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Time Business News
2 days ago
- Business
- Time Business News
10 Advanced SEO Techniques Only Experts Are Using
It's no longer enough to just know SEO—you have to master it. In 2025, search engine optimization is evolving faster than ever, and if you're still relying on the basics, you're already behind. That's why only a few experts are tapping into the full potential of Advanced SEO Techniques—methods that go beyond keyword stuffing and backlinks to deliver long-term, scalable results. If you're serious about ranking higher, gaining authority, and truly understanding how to future-proof your digital presence, this deep dive into the strategies elite professionals are quietly using will give you a serious edge. From AI-driven optimization to semantic search mastery, here's what the top 1% of SEO specialists are doing differently. Google's shift toward Natural Language Processing (NLP) and AI-powered algorithms like BERT and MUM has dramatically changed how content is evaluated. Semantic SEO goes beyond targeting keywords—it's about creating a topic ecosystem. Experts create pillar pages that address broad search intents and support them with tightly connected cluster articles covering subtopics in depth. This structure not only improves crawlability but also signals expertise and authority on a subject, which Google rewards with higher rankings. It's how top marketers build topical authority without relying solely on backlinks. With the phasing out of third-party cookies, first-party data has become SEO gold. Experts are integrating CRM and analytics platforms to mine user behavior and develop predictive models. By mapping first-party data to specific user intents, marketers can anticipate what content will resonate with users before they even search for it. This allows for content personalization at scale, especially when aligned with automated content systems or dynamic content rendering. AI tools like ChatGPT, Jasper, and Writesonic have changed the game—but simply generating content isn't enough. Experts use AI to build the first draft and then enhance it with human expertise, storytelling, and factual verification. This hybrid approach saves time while ensuring the final product aligns with E-E-A-T principles (Experience, Expertise, Authoritativeness, Trustworthiness). Google can now detect AI-written content that lacks depth, so expert SEOs fine-tune everything for authenticity and accuracy. Structured data isn't new, but few marketers are fully exploiting its power. Experts implement advanced schema types like FAQ, HowTo, Speakable, and Author markup—not just the standard Article and Product schemas. These markups provide search engines with precise signals about content type, increasing the chance of earning rich snippets, voice search results, and visibility in Google Discover. Some SEOs even experiment with custom schemas in niche verticals to stand out. Over 60% of Google searches end without a click. That's why expert SEOs are optimizing for zero-click searches by owning position zero with concise, high-value content designed for featured snippets, People Also Ask, and Google Knowledge Panels. Instead of chasing clicks, the goal is brand visibility and authority. This involves writing precise definitions, question-based content, and structured answers that directly address the query within the first 100 words of a page. Google is no longer ranking pages—it's ranking entities. Expert SEOs are ensuring that brands, people, and products are recognized as distinct entities in the Knowledge Graph. They do this by optimizing Google Business Profiles, Wikidata entries, LinkedIn pages, and consistent schema markup across platforms. The more Google can identify your content as part of a known entity, the more trust and visibility it gets in search results. For websites targeting large sets of keywords (like ecommerce or real estate platforms), programmatic SEO is a game-changer. Experts build systems that generate hundreds or thousands of optimized pages using dynamic templates, keyword variations, and structured content blocks. This method works especially well for 'long tail' queries with high conversion potential. The key is ensuring quality and uniqueness in each output—something that's now easier with AI integration and modular CMS setups. Google's emphasis on user experience isn't going anywhere. Core Web Vitals—metrics like Largest Contentful Paint, First Input Delay, and Cumulative Layout Shift—are baked into the ranking algorithm. Expert SEOs go beyond just hitting these numbers; they redesign site architecture, eliminate render-blocking resources, and apply lazy loading techniques. This focus on seamless UX reduces bounce rates and improves rankings on both mobile and desktop. Modern SEO isn't just reactive—it's predictive. Experts analyze trending topics, seasonal keywords, and historical search data to publish content before