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RoBERTa, a powerful natural language processing model developed by Facebook AI, offers advanced capabilities in understanding the context of words and sentences. This project showcases the application of RoBERTa for analyzing and enhancing website content, specifically by evaluating the alignment between the search intent of users and the page type of web content.

The primary goal is to assess whether a webpage is structured in a way that meets the expectations of users based on their search intent. Through this analysis, suggestions are generated for improving the content’s alignment with user expectations, ultimately helping to improve the website’s SEO performance and relevance in search engine results.

By analyzing the title and meta description of a web page, this project demonstrates how RoBERTa can be used to:

·         Classify the search intent behind the content (e.g., informational, transactional).

·         Identify the page type (e.g., blog, product, service).

·         Provide actionable suggestions when there is a mismatch between the two, allowing for informed decisions regarding content optimization.

Purpose of This Project

The purpose of this project is to enhance SEO by evaluating and improving website content. The project focuses on classifying web content into search intent and page type categories, allowing for a deeper understanding of how well a page serves user needs.

Through this classification, the project aims to:

·         Assess whether the page content aligns with user expectations.

·         Highlight potential areas where content and structure can be improved to better match search intent.

·         Provide automated insights and suggestions for SEO optimization.

This project is designed to demonstrate how AI-powered language models like RoBERTa can be applied to SEO workflows, enhancing the decision-making process and ensuring that web content remains relevant, accessible, and optimized for search engines.

What is Search Intent?

Search intent refers to the reason behind a user’s search query. It reflects what the user is hoping to achieve when they perform a search. Broadly speaking, there are four types of search intent:

Informational: The user is looking for information, such as definitions, guides, how-to articles, or general knowledge on a topic.

Navigational: The user is trying to find a specific website or webpage, such as a company’s home page or a specific blog post.

Transactional: The user is ready to take an action, such as purchasing a product, subscribing to a service, or downloading a resource.

Commercial Investigation: The user is comparing options, researching products or services, and evaluating different choices before making a decision.

Understanding the type of intent behind a search helps ensure that the content provided matches the user’s needs, which is crucial for improving user satisfaction and SEO performance.

What is Page Type?

Each webpage on a site serves a specific purpose and can generally be classified into one of several page types. These may include:

Blog: Pages offering articles or content focused on providing information.

Product Pages: Pages designed to showcase and sell products.

Service Pages: Pages focused on describing and promoting services.

Landing Pages: Targeted pages designed for specific actions or marketing campaigns.

FAQ Pages: Pages dedicated to providing answers to common questions.

About Pages: Pages providing information about the company, its team, and its mission.

Category Pages: Pages that group related products or articles together.

The type of page should align with the intent behind the user’s search to ensure they find what they are looking for.

RoBERTa Model Explanation

What is RoBERTa

RoBERTa (Robustly Optimized BERT Pretraining Approach) is a powerful language model designed to understand text in a deep and meaningful way. It is based on BERT (Bidirectional Encoder Representations from Transformers), which is widely known for its ability to understand the context of words in sentences. RoBERTa improves upon BERT by optimizing its training process to enhance performance, making it particularly powerful for a wide range of tasks, including search engine optimization (SEO).

At its core, RoBERTa is a transformer-based model that has been trained to process and understand vast amounts of text data. By learning how words relate to each other, RoBERTa can identify the meaning of sentences and the context in which words are used. This deep understanding is essential for SEO, where it’s not just important to match keywords, but also to understand the overall meaning of the content on a webpage.

How RoBERTa Works

·         Contextual Understanding: RoBERTa reads sentences and understands the relationships between words, allowing it to capture the meaning of the entire sentence. This helps ensure that content is relevant to user searches, which is essential for improving SEO performance.

·         Improved Training: While RoBERTa is based on BERT, it uses optimized training techniques to perform even better. These optimizations help RoBERTa better understand and process content, making it more effective in analyzing the text on a webpage.

·         Language Comprehension: RoBERTa is excellent at comprehending words in context, which makes it ideal for understanding search intent and categorizing pages correctly. For SEO, this means RoBERTa can analyze whether the content of a page truly matches what a user is looking for, improving the relevance of the page in search results.

How RoBERTa Helps with SEO Tasks

In this project, RoBERTa performs two essential functions:

·         Search Intent Classification: RoBERTa analyzes a webpage’s content to determine the search intent behind it. This can be informational (e.g., seeking knowledge), transactional (e.g., looking to buy), or navigational (e.g., trying to find a specific website or page). Understanding the intent helps ensure that the content aligns with user needs and expectations.

