Reinforcement Learning-Enhanced SEO: Automating Keywords and Backlinks for Growth – Next Gen SEO with Hyper Intelligence

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The purpose of this project is to use Artificial Intelligence (AI), specifically a technique called Reinforcement Learning (RL), to make Search Engine Optimization (SEO) smarter and more efficient. The main aim is to help websites grow by automatically deciding which keywords and backlinks to use without requiring a human to make these decisions manually.

What is SEO?

SEO improves a website’s appearance in search results (like Google). Websites that rank higher get more visitors, which often means more business or exposure.

The Problem:

Traditionally, SEO requires people (like website owners or marketers) to manually pick the right keywords (the terms people search for) and create backlinks (links from other websites to your site) to help the website rank higher. This can be time-consuming, and the right choices can help the website’s visibility.

What Does Reinforcement Learning Do?

Reinforcement Learning (RL) is an AI that learns by trying things out and improving its actions based on the results. Just like a person learns from experience, RL learns by doing and improves at making decisions over time.

In this project, Reinforcement Learning is used to:

1. Choose the best keywords to target based on current website traffic data.
2. Select the most effective backlinks to use for improving the website’s ranking.
3. Continuously improve these choices as more data (such as website visitors, traffic patterns, and engagement levels) is fed into the system.

How Does This Help Website Owners?

The key benefit of using Reinforcement Learning in SEO is that it automates the process of optimizing a website. Instead of relying on human judgment or experience, the AI system makes these decisions automatically based on real-time data. This saves time and ensures that the website constantly improves its chances of ranking higher on search engines like Google.

With this project, the website can:

• Adapt to changing traffic levels without human intervention.
• Test different keywords and backlinks to see what works best, adjusting strategies in real-time.
• Boost traffic by making smarter, data-driven decisions about SEO.

Who Can Benefit from This Project?

• Website owners who want to grow their audience but need more time or expertise to manage SEO manually.
• Marketers looking to automate their SEO tasks and improve efficiency.
• Businesses that rely on website traffic for customers or visibility.

What is Reinforcement Learning for SEO?

Reinforcement Learning (RL) is a type of machine learning in which an algorithm learns by interacting with an environment and receives feedback in the form of rewards or penalties. For SEO (Search Engine Optimization), this means using RL to improve website performance by continuously adjusting strategies like content updates, link-building campaigns, or keyword use based on how these actions affect website ranking and traffic in real-time.

Use Cases of Reinforcement Learning for SEO

1.    Content Optimization: RL can suggest the best ways to update or create new content by analyzing what drives traffic and improves rankings over time. For example, the algorithm can track the performance of different article topics or keywords and suggest adjustments to improve visibility.

2.    Link-Building: The algorithm can decide where and when to build external links (backlinks) or internal links based on past data. It can learn which links bring more traffic and improve rankings, optimizing link-building campaigns automatically.

3.    Keyword Targeting: RL can help identify which keywords to target or focus on by analyzing which drives traffic over time, allowing the system to adjust strategies dynamically.

Real-Life Implementation of RL for SEO

Imagine a website where you want to improve SEO performance, say a blog. In this case, RL can be implemented to monitor user behavior, track which pages perform well (in terms of traffic, bounce rate, etc.), and adjust various website elements automatically. For example, it could:

• Dynamically suggest which blog posts to promote.
• Recommend changes in content format or structure (like adding images, videos, or headings) to improve user engagement.
• Automatically adjust keywords in your content based on real-time trends.

Use Case in the Context of a Website

For your project related to a website owner, the RL algorithm can interact with the website’s SEO data. Let’s assume your client wants to improve how their blog ranks on Google. The RL model can monitor how each page performs—such as how long people stay on a page, which pages lead to conversions, or which pages are being ignored. Based on this, it can dynamically suggest:

• Changes to content (like rewriting certain paragraphs, adding keywords, etc.).
• Which old blog posts should be updated and how?
• Optimal internal links between different blog posts to increase overall engagement.

How Does the Code Work?