demand peaks. They also track the appearance of new SERP features like AI Overviews, Shopping Graph integrations, or featured videos. By optimizing for emerging formats early, they gain competitive advantages and secure valuable real estate before the competition catches up. One of the most underrated Advanced SEO Techniques is combating content decay—when once-successful articles lose rankings due to freshness or relevance issues. Experts continuously audit their top-performing pages and update them with current stats, new visuals, and improved internal linking. They also rewrite outdated information, re-optimize headers, and add multimedia to align with current SERP expectations. This strategy not only recovers lost traffic but also strengthens overall site authority in Google's eyes. These techniques are not theoretical—they're already being implemented by top SEO strategists, SaaS giants, and cutting-edge content teams. As Google gets smarter, your SEO must become more strategic, technical, and user-focused. Whether you're a freelance marketer or part of a high-performing growth team, integrating even a few of these approaches can radically improve your visibility. And if you're serious about staying ahead, consider enrolling in an advanced digital marketing course that goes beyond the basics and delves into real-world applications of these methods. As we head into a more AI-integrated digital landscape, success will belong to those who understand not just how to optimize for search engines, but how to predict, adapt, and lead within the ecosystem. TIME BUSINESS NEWS
Forbes
20-05-2025
- Business
- Forbes
How AI Is Changing The Way Customers Search For Businesses
Jay Sen, CEO of JV Capital & HireCoder AI– Expert in AI, tech startups, VC & recruiting. Traditional search volumes are expected to drop 25% by 2026 due to AI chatbots, according to Gartner. Meanwhile, websites already ranking high on Google are losing nearly 50% of traffic because of Google's AI Overviews. It's not just a trend but an actual shift. The way people find businesses is changing rapidly—and I believe this change necessitates that we as business leaders modify our strategies to remain visible and competitive. Traditional search engines like Google initially relied on exact-match keywords to answer user queries. But this method was generally inefficient. To improve the search quality and user experience, they started incorporating large language models (LLMs) into their algorithm. For example, in 2019, Google came up with BERT, which helps the search engine understand user queries better and offer more relevant search results. But things have completely changed now. Google is losing its market share to AI chatbots like ChatGPT. To compete, Google released their own chatbot—Gemini. These AI chatbots are really good at leveraging user data to provide tailored recommendations, which directly impact how individuals find businesses. Google also released AI Overviews for information retrieval, providing users with the answers to their queries without even clicking on any search results. All these combined have reduced the organic search traffic by up to 50% for the top three ranking websites. And these numbers could continue to increase with time. But there is still some good news for marketers and businesses: Although the organic search traffic has reduced, it has not completely vanished, and some of it is now coming in the form of referral traffic from AI Overviews and AI chatbots. Businesses need to adapt to these changes to stay competitive. One such step is changing your SEO approach to go beyond keywords and account for user intent. Since search engines can now understand user intent beyond keywords, they can provide more accurate and contextually relevant results to these voice queries. That's why it's important to optimize your webpages for long-tail conversational keywords. The next thing you can do is leverage your first-party data, such as customer reviews, behavior, preferences, past purchases, etc. Use this information to make hyper-personalized content for your audience, and implement structured data and FAQ schema into your webpages. Structured data helps search engines understand and categorize content more effectively and increases the chances of appearing in rich results. FAQ schema allows voice assistants to pull direct answers from web pages and, in my experience, can improve overall visibility in voice search queries. This personalized content, when paired with structured data markup, can not only rank you for long-tail queries to drive organic traffic but also increase your chances of being cited by AI chatbots and AI Overviews. Users don't always find your business on search engines, so it's important to be present on the platforms where your prospects can find you. These platforms analyze multiple factors before putting your business in front of your potential audience. For instance, Google Maps and Yelp rank