·         Page Type Classification: RoBERTa helps categorize pages based on their content type, such as blog posts, product pages, or service pages. This ensures that each page is correctly classified, helping search engines understand what the page is about and how it should rank.

Why RoBERTa is Effective for This Project

The ability to understand words in context makes RoBERTa an excellent tool for analyzing web content. For SEO, RoBERTa helps ensure that content matches user search intent and is properly categorized by page type. This alignment is crucial for improving a website’s search engine ranking and visibility.

How will this project help improve website’s SEO performance?

By applying RoBERTa to website, this project ensures that the content is relevant to user queries and properly categorized. It identifies gaps or mismatches in the current content strategy and offers actionable suggestions to improve the relevance of the pages, which can help drive more traffic to the website and improve its position in search results.

Why is search intent classification important for SEO?

Search intent classification is crucial for SEO because it helps ensure that the content on a website aligns with what users are actually looking for. By understanding whether a user’s search is informational, transactional, or navigational, website content can be tailored to meet these specific needs. This improves the chances of ranking higher in search engine results, as search engines prioritize content that is directly relevant to user queries.

How does page type classification improve SEO?

Page type classification is important because it helps to accurately categorize the content of a webpage. When pages are properly classified (as blogs, product pages, service pages, etc.), search engines can better understand the purpose of each page and rank it appropriately. This increases the chances of the page being discovered by users who are specifically looking for that type of content, improving visibility and organic traffic.

How will search intent classification practically help website owners?

By classifying the search intent behind queries, website owners can tailor their content more effectively to meet user needs. For example, if the intent is informational, website owners can ensure their content provides clear, in-depth answers to questions. If the intent is transactional, the content can be optimized with more persuasive calls-to-action (CTAs) and product details. This increases the relevance of the content and can lead to better rankings, more traffic, and higher conversion rates.

What practical SEO improvements can be made after running the analysis with RoBERTa?

After running the analysis, website owners can make practical SEO improvements such as:

• Content optimization: Ensure content matches the user’s search intent (informational, transactional, etc.), improving relevance and user engagement.
• Content restructuring: Convert pages into the correct type, such as turning a product description into a more detailed landing page or blog-style content into an FAQ.
• Identifying underperforming pages: The analysis may highlight pages that are not categorized correctly, allowing for targeted updates or reclassification. These improvements help the website better satisfy search engine algorithms, leading to better rankings and more targeted organic traffic.

Libraries Used

requests

The requests library is used to make HTTP requests to web pages. It allows for the easy retrieval of web page content by sending GET requests. This is crucial for scraping the content of the websites to be analyzed. Without this, fetching content from external URLs would be difficult.

Purpose: Fetch web page content from URLs to analyze titles, meta descriptions, and other relevant data for search intent and page type classification.

BeautifulSoup (from bs4)

BeautifulSoup is a powerful Python library used to parse HTML and XML documents. It is used to extract specific content from web pages, such as titles, meta descriptions, and other tags. This library allows for easy navigation and extraction of the HTML tree.

Purpose: Scrape and parse content from web pages to retrieve the title, meta descripti

pipeline (from transformers)

The pipeline function in the transformers library is a high-level API that allows seamless integration of pre-trained models into machine learning tasks. It abstracts away much of the complexity involved in loading models and tokenizers, making it extremely easy to apply models like RoBERTa for tasks such as text classification.

Purpose: the pipeline is used to handle both search intent classification and page type detection. By using a pre-trained model such as RoBERTa, the pipeline can quickly classify a given text input (like a webpage title or meta description) into predefined categories (e.g., “informational,” “transactional,” “blog,” “product,” etc.).

pandas

pandas is a widely used Python library for data manipulation and analysis. In this project, it helps store and manipulate data such as scraped content, results of classification tasks, and suggestions. It is particularly useful for organizing the output of the analysis in a structured format.

Purpose: Store and manipulate the results of page scraping, classification tasks, and suggestions. It can be used to export results in an organized manner for later use or reporting.

Explanation:

The function extract_title_meta takes a single argument url, which is the web address from which the title and meta description will be extracted.

Request the Webpage Content:

response = requests.get(url, timeout=10) This line sends an HTTP GET request to the provided URL using the requests library. The timeout=10 ensures that if the website does not respond within 10 seconds, the request will be aborted.