As a non-tech person, don’t worry about the technical complexities. Here’s the simple version:

1.    Input Data: The RL model needs data to work. This data can either be URLs from the website (where the algorithm crawls and processes content) or in a structured format like a CSV file that contains SEO metrics (like page views, rankings, bounce rates, etc.).

• URLs: If you provide URLs, the algorithm can automatically fetch the page content, analyze it, and decide how to improve it.
• CSV Data: A CSV file can contain columns like keywords, ranking positions, page views, etc., which the model will use to make decisions.

2.    RL Process: The algorithm learns over time. It checks how changes it suggests (like updating content, adding links, or targeting new keywords) affect your SEO performance and adjusts its strategy accordingly. The process involves:

• Action: The RL model suggests an SEO action (like updating a page or building a link).
• Feedback: The model checks whether the action improved or hurt performance (e.g., if the page ranks higher or gets more traffic).
• Learning: The algorithm learns and suggests better actions over time based on the feedback.

3.    Output: The final output would be a set of recommendations or automatic updates to the website that are aimed at improving SEO performance, such as:

• Which blog post to promote or update.
• How to optimize content for better user engagement.
• Which keywords to focus on for improving rankings.

Data Needed for RL in SEO

The model needs real-time performance data to make decisions. Common data includes:

• Page Traffic: How many visitors each page gets.
• Keyword Performance: How well specific keywords rank over time.
• User Engagement: Metrics like bounce rate, time on site, and conversion rates.
• Backlink Data: Information about the number and quality of links pointing to the website.

The model uses this data to evaluate its actions (like content updates or link-building) and decide what to try next to maximize SEO performance.

Why is RL Useful for SEO?

RL is useful because SEO is dynamic—search engine algorithms change frequently, and user behavior can shift over time. Using RL, you create a system that constantly adapts and improves based on real-time data. This makes SEO strategies more efficient and reduces the guesswork involved. It helps websites stay competitive in search engine rankings without needing constant manual intervention.

What is this Output?

This output shows two key pieces of information:

1. Cosine Similarity Matrix between URLs: This part shows how similar different pages on your website are in terms of content.
2. Backlink Recommendations for Each URL: Based on the similarity between pages, it provides recommendations for which pages should be linked. This can help improve internal linking for SEO purposes.

Let’s go through each part in detail, step by step.

1. Cosine Similarity Matrix between URLs

The Cosine Similarity Matrix compares the content of different URLs (webpages) on your website to determine their similarity. The similarity is measured using a number between 0 and 1:

• 1: Perfect similarity (the content is very similar).
• 0: No similarity (the content is completely different).

The matrix shows the similarity between every combination of URLs on your website.

Example from the matrix:

• https://thatware.co/ is compared with itself (which is why the value is 1.0, meaning perfect similarity).
• https://thatware.co/services/ has a similarity score of 0.973669 with the homepage, meaning the content of the /services/ page is similar to the homepage.

Each row and column represent different pages on your website, and the numbers represent how closely related their content is.

What Does This Mean?

• The Cosine Similarity helps you understand which pages have content that overlaps or is closely related.
• Pages with high similarity cover similar topics or services, so you can cross-link them to guide users and improve SEO.

For example, if two pages have a high similarity, like:

This suggests that these two pages might benefit from internal linking, which would help users navigate between similar topics and improve SEO.

2. Backlink Recommendations for Each URL

This section provides backlink recommendations based on the similarity scores from the Cosine Similarity Matrix. A “backlink” in this context means creating internal links between pages on your website. These links are beneficial for SEO, as they:

• Help users navigate between related pages.
• Improve the flow of “link juice” (SEO value) within your website, making it more likely for important pages to rank higher on search engines.

Each recommendation tells you which URLs (pages) to link to from a specific page on your website.

Example from the recommendations:

This means that for the homepage (https://thatware.co/), you should consider adding links to the following pages:

• Competitor Keyword Analysis (https://thatware.co/competitor-keyword-analysis/)
• Services (https://thatware.co/services/)
• Conversion Rate Optimization (https://thatware.co/conversion-rate-optimization/)

What Does This Mean?