businesses based on AI's assessment of your service quality, response times and review scores. Ecommerce platforms like Walmart and Amazon, on the other hand, leverage machine learning to predict and recommend products. An Omnisend survey of U.S. respondents shows that nearly 31% of users like recommendations made by AI, as it speeds up the shopping process. And these are just a few examples. AI is being used almost everywhere, and that's why a number of businesses are opting for a holistic marketing approach. By being digitally available on all the platforms where their audience is, leveraging reviews and engaging with users, businesses can show they're active and improve their visibility on such platforms. Business discovery goes beyond traditional text-based searches. Along with voice, image-based and multimodal searches allow users to find businesses in new ways. For example, there were already platforms like Google Lens, Pinterest Lens and Bing Visual Search that could be used for image-based searches. Now, we have platforms like ChatGPT and Gemini that allow users to combine text, voice and images to refine their queries. While visual and multimodal searches are not as common today as voice and text-based searches, these are not the concepts of the far future. In my experience, people are already switching to them, and that's why optimizing for these channels can be a worthy investment for businesses. I've found that the best way to optimize for this type of search is to properly use alt-tags, descriptions, metadata and schema for your visual content. Also, make sure that your business has consistent branding across platforms to make it easy for users and search engines to recognize your brand. AI isn't changing search to create obstacles; it's refining it to be more intuitive. Instead of just matching keywords, AI is used to understand intent, predict user needs, and deliver value—sometimes before the customer even realizes they need it. This shift creates opportunities for businesses to build trust and authority across multiple discovery channels. The better AI understands your business, the more it can recognize your expertise and credibility. And the stronger this association, the more frequently AI should surface your brand when potential customers search. There's no shortcut or algorithm trick. The key is to create valuable, discoverable and AI-friendly content that genuinely helps people—because when your content is useful, AI will distribute it naturally. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Time Business News
09-05-2025
- Business
- Time Business News
The Ultimate Guide to Modern SEO Strategies: Beat the Algorithm with Smarter Tactics
Search engine algorithms are evolving faster than ever. In 2025, winning top positions in Google isn't just about keywords—it's about intent, experience, and adaptability. This guide walks you through modern SEO strategies that not only meet current best practices but also future-proof your rankings in a constantly changing landscape. SEO strategies are high-level plans that guide how you optimize your website to increase visibility in search engine results pages (SERPs). Unlike SEO tactics, which are specific techniques (like updating a meta description), strategies define your overall direction—such as focusing on topical authority or targeting high-conversion intent keywords. Think of strategy as your roadmap and tactics as the turns you take. For example: Strategy: Become the go-to source for digital marketing knowledge in your niche. Tactic: Publish three blog posts per week targeting long-tail informational keywords. By clearly distinguishing the two, you'll execute more purposefully. Today's SEO isn't just about appeasing algorithms—it's about delivering user-first experiences. Google's advancements in machine learning (e.g., BERT, MUM) mean that content relevance, quality, and engagement carry more weight than ever. Without a strong strategy, even great content can fail to perform. If you want to outrank your competitors in 2025, you need to build a holistic, adaptive strategy. Here's what should be at the core: Modern keyword research focuses less on volume and more on intent. Tools like Semrush, Ahrefs, and Google's Keyword Planner can still help you identify terms, but it's your job to determine the user's goal behind each query. Tip: Group keywords by intent—informational, navigational, transactional—and tailor your content accordingly. While optimizing titles, H1s, and meta descriptions still matters, modern on-page SEO also includes: Content layout & scannability Accessibility (alt text, ARIA tags) Engagement signals (time on page, scroll depth) Make sure every page is built for users first, bots second. Search engines won't rank what they can't crawl. Focus on: Core Web Vitals Mobile usability HTTPS security XML sitemaps and optimization Canonical tags and proper redirects A well-maintained site foundation supports every