Parse the HTML Content:

soup = BeautifulSoup(response.content, ‘html.parser’)

Once the webpage content is retrieved, BeautifulSoup is used to parse the HTML. It allows us to extract specific elements from the webpage easily.

Extract the Title:

title = soup.title.string.strip() if soup.title else ”

This checks for the <title> HTML tag and retrieves its content (the title of the webpage). The strip() function is used to remove any leading or trailing whitespace. If the title tag does not exist, it returns an empty string.

Extract the Meta Description:

meta_tag = soup.find(“meta”, attrs={“name”: “description”})

meta_desc = meta_tag[“content”].strip() if meta_tag and “content” in meta_tag.attrs else ”

The soup.find() function searches for the <meta> tag with the attribute name=”description”, which holds the meta description of the page. If the tag is found and contains the “content” attribute, it extracts and strips the content. If the meta description does not exist or is missing the “content” attribute, it returns an empty string.

Error Handling:

except Exception as e: return “”, “”

If an error occurs during the request (such as the website being down or unreachable), the function will return two empty strings for the title and meta description.

This code snippet demonstrates how to use the extract_title_meta function to retrieve the title and meta description from a specific URL.

The function extract_title_meta is called with the provided URL. It returns two values:

url_title: The title of the webpage.

url_desc: The meta description of the webpage.

Explanation

In this step, the zero-shot classification pipeline is loaded using the pre-trained RoBERTa model. This model is designed to classify texts into categories without requiring retraining or fine-tuning on specific labels. It uses the “roberta-large-mnli” model, which is a version of RoBERTa trained on the Multi-Genre Natural Language Inference (MNLI) dataset, enabling it to classify text based on given labels.

The pipeline function from the transformers library is used to load the zero-shot classification model. The model “roberta-large-mnli” is specified, which is a large version of RoBERTa optimized for zero-shot classification tasks.

The zero-shot-classification pipeline allows the model to classify a given text into any set of predefined labels, even if those labels were not part of the model’s original training data.

Purpose

RoBERTa is used here for its ability to understand and infer relationships between texts and categories based on its training on the MNLI dataset.

Explanation

The classify_intent function is responsible for determining the search intent of a given keyword or text. This function utilizes the pre-trained RoBERTa model via the zero-shot classification pipeline to classify the input keyword into one of the predefined search intent categories, such as informational, transactional, navigational, or commercial investigation.

• Setting Default Labels: The function first checks whether the candidate_labels parameter has been provided. If not, it sets a default set of labels, which are common search intents:
  • Informational: Queries looking for information.
  • Transactional: Queries with the intention to buy something.
  • Navigational: Queries aimed at finding a specific website or page.
  • Commercial Investigation: Queries comparing products or services, generally before making a purchase.
• Classifying the Keyword: The function passes the keyword (or text) and the candidate labels to the pre-trained RoBERTa zero-shot classification model via the classifier pipeline. The model returns a set of labels and their corresponding scores.
• Extracting the Highest Scoring Label: The function then extracts the label with the highest score, which represents the most probable search intent for the given keyword.

Purpose

The goal of this function is to classify a given text (like a title or meta description) into a specific search intent. Understanding the search intent helps website owners or SEO specialists optimize content to match user queries more effectively, which ultimately contributes to better rankings in search engines and improved user engagement.

The function classify_intent is called with the url_title (which contains the title of the webpage) as the input. This will classify the search intent of the webpage based on the title. The result is stored in the intent_result variable.

In this case, the model would have classified the title of the page as “navigational”, with the highest confidence, and provided scores for all the other intents as well.

Explanation

The code initializes a page_type_classifier using a zero-shot classification pipeline with the Facebook BART model, specifically the “facebook/bart-large-mnli” variant. This model is designed to classify page types without the need for additional fine-tuning on a specific dataset.

Zero-shot classification means that the model can predict the category of a text (in this case, a webpage) without having been explicitly trained on that specific category beforehand.

Purpose

This classifier is used to determine the page type of a webpage (such as “blog,” “product page,” “landing page,” etc.) based on its content. It will help to analyze whether a page fits the intended content type and guide potential optimizations.

How it works:

When this classifier is provided with a webpage’s title and description (or other relevant content), it assigns it to one of the predefined candidate page types based on the zero-shot classification task. For instance, it might classify a page as “product,” “service,” or “blog,” based on the content provided.

