AI capabilities. We address the challenge of subpar automated content, ensuring a higher quality standard.
With a Knowledge Graph-centric methodology, we eliminate the need for extensive external data, emphasizing sustainability and ethical AI use. Our validation rules further enhance precision—this fusion of AI and human oversight results in top-tier content, robust control, and eco-conscious practices.
By developing a specialized dashboard,we’ve developed a powerful content generation tool that not only aids in the content generation process but also serves as a valuable resource for the EssilorLuxottica team, ensuring seamless integration and accessibility for future endeavors. This strategic advancement underscores our commitment to leveraging cutting-edge technology to enhance the brand’s digital presence and solidify EssilorLuxottica’s position as a leader in the eyewear industry. Using it, nowwe are able to produce +1000 completions per minute. Our tailored approach has not only ensured a consistent and high-quality output but has also provided a substantial boost to the overall content production capacity.
Analyzing the data from July 1st to September 1st in a Year-over-Year comparison, we observe a commendable stability with a noteworthy increase of +5.4% in clicks.
Opportunities
In its pursuit of digital excellence, EssilorLuxottica has harnessed the transformative power of data control. This key aspect has driven it to create tailored content and customized models for its audiences. This newfound mastery of data has laid the foundation for a dynamic and personalized digital experience.
A significant leap forward was made by implementing an automation system powered by artificial intelligence that synchronized multiple data points. This ensured the delivery of up-to-date information and helped create a streamlined and efficient process (PKG). The accuracy of data synchronization became a milestone, improving the overall accuracy of content delivery and reinforcing EssilorLuxottica’s commitment to excellence.
Throughout the journey, the significant increase in the learning curve became a testament to the collaborative efforts between WordLift and the EssilorLuxottica team. The challenges were not simply obstacles but opportunities for growth. This enhanced collaboration improved the teams’ collective knowledge and resulted in more effective strategies and streamlined operations.
Adopting AI technologies has emerged as a proactive measure, especially for navigating the dynamic digital landscape during critical times. The integration of AI facilitated adaptability and allowed EssilorLuxottica to remain at the forefront of an ever-changing technological environment. The proactive approach ensured that strategies remained current and forward-looking, aligning perfectly with the company’s vision for digital innovation.
In conclusion, EssilorLuxottica’s journey reflects a transformative adoption of data control, AI-based automation, and a commitment to continuous learning. These strategic initiatives have positioned the company as a digital leader and laid the foundation for sustained success in an ever-evolving digital ecosystem. As EssilorLuxottica continues to pioneer innovation, the synergy with WordLift remains a driving force, propelling it to new heights of digital excellence.
Note on the methodology used
The methodology that we used to calculate the achieved outcomes is causal inference.
It is a branch of statistics that helps us isolate the impact of the change by comparing the results achieved with the predicted results we would have had without the implementation by the team.
The prediction uses a Bayesian structural time-series model that learns from the clicks we had before the intervention (occurring during the dedicated period) on the pages considered.
To perform the evaluation, we used CausalImpact, an open-sourced Google tool that created baseline values for the period after the event.
“Our long lasting collaboration with Wordlift helped the e-commerce division of EssilorLuxottica moving ahead of the trends, supporting the SEO activities of the group to achieve more revenues at scale thanks to the innovative usage of markups and AI”.
Federico Rebeschini – Global Head of SEO and Performance at EssilorLuxottica
The concept of “human in the loop” primarily concerns the quality of the data used to craft prompts for AI systems. To ensure fair and scalable AI systems, WordLift utilizes the Retrieval Augmented Generation (RAG) approach.
RAG combines a retriever and a generator to enhance the capabilities of Large Language Models (LLMs). The retriever scours the knowledge graph to find relevant information, which the generator then uses to craft accurate and contextually precise responses. This approach addresses the limitations of traditional LLM usage by providing up-to-date and reputable information. You can learn more about RAG and its benefits in the article here.
Prompt engineering is a technique used in machine learning to provide additional information or insights to improve the predictions of a model. It involves including this information in the prompt used during training or fine-tuning. Prompt engineering is considered a form of technical SEO and can be used to enhance the capabilities of AI systems in generating content. To learn more about prompt engineering and its application in natural language generation, you can read this article: ChatGPT Prompt Engineering: NLG – A Detailed Overview.