other SEO effort. Google's E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) has become a key ranking factor. Establish topical authority by: Covering your niche comprehensively Updating outdated content Adding author bios and source citations Using internal linking to signal content hierarchy Spammy links can tank your rankings. Instead: Earn links through original research, guest posts, and digital PR Use HARO to connect with journalists Leverage broken link building on relevant blogs A handful of high-quality links from authoritative sources outweigh hundreds of weak ones. Smarter Tactics to Beat the Algorithm With AI-driven updates like Google's Search Generative Experience (SGE) changing how results appear, modern SEO demands more than the basics. Below are tactical approaches that align with your strategy—and help you beat the algorithm without gaming it. AI writing tools can boost productivity, but overuse can result in robotic, generic content. Worse, poorly humanized AI content can trigger detection tools used by publishers, educators, and even Google (indirectly). Smarter tactic: Use AI for outlines, ideation, and first drafts—then rewrite with a human voice. Tools like Content at Scale, Koala Writer, or Writesonic can help, but always edit to infuse expertise and natural flow. Aim to pass both human readers and AI detectors. 'AI humanizers' like and AISEO are designed to rewrite content so it appears human. However, they often fail to mimic authentic tone, insight, and emotional resonance. Smarter tactic: Go beyond mechanical rewrites. Include first-hand experience, examples, case studies, and unique analogies. Break the AI mold by writing as if you're speaking to one person, not an audience of bots. Pro tip: Google's Helpful Content System rewards content created for users by humans—don't forget the 'experience' in E-E-A-T. Schema markup helps search engines understand your content better—and can get you featured with rich snippets, FAQs, carousels, or video thumbnails. Smarter tactic: Use JSON-LD to add structured data for: Articles FAQs Product reviews Events How-to guides Tools like Google's Rich Results Test and Validator can help ensure proper formatting. Internal links aren't just for navigation—they signal topic clusters and distribute page authority (link equity). Smarter tactic: Link to cornerstone content from supporting pages Use descriptive, keyword-rich anchor text Keep your internal links natural, not forced Maintain a flat site structure for easy crawlability Bonus: This tactic also reduces bounce rate and boosts average session duration. Google's SERP has evolved—ranking #1 doesn't guarantee top visibility. Featured snippets, People Also Ask boxes, and video carousels now dominate page one. Smarter tactic: Structure your content with clear H2s/H3s to qualify for featured snippets Add FAQ schema to target People Also Ask Embed optimized YouTube videos with relevant transcripts By aiming for these SERP features, you earn more visibility—even if your organic result is technically 'below' others. Final Thoughts: Staying Ahead of SEO in an AI-Driven Era Modern SEO isn't about gaming the algorithm — it's about working smarter. With the right strategy, quality content, and user-first mindset, you can stay ahead in search engine results pages (SERPs), even as algorithms evolve. Keep your focus on intent, experience, and adaptability. SEO success in 2025 belongs to those who create for people — not just for search engines. TIME BUSINESS NEWS
Time Business News
05-05-2025
- Business
- Time Business News
The Future of SEO: From Keywords to User Intent – 7 Shifts You Must Understand
Just a decade ago, ranking on Google was all about stuffing your content with the right keywords, getting a few backlinks, and watching the traffic roll in. Fast forward to today—and even more so into the near future—the game has changed dramatically. Search engines have become smarter, users have become more sophisticated, and the algorithms now prioritize context, relevance, and intent over simple keyword matches. If you're still treating SEO like it's 2010, you're already falling behind. This article explores the future of SEO, unpacking seven major shifts you must understand to stay ahead. Whether you're a digital marketer, business owner, or SEO professional, adapting to these changes is essential for long-term success. In the past, keywords were the cornerstone of SEO strategy. Marketers focused on exact-match phrases and often awkwardly forced them into content. But with the evolution of Google's algorithm—especially updates like Hummingbird, RankBrain, and BERT—the focus has moved from what users are typing to why they're searching in the first place. User intent is typically categorized into three types: Informational : Searching for knowledge (e.g., 'how to start a podcast') : Searching for knowledge (e.g., 'how to start a podcast') Navigational : Looking for a specific site (e.g., 