Once the page type is identified, it can be compared against the search intent (such as informational, transactional, etc.) to identify any mismatches or suggest possible improvements.

Explanation

The function detect_page_type is designed to classify a webpage’s type (such as “blog,” “product,” “service,” etc.) by leveraging the RoBERTa model through the zero-shot classification pipeline. This helps in analyzing the content of a page and determining whether it fits a specific category.

Purpose

The function classifies the page type of a webpage by analyzing both the title and meta description. These two elements are commonly available and give context about the content of a webpage. The function uses a pre-trained RoBERTa model to predict the most relevant page type from a set of predefined categories.

How it works

• Combining Title and Meta Description: The title and meta description of the page are combined into a single string of text. This combination provides the model with the necessary context to classify the page.
• Candidate Labels: A list of candidate page types is defined, which includes categories like “blog,” “product,” “service,” “landing page,” and more.
• Classification: The combined input text (title + meta description) is passed through the RoBERTa-based classifier to classify it into one of the page types in the candidate labels.
• Extracting Results: The classifier returns the predicted page type along with confidence scores for each candidate label. The top label (the one with the highest score) is selected as the final page type.
• Returning the Result: The function returns a dictionary containing the predicted page type and a dictionary of page type scores for all candidate labels.

This code snippet calls the detect_page_type function to classify the type of the webpage using its title and meta description.

The function is called with the page_type_classifier (the zero-shot classification pipeline with the facebook/bart-large-mnli model), url_title (the webpage title), and url_desc (the webpage meta description) as arguments.

The result returned by the detect_page_type function is stored in the page_type variable. This result contains the predicted page type and the associated confidence scores for all the candidate labels.

In this example, the predicted page type is “product”.

Explanation:

This function detect_mismatch checks whether there is a mismatch between the search intent and the predicted page type based on predefined rules.

• Mismatch Rules
  • The function defines intent_page_mapping, which maps different search intents (informational, navigational, transactional, and commercial investigation) to their expected page types.
  • For example, for an informational intent, the expected page types include “blog”, “informational-landing”, “landing page”, “faq”, and “category page”. If the predicted page type is “blog”, it is considered a match for the informational intent.
• Mapping Search Intent to Expected Page Types
  • The function retrieves the list of expected page types for the given intent from the intent_page_mapping dictionary using intent_page_mapping.get(intent, []).
  • If the intent does not exist in the mapping, it returns an empty list, which helps to handle any cases where the intent is not found.
• Mismatch Check
  • The function checks whether the predicted page_type is not in the list of expected page types for the given intent.
  • If the page_type is not in the expected list for the intent, the function returns True (indicating a mismatch). If it is in the list, it returns False (indicating no mismatch).

The detect_mismatch function is used here to determine if there is a mismatch between the intent detected for the webpage (from intent_result[‘intent’]) and the predicted page type (from page_type[‘page_type’]).

In this example, since a “product” page type is not expected for an navigational intent (according to the mismatch rules), the function will return True, indicating a mismatch.

Explanation:

The suggest_fix function works by analyzing whether the intent of a page (e.g., informational, transactional) aligns with the page type (e.g., blog, product, service). Based on this analysis, it provides actionable suggestions for improving the page’s format or structure to better meet the search intent.

• Input Parameters:
  • intent: The detected search intent, which can be one of the following:
    • Informational: Users are looking for information.
    • Navigational: Users are looking for a specific website or page.
    • Transactional: Users are looking to make a purchase or complete a transaction.
    • Commercial investigation: Users are comparing products or services before making a decision.
  • page_type: The predicted type of the webpage, such as:
    • Blog
    • Product
    • Service
    • Landing page
    • Home page
    • Category page
    • detected_mismatch: A flag indicating whether there is a mismatch between the intent and page type (True for mismatch, False for no mismatch).
• Output:
  • If the intent and page type match, the function returns “No change needed”.
  • If there is a mismatch, the function provides a suggestion to better align the page with the search intent. For example:
    • Informational Intent: Suggests converting the page to a blog or FAQ-style page.
    • Navigational Intent: Suggests making the page a service or company overview page.
    • Transactional Intent: Suggests turning the page into a product or service landing page with Calls to Action (CTAs).
    • Commercial Investigation Intent: Suggests structuring the page as a comparison or product review.