• By following these recommendations, you can strengthen the internal linking structure of your website.
• For example, if you have a page about Conversion Rate Optimization that is highly related to your homepage, you should link to it from your homepage. This will help users and search engines understand that these pages are closely related.

Summary of Steps You Should Take:

1. Using the Cosine Similarity Matrix, understand which pages are closely related. Look for high similarity scores (closer to 1).
2. Follow the backlink recommendations by creating internal links between related pages. This improves SEO by connecting similar content and helping search engines understand the relationship between different pages on your website.

Explanation of Each Key Concept in Simple Terms:

·         Cosine Similarity Matrix: This table shows how similar the content of different pages on your website is. The more similar the two pages are, the higher the number (closer to 1) you’ll see. You can use this information to link similar pages together.

·         Backlink Recommendations: These are suggestions for which pages you should link to from each page. Linking related pages together helps users find relevant information more easily and boosts your site’s SEO.

What to Do Next:

• Based on the recommendations, add internal links. Check which other pages are similar for each page and add links between them.
• Check the content of highly similar pages. If two pages are too similar, you should adjust the content so each page focuses on a unique topic.

Why This is Important:

• Internal linking helps search engines understand your website’s structure, which can lead to higher rankings in search results.
• Improved user navigation: When users can easily find related content, they spend more time on your site, which is also good for SEO.

Part 1: Data Loading and Keyword Extraction

Name: Data Collection and Keyword Extraction

What it does:

• This part of the code loads website data and fetches content from web pages.
• It uses libraries like pandas to load data files and requests with BeautifulSoup to grab the text from the URLs (web pages).
• The code then cleans the text from the web pages by removing unnecessary things like numbers, punctuation, and common words that don’t add much meaning (called stopwords).
• After cleaning, the code uses a method called TF-IDF (Term Frequency-Inverse Document Frequency), which finds the most important keywords from the cleaned text of each web page.
• These keywords are extracted and displayed in a table, listing to find the most important keywords in each web page’s cleaned text the most relevant words for each web page.

Purpose: The first part of the code gathers data and extracts keywords that will be used later to make decisions about SEO.

What is this output?

The output shows different data related to user behavior and page performance on your website, alongside keyword extraction from your web pages. This information is used to understand how users interact with your website and to analyze which keywords are most important for SEO (Search Engine Optimization). Let’s walk through each section of the output.

1. Pagewise User Flow Data

This section provides information about user interaction with specific pages on your website. Here’s what the columns mean:

• Page path and screen class: The specific page on your website (e.g., /, /services/, etc.).
• Views: The total number of page views, i.e., how many times a page was viewed.
• Active users: The number of unique users actively interacting with the page.
• Views per active user: This shows how often an active user viewed the page. For example, if a user visited a page twice, the value would be above 1.
• Average engagement time per active user: The average time (in seconds or minutes) each active user spent on the page.
• Event count indicates the total number of events (clicks, scrolls, interactions) on the page.
• Key events are special or significant events (e.g., button clicks, form submissions) that you should track.
• Total revenue: This would represent any revenue generated from the page (e.g., through sales), but in this case, the value is 0, indicating no direct revenue tracked.

Example from the output:

This means that your homepage (/) was viewed 36,464 times by 27,161 unique users. Each user viewed the page about 1.34 times on average, indicating that some users may have returned to it.

2. User Flow Data

This section gives insights into how users arrived at your website (the “channel group”) and their behavior. Here’s what each term means:

• First user primary channel group (Default Channel Group): This shows how users first discovered your website. For example, “Organic Search” means users found your site via search engines like Google.
• Total users: The total number of users coming from that particular channel.
• New users: The number of users who visited your website for the first time.
• Returning users: The number of users who have visited your site before and returned.
• Average engagement time per active user: The average time these users spent on your site.
• Engaged sessions per active user: How many interactions or “sessions” occurred per user?
• Event count: The total number of interactions or actions (e.g., clicks, form submissions) made by users.
• Key events: The number of important events tracked (e.g., purchases, downloads).
• User key event rate: This shows how users performed key events relative to their interactions.

