You can check the fields description in the documentation: current Keyword database: https://docs.dataforseo.com/v3/databases/google/keywords/?bash; Historical Keyword database: https://docs.dataforseo.com/v3/databases/google/history/keywords/?bash. You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

You can check the fields description in the documentation: regular SERP: https://docs.dataforseo.com/v3/databases/google/serp_regular/?bash; Advanced SERP: https://docs.dataforseo.com/v3/databases/google/serp_advanced/?bash; Historical SERP: https://docs.dataforseo.com/v3/databases/google/history/serp_advanced/?bash You don’t have to download fresh data dumps in JSON or CSV – we can deliver data straight to your storage or database. We send terrabytes of data to dozens of customers every month using Amazon S3, Google Cloud Storage, Microsoft Azure Blob, Eleasticsearch, and Google Big Query. Let us know if you’d like to get your data to any other storage or database.

It is a dataset with various features that affect the ranking of selected words in the search engine.

Data of investigation published in the article: "Using Machine Learning for Web Page Classification in Search Engine Optimization"

Abstract of the article:

This paper presents a novel approach of using machine learning algorithms based on experts’ knowledge to classify web pages into three predefined classes according to the degree of content adjustment to the search engine optimization (SEO) recommendations. In this study, classifiers were built and trained to classify an unknown sample (web page) into one of the three predefined classes and to identify important factors that affect the degree of page adjustment. The data in the training set are manually labeled by domain experts. The experimental results show that machine learning can be used for predicting the degree of adjustment of web pages to the SEO recommendations—classifier accuracy ranges from 54.59% to 69.67%, which is higher than the baseline accuracy of classification of samples in the majority class (48.83%). Practical significance of the proposed approach is in providing the core for building software agents and expert systems to automatically detect web pages, or parts of web pages, that need improvement to comply with the SEO guidelines and, therefore, potentially gain higher rankings by search engines. Also, the results of this study contribute to the field of detecting optimal values of ranking factors that search engines use to rank web pages. Experiments in this paper suggest that important factors to be taken into consideration when preparing a web page are page title, meta description, H1 tag (heading), and body text—which is aligned with the findings of previous research. Another result of this research is a new data set of manually labeled web pages that can be used in further research.

This is the feedback from the participants at a community meeting in May 2018 at the Central LIbrary for the Special Events Ordinance

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset contains a comprehensive guide on integrating schema markup and meta descriptions to create rich SERP features that improve click-through rates and search visibility. The content covers best practices, implementation steps, and examples for leveraging structured data and optimized meta descriptions to boost search performance.

The fields description may be found here: https://docs.dataforseo.com/v3/databases/backlink_summary/?bash

DataForSEO Backlink Summary Database encompasses millions of domains enriched with backlink data and other related metrics. You will get a comprehensive overview of a domain’s backlink profile, including the number of inbound links, referring domains and referring pages, new & lost backlinks and referring domains, domain rank, backlink spam score, and more.

This database is available in both JSON and CSV formats.

This dataset provides a comprehensive guide to dental SEO strategies and tactics for dental practices in Colorado. It covers key topics such as local SEO, Google Business Profile optimization, content creation, and patient acquisition through online marketing. The dataset includes step-by-step instructions, industry benchmarks, and real-world examples to help Colorado dentists improve their online visibility and attract more patients.

Netlas.io is a set of internet intelligence apps that provide accurate technical information on IP addresses, domain names, websites, web applications, IoT devices, and other online assets.

Netlas.io scans every IPv4 address and every known domain name utilizing such protocols as HTTP, FTP, SMTP, POP3, IMAP, SMB/CIFS, SSH, Telnet, SQL and others. Collected data is enriched with additional info and available in Netlas.io Search Engine. Some parts of Netlas.io database is available as downloadable datasets.

Netlas.io accumulates domain names to make internet scan coverage as wide as possible. Domain names are collected from ICANN Centralized Zone Data Service, SSL Certificates, 301 & 302 HTTP redirects (while scanning) and other sources.

This dataset contains domains and subdomains (all gTLD and ccTLD), that have at least one associated DNS registry entry (A, MX, NS, CNAME and TXT records).

SEO Brief Generation AI Market Outlook

According to our latest research, the global SEO Brief Generation AI market size was valued at USD 1.24 billion in 2024, with a robust compound annual growth rate (CAGR) of 25.3% anticipated from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 9.25 billion. This impressive growth is primarily driven by the increasing adoption of AI-powered tools within the digital marketing ecosystem, the need for scalable and data-driven SEO solutions, and the relentless pursuit of competitive advantages in online visibility and content performance.