This function ensures that webpages are structured to effectively meet user expectations and improve their visibility and relevance in search results.

Purpose:

The suggest_fix function aims to offer actionable recommendations when there is a mismatch between a webpage’s search intent and its identified page type. The function is particularly useful for SEO optimization, guiding content creators or website owners to improve the relevance of their pages to the specific intent they aim to address. The goal is to ensure that the content aligns with user expectations, ultimately improving user experience and search engine rankings.

Explanation:

The output of the suggest_fix function is a suggestion aimed at addressing the mismatch between the search intent and the page type.

In this example case:

• Search Intent: “Navigational” – This indicates that users are likely looking for a specific website or page, which typically suggests the need for content that helps users navigate easily to relevant information.
• Page Type: “Product” – The page was identified as a product page, which may not align with a navigational search intent, as users might expect a company overview or a service-related page.

Suggested Fix:

The output suggestion, “Consider making this a service or company overview page,” helps resolve this mismatch by guiding the content creator to consider reworking the page structure to better suit the user’s search intent.

For navigational intents, the suggestion is to move away from focusing solely on product-related content and instead provide a more general overview of the company or service, facilitating easy navigation for users.

Explanation

When analyzing a webpage, the goal is to align the search intent with the page type to ensure that the content best matches the user’s needs.

1.      Search Intent vs. Page Type Matching:

o Every webpage serves a particular purpose, which can be categorized into one of several types: informational, transactional, navigational, or commercial investigation. These categories represent the type of content that is most likely to be useful for the user’s search query.

o The page type, such as blog, product, service, or FAQ page, is classified based on the content extracted from the page, primarily focusing on the title and meta description. The idea is to determine if the content of the page is aligned with what the user is most likely searching for.

2.      Mismatch Detection:

o A mismatch occurs when the search intent does not align with the page type. For instance, if a user’s query is informational but the page type is a product page, there’s a mismatch because the page is more focused on selling rather than providing information.

o The system checks if the detected page type is the best fit for the identified search intent, following predefined rules. For example, if the search intent is informational, but the page is categorized as a service page, this might be flagged as a mismatch.

3.      Suggestions for ImprovementL

The analysis provides several suggestions to improve the alignment between the search intent of users and the page type of the content. These suggestions help ensure that content serves the user’s needs effectively, thereby enhancing both user experience and SEO performance. Below is a general explanation of what the suggestions might look like based on the results:

o Consider Converting to a Blog or FAQ-Style Page: If a page has informational intent but is categorized under a different page type (such as service or product pages), the recommendation might be to consider transforming the page into a blog or FAQ style page. This is because these page types are more suited for providing valuable, detailed information. For instance, if a page focused on advanced SEO services is more suited for users looking for SEO advice rather than purchasing services, it would be beneficial to convert the page into a blog or FAQ. These content types are designed to educate and inform, making them a better fit for such intent.

o Consider Making this a Service, Contact, or Company Overview Page: If the search intent is navigational (for example, users are looking for contact information, company details, or company-related pages) but the page type is something like a product page or pricing page, the suggestion would be to consider restructuring the content to be more in line with what users are searching for. For example, if a page that users are visiting to find SEO pricing is categorized as a product page, it would make sense to reframe the page into a service, contact, or company overview page to better align with the navigational intent.

o Consider Structuring the Page as a Comparison or Product Review: If the search intent is commercial investigation (such as when users are comparing products or researching before making a purchase), but the page type is not suitable for this, such as a landing page, it is recommended to consider restructuring the page into a comparison page or product review. This would provide users with more in-depth comparisons or reviews to help them make informed decisions. For instance, if users are looking to compare different SEO service providers but the page is simply a landing page focused on one particular service, converting it into a comparison page would be a better fit for commercial investigation intent.

o Consider Aligning Page Format with Search Intent: In cases where no clear mismatch is identified, the general suggestion is to ensure that the page type and search intent are in harmony. If a page type does not align with the search intent, adjusting the page format to better meet user expectations can significantly improve user engagement and overall effectiveness. For example, if a home page with transactional intent (users looking to make a purchase or engage with a service) exists, it may be beneficial to create a more direct, conversion-oriented page with clear calls to action, similar to a product landing page or service page.

o No Changes Needed: If the search intent matches the page type, it indicates that the current content is well-optimized for its intended purpose. For example, if a page has informational intent and is categorized as a blog or FAQ page, it’s already in good shape. In this case, the system would not recommend any changes. This alignment suggests that the page is already effectively serving its users’ needs.