Example from the output:

This means that 17,155 users came to your site through organic search, 16,986 of whom visited for the first time.

3. User Behavior Data

This part repeats information similar to “Pagewise User Flow Data” but focuses on user behavior. It shows how users interact with specific pages by looking at:

• Views: The total number of views for each page.
• Active users: How many users interact with the page?
• Views per active user: How often did each user view the page?
• Average engagement time per active user: How long did users stay on each page?
• Event count: Total number of actions (clicks, interactions) performed on the page.
• Key events: Important actions like purchases, form submissions, etc.

Example from the output:

This shows that your homepage had 40,681 views and 30,791 active users. On average, each user viewed the page 1.32 times.

4. Extracted Keywords for each webpage (Top 50)

This section provides the top 50 keywords extracted from each webpage’s content. These words or phrases appear most frequently and are most relevant to the page’s content. The keywords are ranked by their importance or frequency, and each has a numerical value associated with it, which reflects its significance on the page.

• Top Keywords: These are the keywords your model identified as important for each page’s SEO. The more these keywords are included (in a relevant and natural way), the more likely it is for that page to rank higher on search engines.

Each webpage will have its own set of top keywords, and their associated values show their prominence in the page’s content.

Example from the output:

This means that on the /services/ page, the most important keyword is “SEO” with a value of 0.801, followed by “services” (0.557). These keywords are key for optimizing your page for SEO purposes.

What should you do with this data?

As a website owner or marketer, here’s what you can do with the information:

1.    Optimize Content with Keywords:

• For each page, look at the extracted keywords and make sure those keywords are naturally and frequently used in the content. For example, if “SEO” is a top keyword for your /services/ page, ensure that the page mentions SEO in headings, text, and metadata.
• If a keyword that should be important needs to be added to the top keywords list, consider adding content around that keyword.

2.    Improve User Engagement:

• Check the engagement time per user for different pages. If users spend less time on a page than expected, it may indicate that the content needs to be more engaging. You could add more detailed information, images, or videos to engage users.

3.    Boost Page Performance:

• Review the event counts and key events for each page. If certain pages have low event counts, you can improve the call-to-action (CTA) buttons or make the page easier to navigate so users interact more with it (e.g., submit a form, click a button).

4.    Analyze Traffic Sources:

• Look at the user flow data to see where your traffic is coming from (e.g., Organic Search, Direct, Social Media). If organic search is performing well, continue focusing on SEO. If traffic from social media is low, you can boost your social media marketing efforts.

Conclusion

This output provides valuable insights into how your users interact with your website and which keywords are most important for optimizing your content. The main takeaways for you as a website owner are:

• Ensure that important keywords are used on each page in a relevant way.
• Increase engagement on low-performing pages by improving content or design.
• Use traffic data to focus on channels that bring the most users, and optimize the underperforming ones.

Part 2: Keyword and Backlink Recommendations

Name: Automatic Keyword and Backlink Suggestions

What it does:

• This part uses the keywords extracted in the first part and generates keyword recommendations for each webpage. Based on the data from TF-IDF, it picks the top 5 keywords for each page.
• It also generates backlink suggestions by searching the Internet for relevant links. It does this by performing a Google search based on each webpage’s content.
• The code displays both keyword and backlink recommendations for each web page, suggesting what keywords to target and which external websites to link to for better SEO performance.

Purpose: This part automatically recommends keywords and backlinks that can improve SEO for each webpage.

What is this output?

The output you shared provides keyword recommendations and suggested backlinks for each page of your website. These recommendations are generated by a model designed to improve your website’s SEO (Search Engine Optimization) performance.

The model analyzed the content of the different URLs (pages) on your site and came up with suggestions for:

1. Top Keywords: These are the most important words or phrases that should be included in the page’s content to improve search engine ranking.
2. Suggested Backlinks: These are links to other websites or internal pages that your website should include or try to get linked from to improve its authority and SEO ranking.

Now, let’s go through the different parts of the output.