The growth trajectory of the SEO Brief Generation AI market is underpinned by the rapid digital transformation initiatives undertaken by businesses of all sizes. Organizations are increasingly relying on advanced AI algorithms to streamline their SEO processes, reduce manual labor, and enhance the quality of their content briefs. This shift is further bolstered by the exponential rise in online content consumption, which necessitates more precise and strategic SEO planning. As competition intensifies in the digital landscape, AI-powered SEO brief generation tools are enabling marketers to quickly analyze vast datasets, identify high-impact keywords, and generate actionable recommendations, thereby accelerating their go-to-market strategies and improving ROI.

Another key growth factor is the evolution of AI technologies, particularly natural language processing (NLP) and machine learning (ML). These technologies have significantly improved the ability of SEO brief generation tools to interpret search intent, analyze competitor strategies, and tailor recommendations to specific business objectives. The integration of AI with other digital marketing platforms, such as content management systems and analytics suites, has further enhanced the value proposition of these solutions. Enterprises are now able to leverage AI-driven insights for holistic SEO strategies that encompass keyword research, on-page optimization, content creation, and performance tracking, all within a unified workflow.

Additionally, the democratization of AI-powered SEO tools is making them accessible to small and medium enterprises (SMEs), not just large corporations. Cloud-based deployment models and flexible subscription pricing are lowering barriers to entry, enabling a wider range of businesses to benefit from sophisticated SEO automation. This is particularly important in the context of global e-commerce expansion and the proliferation of digital-first brands, where the ability to rapidly generate and execute effective SEO briefs can be a critical differentiator. As a result, the SEO Brief Generation AI market is witnessing widespread adoption across diverse industry verticals, including e-commerce, media and publishing, IT and telecommunications, and digital marketing agencies.

Regionally, North America continues to dominate the SEO Brief Generation AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced digital marketing technologies, a mature e-commerce ecosystem, and the presence of leading AI solution providers are key factors contributing to North America's leadership position. Meanwhile, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increasing internet penetration, and a burgeoning startup ecosystem. Europe, with its strong regulatory framework and emphasis on data privacy, remains a critical market for AI-driven SEO solutions, especially among enterprises seeking GDPR-compliant technologies.

Component Analysis

The SEO Brief Generation AI market by component is segmented into Software and Services. The software segment dominates the market, accounting for a significant portion of the overall revenue in 2024. This dominance is attributed to the increasing demand for AI-powered platforms that automate and optimize the SEO brief creatio

This public data contains information on designated model Korean restaurants in Seo-gu, Incheon Metropolitan City. This data contains information on the name, type, main menu (price), location, and number of parking spaces of model Korean restaurants in Seo-gu, Incheon Metropolitan City. This data can help citizens choose sanitary and trustworthy places to eat out, and it is expected to help revitalize the local economy by providing information on local restaurants to foreign visitors. This public data is regularly updated every year to reflect the latest information.

Technical SEO Automation AI Market Outlook

According to our latest research, the global Technical SEO Automation AI market size reached USD 1.68 billion in 2024, demonstrating robust growth driven by the increasing digitalization of businesses worldwide. The market is projected to expand at a CAGR of 21.2% from 2025 to 2033, reaching an estimated value of USD 10.29 billion by 2033. This remarkable growth is primarily fueled by the rising demand for scalable, intelligent SEO solutions that streamline website optimization and enhance organic search performance in an increasingly competitive digital landscape.

One of the primary growth factors propelling the Technical SEO Automation AI market is the exponential increase in online content and e-commerce activities. As businesses strive to improve their online visibility and user experience, the need for advanced technical SEO practices has become paramount. AI-powered automation tools are now indispensable for efficiently managing large-scale websites, identifying and resolving technical issues, and ensuring compliance with constantly evolving search engine algorithms. This automation not only reduces manual intervention but also delivers actionable insights, enabling organizations to stay ahead of the competition and capture a larger share of organic traffic.

Another significant driver for the market is the growing complexity and sophistication of search engine ranking factors. Search engines like Google have increasingly adopted AI and machine learning within their algorithms, making technical SEO tasks more challenging and nuanced. As a result, enterprises are turning to AI-driven SEO automation platforms to handle tasks such as crawl analysis, log file analysis, structured data optimization, and site speed enhancements at scale. These solutions are particularly valuable for organizations managing extensive digital assets and multi-regional websites, as they facilitate rapid identification of issues, prioritization of fixes, and continuous monitoring for optimal search performance.

Furthermore, the integration of technical SEO automation with broader digital marketing and analytics ecosystems has unlocked new opportunities for data-driven decision-making. By leveraging AI to analyze vast amounts of website and user data, organizations can uncover hidden patterns, predict search trends, and personalize website experiences. This holistic approach not only improves search engine rankings but also drives higher user engagement and conversion rates. The proliferation of cloud-based SEO automation platforms has further democratized access to these advanced capabilities, enabling small and medium enterprises (SMEs) to compete effectively with larger players in the digital arena.