By addressing these suggestions, website owners can refine their pages to better match user expectations and improve overall SEO performance. Aligning content with user intent not only improves the site’s relevance but can also increase its chances of ranking higher in search results, ultimately driving more targeted traffic and improving conversion rates.

Understanding the Importance of Search Intent and Page Type in SEO Optimization

In the world of SEO, aligning the search intent of users with the type of page they land on is crucial for improving user engagement and enhancing search rankings. Every time a user searches online, they have a specific intent behind their query. This intent can be broadly categorized into four types: informational, navigational, transactional, and commercial investigation. Each type of search intent has different expectations, and understanding these expectations is the key to optimizing webpages effectively.

Why Does It Matter to Align Search Intent with Page Type?

When a webpage matches the user’s search intent, it leads to a smoother user experience and improves the likelihood that users will stay longer on the page and interact with the content. On the other hand, when a mismatch occurs—such as when an informational search intent leads to a transactional page—users may feel frustrated or confused and leave the site quickly, leading to a higher bounce rate.

For example:

A navigational intent should lead to a home page or about page, not a product page.

A transactional intent should lead to a product page or landing page, not an informational blog.

When the search intent matches the page type, users are more likely to find what they are looking for, engage with the content, and take desired actions like signing up for a newsletter, making a purchase, or exploring more pages on the website.

Addressing Mismatches Between Search Intent and Page Type

If a mismatch is detected between search intent and page type, several actions can be taken:

Informational Intent: If a user searching for information lands on a product page, consider converting that page into a more informative blog post or an FAQ-style page.

Navigational Intent: If users are searching for a specific company or service page but land on a product page, consider making that page a more general overview or service page to improve navigability.

Transactional Intent: If users searching to make a purchase land on an informational page, consider restructuring the page as a product or service landing page with clear calls to action.

By optimizing the structure and content of a webpage based on search intent, website owners can provide better experiences for users, reduce bounce rates, and increase the chances of conversions.

Improving SEO Performance

Pages that align well with search intent are more likely to rank higher in search engine results. This is because search engines like Google prioritize pages that provide the most relevant and helpful content based on a user’s query. By ensuring that each page type corresponds with the expected search intent, websites are more likely to appear in relevant search results, driving more traffic to the site.

In addition to improved rankings, aligning search intent with the right page type helps guide users through their journey on the website, making it easier for them to find the information they need, take desired actions, and engage with the content. This not only helps with SEO but also enhances the overall user experience and increases the chances of achieving business goals.

What Should You Do?

Reviewing the Output: Examine the search intent and page type classifications for each page. Identifying mismatches between the intended content and its classification is key to improving SEO. If an informational page is classified as a product or service page, it indicates a need for adjustment to align with user expectations.

Making Changes Based on Suggestions: The project will provide suggestions to address mismatches, such as converting an informational page to a blog or FAQ-style page, or adding clear calls to action for transactional pages. You should prioritize these suggestions based on business goals and the importance of the page.

Implementing Updates: Once adjustments are prioritized, You should update page titles, meta descriptions, and content structure. For example, converting a page to a blog may involve rewriting the content in a more casual, informative style with internal links to related content.

Final Thoughts

This project provides valuable insights into optimizing website content for better alignment with search intent and page types. By leveraging advanced classification techniques, it helps identify mismatches between content and user expectations, offering actionable recommendations to enhance SEO performance. The suggestions provided can help website owners refine their content, improve user engagement, and ultimately boost search engine rankings.

Implementing the recommended changes based on the analysis will allow websites to better meet user needs, increase relevancy, and enhance their overall digital presence. SEO optimization is an ongoing process, and continuously refining website content in line with search intent and user expectations will contribute to sustained success in search results. Regular monitoring and updates will help keep content fresh and competitive in the ever-evolving digital landscape.

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Tuhin Banik

Thatware | Founder & CEO

Tuhin is recognized across the globe for his vision to revolutionize digital transformation industry with the help of cutting-edge technology. He won bronze for India at the Stevie Awards USA as well as winning the India Business Awards, India Technology Award, Top 100 influential tech leaders from Analytics Insights, Clutch Global Front runner in digital marketing, founder of the fastest growing company in Asia by The CEO Magazine and is a TEDx speaker and BrightonSEO speaker.

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