Example Breakdown:

Let’s take the first page in the list as an example:

1.    Recommendations for: https://thatware.co/digital-marketing-services/:

• The model analyzed this page on your website: the “Digital Marketing Services” page on that ware.co.

2.    Top Keywords:

• These are the most important keywords that the model suggests using (or optimizing) on the page to improve its visibility in search engines:
  • marketing
  • digital
  • seo
  • services
  • strategy
• These keywords are essential because they reflect what people are likely searching for when looking for digital marketing services. Using these keywords effectively in your content can make your page more relevant to those searches.

3.    Suggested Backlinks:

• You should try to get or link to these links from other websites. Backlinks from high-quality, relevant sites increase your website’s authority and improve its SEO.
  • https://thatware.co/advanced-seo-services/: This is an internal link to another page on your website that can boost both pages’ SEO when they link to each other.
  • https://www.infintechdesigns.com/web-design-digital-marketing-enterprise/: This is a backlink suggestion from an external website in the digital marketing space. Getting a link from this website can improve your page’s authority.
  • https://coalitiontechnologies.com/digital-marketing: Another external link from a reputable digital marketing company.

Let’s go through another example:

1. Page Analyzed: This is your “Business Intelligence Services” page.
2. Top Keywords:
   • seo: Search engine optimization, a critical keyword for your business.
   • services: Reflects the services you offer.
   • business, data, analysis: Important terms related to business intelligence that the model recommends optimizing on the page to make it more relevant to user searches.
3. Suggested Backlinks:
   • The suggested backlinks come from websites that specialize in SEO and competitor analysis:
     • https://sureoak.com/services/seo-competitor-analysis: A high-authority link suggestion from a website offering competitor analysis services.
     • https://brandauditors.com/blog/seo-competitor-analysis-services/: Another relevant link from a blog focused on SEO competitor analysis.
     • https://www.vixendigital.com/seo-agency/seo-competitor-analysis-services/: Another backlink suggestion from an SEO agency offering competitor analysis services.

What should you do with this output?

1.    Use the Top Keywords:

• For each page, start by ensuring that the top keywords provided are used in the content. These keywords should be naturally included in the page’s headings, body text, and meta tags (title, description).
• The goal is to make the page more relevant to what people search on Google. For example, on your “Digital Marketing Services” page, make sure you use words like “marketing,” “digital,” “SEO,” and “strategy” naturally within the content.

2.    Build the Suggested Backlinks:

• Internal Links: Start by linking between your pages. If the content on your “Digital Marketing Services” page is relevant, add a link to the “Advanced SEO Services” page.
• External Backlinks: Try to get backlinks from the websites the model suggested. You can do this by contacting the site owners and asking if they would be willing to link to your page, especially if your content is relevant and high-quality. For example, you could contact infintechdesigns.com and explain how your content could complement theirs.

Summary of what this output means:

1.    Page-Specific Recommendations: For each page, the model analyzes which keywords are most effective in improving SEO and suggests the best words to use on that page to increase its relevance to search engines.

2.    Backlink Suggestions: The model also suggests backlinks—both internal (within your website) and external (other websites)—that can improve your SEO authority. Building these backlinks can increase your chances of ranking higher in search results.

As a website owner, what should you do next?

·         Update the Content: Based on the top keyword recommendations for each page, go through and update the content to reflect those keywords. Ensure they are used naturally and in a way that provides value to your visitors.

·         Pursue Backlink Opportunities: For the suggested backlinks, prioritize getting links from the external websites the model recommended. This may involve contacting those sites and explaining how your content can add value.

Part 3: Reinforcement Learning for SEO Actions

Name: SEO Action Learning with Reinforcement Learning

What it does:

• This part introduces Reinforcement Learning (RL) to help the system learn which actions (like using specific keywords or backlinks) lead to the best improvement in website traffic.
• The code defines states (low, medium, high traffic) and actions (using keywords and backlinks).
• A Q-table is created, like a scoreboard where the system keeps track of which actions perform best for each traffic state.
• Over time, the system learns by trying different actions, checking how much traffic changes, and updating its knowledge in the Q-table to improve future decisions.
• After running for several rounds (called episodes), the system becomes better at choosing the best SEO actions to boost traffic.