From a regional perspective, North America currently dominates the Technical SEO Automation AI market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is a hotbed for innovation in AI-driven SEO tools, with a high concentration of technology providers and early adopters. Meanwhile, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid digital transformation across emerging economies such as India, China, and Southeast Asia. The increasing penetration of internet services, expanding e-commerce sector, and growing awareness of SEO best practices are fueling demand for automation solutions across these regions.

As the Technical SEO Automation AI market continues to evolve, a new trend is emerging in the form of Content Optimization as a Service. This innovative approach leverages AI-driven technologies to enhance the quality and relevance of digital content, ensuring it aligns with search engine algorithms and user expectations. By integrating content optimization into the broader SEO strategy, businesses can achieve a more holistic approach to digital marketing. This service not only focuses on keyword density and metadata but also emphasizes the importance of user engagement metrics and content freshness. As a result, companies can maintain a competitive edge by continuously adapting their content strategies to meet the dynamic demands of the digital landscape.

Survey of 250 businesses on how much they spend on SEO and their SEO costs

Snapshot img

Search Engine Optimization (SEO) Software Market Size 2025-2029

The search engine optimization (seo) software market size is forecast to increase by USD 40.05 billion, at a CAGR of 21.3% between 2024 and 2029.

The SEO Software Market is experiencing significant growth, driven by the increasing penetration of the Internet worldwide. The global digital transformation has led to an escalating demand for SEO solutions to optimize online presence and visibility. An additional key driver is the advent of advanced Artificial Intelligence (AI) technologies, which are revolutionizing SEO by enhancing user experience and delivering more accurate and personalized search results. However, this market is not without challenges. Data privacy concerns among end-users pose a significant obstacle, as companies must ensure they comply with stringent regulations, such as GDPR and CCPA, while maintaining effective SEO strategies.
Balancing user privacy with search engine optimization requirements is a delicate challenge that demands innovative solutions and strategic planning. Companies seeking to capitalize on market opportunities and navigate these challenges effectively must stay informed of the latest trends and best practices in SEO and data privacy regulations.

What will be the Size of the Search Engine Optimization (SEO) Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The SEO software market continues to evolve, with new tools and techniques emerging to help businesses optimize their online presence. On-page optimization techniques, such as keyword difficulty scores and content strategy tools, remain essential for improving website performance. Local SEO optimization, website crawlability issues, and indexation monitoring tools are crucial for businesses targeting local markets and ensuring their websites are easily accessible to search engines. Content optimization features, data visualization tools, and image optimization techniques enable businesses to create engaging and optimized content for their audiences. AI-powered SEO tools, structured data validation, and SERP feature analysis offer insights into search engine behavior and user intent, providing valuable data for optimization strategies.

Backlink analysis software, website speed optimization, link building strategies, and video SEO strategies are essential for building a strong online presence and increasing visibility. Technical SEO capabilities, site audit functionalities, content promotion features, competitor SEO analysis, mobile SEO performance, conversion rate optimization, semantic keyword analysis, internal linking strategy, schema markup implementation, and keyword research tools are all critical components of a comprehensive SEO strategy. According to recent industry reports, the SEO software market is expected to grow by over 15% annually, reflecting the increasing importance of digital presence for businesses across sectors. For instance, a large e-commerce company reported a 20% increase in organic traffic after implementing a comprehensive SEO strategy using a combination of these tools and techniques.

How is this Search Engine Optimization (SEO) Software Industry segmented?

The search engine optimization (seo) software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  Cloud-based
  On-premises
  Hybrid


Product Type

  Desktop user
  Mobile user


Application

  Social media marketing
  Email marketing
  Content marketing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The cloud-based segment is estimated to witness significant growth during the forecast period.

The cloud-based SEO software segment in the global market is witnessing significant growth due to the increasing preference for accessible, collaborative, and scalable solutions among professionals and teams. Cloud-based tools, such as Ahrefs, offer users the flexibility to access advanced SEO functionalities from any location with internet connectivity. This enables real-time collaboration, allowing team members to work together seamlessly on SEO projects, regardless of their physical proximity. The user experience of cloud-based SEO software is marked by its browser-based interfaces, ensuring a consistent and responsive experience across various devices. On-page optimization techniques, keyword difficulty scores, and local SEO optimization are essential features integrated into these tools.

Content str

Example input files can be downloaded from here. Note that the attached input and output example files are not real data but have the same format with them. Required input files for each step are described in instructions.Data Sets : The glioblastoma (GBM) data sets including the expression data of mRNA, protein, miRNA used in this paper were collected from TCGA. The information about GBM-related miRNAs was collected from Human miRNA & Disease Database v2.0 (HMDD v2.0). Protein-protein interaction data sets were collected from the Human Protein Reference Database (HPRD) (Prasad et al.,2009).

Business Listings Database is the source of point-of-interest data and can provide you with all the information you need to analyze how specific places are used, what kinds of audiences they attract, and how their visitor profile changes over time.

The full fields description may be found on this page: https://docs.dataforseo.com/v3/databases/business_listings/?bash

This dataset provides a comprehensive content strategy for estate planning law firms to effectively target and connect with Colorado's rapidly aging population. It covers key insights into the demographic shift, the importance of building trust through content marketing, and the specific types of content and digital channels that resonate with this audience. The dataset includes detailed recommendations and examples based on real-world experience working with law firms in Colorado.

Context

Alexa Internet rank websites primarily on tracking a sample set of Internet traffic—users of its toolbar for the Internet Explorer, Firefox and Google Chrome web browsers. The Alexa Toolbar includes a popup blocker (which stops unwanted ads), a search box, links to Amazon.com and the Alexa homepage, and the Alexa ranking of the website that the user is visiting. It also allows the user to rate the website and view links to external, relevant websites. Also, Alexa has prepared a list of information for each site for comparison and ranking with other similar sites for each site.

This dataset is a record of all information on the top websites in each category in Alexa ranking. Source: https://github.com/AshkanGoharfar/Crawler_for_alexa.com

Content

This dataset includes several site data, which were achieved from "alexa.com/siteinfo" (for example alexa.com/siteinfo/facebook.com). Data is included for the top 50 websites for every 550 categories in Alexa ranking. (The dataset was obtained for about 22000 sites.) The data also includes keyword opportunities breakdown fields, which vary between categories. As well as each site has important parameters like all_topics_top_keywords_search_traffic_parameter which represent search traffics in competitor websites to this site. For more details about each site's data, you can find the site's name and site's information in the dataset and you can search alexa.com/siteinfo/SiteName link to understand each parameter and columns in the dataset.

Acknowledgements

This dataset was collected using the selenium library and chrome web driver to crawl alexa.com data with python language.

Provider: Ashkan Goharfar, ashkan_goharfar@aut.ac.ir, Department of Computer Engineering and Information Technology, Amirkabir University of Technology

Citation Request

A. Risheh, A. Goharfar, and N. T. Javan, "Clustering Alexa Internet Data using Auto Encoder Network and Affinity Propagation," 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, 2020, pp. 437-443, doi: 10.1109/ICCKE50421.2020.9303705.

Inspiration

Possible uses for this dataset could include:

Sentiment analysis in a variety of forms. Categorizing websites based on their competitor websites, daily time on the website and Keyword opportunities.

Analyzing what factors affect on Comparison metrics search traffic, Comparison metrics data, Audience overlap sites overlap scores, top keywords share of voice, top keywords search traffic, optimization opportunities organic share of voice, Optimization opportunities search popularity, Buyer keywords organic competition, Buyer keywords Avg traffic, Easy to rank keywords search pop, Easy to rank keywords relevance to site, Keyword gaps search popularity, Keyword gaps Avg traffic and Keywords search traffic.

Training ML algorithms like RNNs to generate a probability for each site in each category to being SEO by Google.

Use NLP for columns like keyword gaps name, Easy to rank keywords name, Buyer keywords name, optimization opportunities name, Top keywords name and Audience overlap similar sites to this site.

Buy Medium Articles Dataset – 500K+ Published Articles in JSON Format

Get access to a premium Medium articles dataset containing 500,000+ curated articles with metadata including author profiles, publication dates, reading time, tags, claps, and more. Ideal for natural language processing (NLP), machine learning, content trend analysis, and AI model training.

Request here for the large dataset Medium datasets

Checkout sample dataset in CSV

Use Cases:

• Training language models (LLMs)

• Analyzing content trends and engagement

• Sentiment and text classification

• SEO research and author profiling

• Academic or commercial research

Why Choose This Dataset?

• High-volume, cleanly structured JSON

• Ideal for developers, researchers, and data scientists

• Easy integration with Python, R, SQL, and other data pipelines

• Affordable and ready-to-use

Fuel Your Photos is a company dedicated to helping photographers grow their business through search engine optimization (SEO). With years of experience in the photography space, they have distilled their own techniques into a project-based SEO course and community that provides actionable items and examples for photographers to follow. Their course and community aim to help photographers achieve consistent inquiries and revenue year after year, freeing up more time to focus on what they love - taking photos.

The company also runs a popular podcast and blog, providing news, walkthroughs, and tutorials on SEO and digital marketing specifically for photographers. With a large and active Facebook group, Fuel Your Photos has created a positive and helpful community where photographers can connect, ask questions, and share their own experiences. Whether you're just starting out or looking to take your photography business to the next level, Fuel Your Photos offers a range of resources to help you succeed.