Purpose: This part trains an AI model using reinforcement learning to learn from experience and decide which keywords or backlinks work best to improve traffic.

What does this output represent?

The output is a Q-table after training a Reinforcement Learning (RL) model. This table is the result of the model “learning” the best actions to take (such as applying a specific keyword or backlink) based on different traffic levels. Let me explain what each part of this means.

What is the Q-table?

• The Q-table is where the model stores the values it has learned over time. These values help the model decide which actions (e.g., use a keyword or a backlink) are the most effective in improving the website’s performance based on the current traffic level (low, medium, or high traffic).

Structure of the Q-table:

The Q-table is a matrix (table) with rows representing different traffic states (low, medium, high traffic), and columns representing different actions (e.g., using specific keywords or backlinks).

In this case:

• The rows represent the traffic states:
  1. Row 1 (index 0): Low traffic
  2. Row 2 (index 1): Medium traffic
  3. Row 3 (index 2): High traffic
• The columns represent the actions the model can take (e.g., using a specific keyword or applying a backlink).

Each number in the Q-table represents the “value” of that action in a particular traffic state. A higher value means that taking that action will likely improve the website’s performance more effectively in that state.

Example:

Let’s break down the first row:

• This row corresponds to low traffic (the first state).
• Each number in this row represents how effective a specific action is for low traffic. The actions might include using specific keywords (like “seo,” “services,” “marketing”) or applying backlinks to improve your website.
• The higher the number, the better that action is at improving your website’s SEO in low traffic conditions.

For example:

• 2105.19054338 is the value of taking action 1 (e.g., using the keyword “seo”).
• 1152.65112375 is the value of taking action 2 (e.g., applying a specific backlink).

From this, we can say that for low traffic, action 1 (using the “seo” keyword) seems to be the most effective, since it has the highest value (2105.19054338).

How do we interpret the output?

1.    First row (Low traffic): The highest value in this row is 2105.19054338, which means that for low traffic, the model recommends using action 1 (likely a keyword like “seo”) because it has the most significant positive impact on improving traffic in this condition.

2.    Second row (Medium traffic): The highest value in this row is 2025.9051201, so for medium traffic, the model suggests action 5 (perhaps using the keyword “marketing” or a more advanced strategy).

3.    Third row (High traffic): The highest value in this row is 2014.57091612, meaning for high traffic, action 4 (perhaps using an advanced backlink or marketing strategy) is the best choice to keep improving the website’s performance.

What does this output tell you?

• For low traffic, the model suggests focusing on actions like using SEO-related keywords because they have proven effective in increasing traffic.
• For medium traffic, you might need to shift to more advanced SEO or marketing strategies.
• For high traffic, more complex strategies such as targeted backlinks or broad marketing campaigns will yield the best results.

What should you, as a website owner, do with this output?

1.    Low Traffic: If your traffic is low, the model suggests focusing on basic SEO strategies, such as incorporating keywords like “seo” and applying effective backlinks. These actions will help boost your visibility and start driving more traffic to your website.

2.    Medium Traffic: For medium traffic, the model recommends using advanced SEO strategies, such as targeting more specific or competitive keywords like “advanced SEO services” or improving your backlinking strategy.

3.    High Traffic: For high traffic, it’s time to focus on broad marketing and scaling strategies to maintain and further boost your performance. This might involve bigger marketing campaigns or using high-quality backlinks to continue growing your audience.

In Simple Terms:

• The model is “telling” you what actions are most effective depending on how many visitors your website is getting (traffic).
• Based on your current traffic, you can make the right changes to your site (using the keywords or backlinks the model recommends) to boost your traffic further.
• The numbers in the Q-table help the model know which actions are better. The higher the number, the better that action is for your site.

Next steps:

1. Monitor your traffic: Based on whether you have low, medium, or high traffic, follow the model’s recommendations.
2. Take action: Use the recommended keywords or backlinks based on your current traffic state.
3. Track performance: After applying the model’s recommendations, check if your traffic is improving over time.

Part 4: Real-Time Recommendations Based on Traffic

Name: Real-Time SEO Action Recommendations

What it does:

• This part uses the trained Q-table (from the previous part) to make real-time recommendations for which keywords or backlinks to use based on the current traffic level.
• It defines three traffic levels (low, medium, high) and suggests the best SEO action (whether to use a keyword or backlink) depending on the traffic.
• It simulates some real-time traffic values and provides recommendations for what actions to take to improve SEO at that moment.

Purpose: This part automatically recommends the best action (keyword or backlink) to apply in real-time, based on how much traffic the website is getting.

What does the output mean?

The output is based on the traffic levels of your website. The model is trying to give you automatic recommendations (keywords or backlinks) to improve your website’s SEO based on the traffic it sees. Here’s a breakdown of each traffic level and its corresponding recommendation:

1.    Traffic level 300: “use_seo”

• What it means: The model detected that your website has low traffic (300 visitors). In this case, the model recommends focusing on the keyword “seo”.
• Why?: SEO is essential for boosting your website’s visibility when traffic is low. Adding the keyword “seo” in your content (such as blog posts, product descriptions, or service pages) can help make your website more searchable on search engines like Google.

2.    Traffic level 1500: “use_advanced”

• What it means: The model found medium traffic (1500 visitors). Here, it suggests focusing on the keyword “advanced”.
• Why?: The model is recommending you to improve your content by using more advanced keywords related to your services, such as “advanced SEO techniques.” It may suggest this because, at a medium traffic level, you’re trying to attract more specialized or experienced users who search for advanced topics.

3.    Traffic level 6000: “use_marketing”

• What it means: When your traffic is high (6000 visitors), the model recommends focusing on the keyword “marketing”.
• Why?: The keyword “marketing” might attract even more visitors or expand your audience. Since your website already has high traffic, you should target broad, important terms like “marketing” to further enhance your reach and appeal to a wider audience.

What should you do as a website owner?

Here’s how to use the output in practical terms to help your website grow:

1.    For traffic level 300 (low traffic):

• Action: Focus on optimizing your content with SEO-related keywords like “seo” or phrases like “how to improve SEO.”
• Why?: When traffic is low, your priority should be to make your website easier to find on search engines. You can write articles, create guides, or add sections to your website that specifically talk about SEO.

2.    For traffic level 1500 (medium traffic):

• Action: Use more advanced content that appeals to users who are looking for deeper information. Include keywords like “advanced SEO” or “advanced marketing strategies” in your posts.
• Why?: When traffic is growing, people expect more from your site, such as detailed guides or professional-level content. This will help your site stand out to more experienced users and keep them engaged.

3.    For traffic level 6000 (high traffic):

• Action: Incorporate marketing-related keywords into your website, such as “digital marketing,” “content marketing strategies,” or “online marketing tips.”
• Why?: At this stage, your site is already attracting a lot of traffic, so you want to expand even further. Using “marketing” keywords helps attract new users interested in promoting their businesses or improving their own marketing skills.

How should you communicate this to the client?

As a website owner or SEO consultant, you can explain this output to your client like this:

• “The model is helping us figure out what actions to take based on how much traffic the website is getting. When traffic is low, we focus on foundational things like SEO, when traffic is growing, we use more advanced topics, and when traffic is high, we can expand even further by targeting broader topics like marketing.”

This explanation will show the client that the model is designed to guide them based on their current traffic situation, ensuring their website continuously improves its performance.

In Summary:

• Traffic 300: Low traffic → Focus on basic SEO keywords.
• Traffic 1500: Medium traffic → Add advanced topics to attract more engaged users.
• Traffic 6000: High traffic → Target broad marketing topics to further grow the site.

What next steps should you take?

• Update your content: Based on the current traffic level, update the content on your website using the recommended keywords or topics.
• Track progress: After making changes, monitor your traffic to see if there’s an improvement. This can help you adjust your strategy further.

Repeat the process: As traffic changes, repeat this process. The model will give you different recommendations based on the traffic.