'Spotify login') : Looking for a specific site (e.g., 'Spotify login') Transactional: Ready to take action (e.g., 'buy noise-canceling headphones') To stay relevant, your content must align with these intents. It's no longer enough to rank for 'best digital cameras'; your page must actually help users choose the best camera based on their needs and budget. Action Tip: Use tools like Google's 'People Also Ask,' AnswerThePublic, and SEMrush's Keyword Magic Tool to understand the why behind searches—not just the words. Google now interprets meaning, context, and relationships between words through semantic search . Instead of focusing on a single keyword, content strategies are shifting to topic clusters—an interconnected set of pages that comprehensively cover a subject. A strong SEO strategy today means creating a pillar page on a broad topic (like 'Digital Marketing') and linking it to several cluster pages that dive into related subtopics (like 'email marketing strategies,' 'PPC advertising,' or 'content marketing tools'). This approach does two things: Enhances topical authority in Google's eyes Creates a better user experience with intuitive navigation and content depth Action Tip: Audit your current content. Group related blog posts and develop a central hub page to connect them. This can significantly boost visibility across the board. With Google's increasing reliance on AI models like RankBrain and the Multitask Unified Model (MUM), traditional ranking factors are now intertwined with machine learning capabilities. These models understand context, user behavior, search history, and even cultural nuances to deliver more accurate results. This means your content should be not only keyword-relevant but deeply useful and accessible in a variety of formats—text, video, audio, infographics, and more. Action Tip: Start creating diverse content formats. For instance, a blog post on SEO trends can include a short explainer video, an infographic, and a downloadable checklist. According to Statista, over 50% of online searches are now voice-based, and that number continues to grow. Voice queries tend to be longer and more conversational, such as: 'What's the best running shoe for flat feet under $100?' This shift requires a more natural language style in content and the incorporation of question-based headers and long-tail keywords. Action Tip: Use schema markup and answer key questions directly in your content. Optimizing for featured snippets is crucial, as these are often read aloud in voice search results. Google has fully adopted mobile-first indexing, meaning it uses the mobile version of your site to determine rankings. Additionally, Core Web Vitals—metrics that measure loading performance, interactivity, and visual stability—are officially ranking factors. A great blog post is useless if your site takes 10 seconds to load or is unreadable on mobile. Action Tip: Run your site through Google's PageSpeed Insights and Lighthouse reports. Focus on improving metrics like Largest Contentful Paint (LCP), First Input Delay (FID), and Cumulative Layout Shift (CLS). E-A-T stands for Expertise, Authoritativeness, and Trustworthiness. Google uses these signals to assess the credibility of content, particularly in YMYL (Your Money or Your Life) niches like health, finance, or legal advice. If your website deals with these topics, it's essential to highlight author credentials, cite credible sources, and maintain up-to-date, accurate content. Action Tip: Add author bios, link to authoritative references, and secure your site with HTTPS. Encourage genuine reviews and third-party mentions to build trust. As SEO rapidly evolves, staying educated is not optional—it's strategic. Enrolling in an Advanced Digital Marketing Course can be a game-changer, especially when you're trying to keep pace with the constant algorithmic changes, new tools, and shifting user behaviors. These courses often cover not just SEO fundamentals but also AI-driven marketing, voice search optimization, and omni-channel content strategies—making them indispensable for modern marketers. Action Tip: Look for courses that are frequently updated, taught by industry leaders, and include hands-on assignments using real-world tools like Google Search Console, SEMrush, or Ahrefs. The future of SEO is no longer about chasing algorithms—it's about serving users. Google's algorithm updates over the last few years have all pushed toward one central truth: relevance, usefulness, and trust beat keyword density every time. If you're not already shifting your content strategy from keyword-centric to intent-centric , now is the time. Focus on delivering true value, addressing user needs, and building authority—and you'll not only rank higher but also build lasting relationships with your audience. And remember, search engines will keep evolving. What won yesterday won't necessarily win tomorrow. But by staying agile, well-informed, and user-focused, you can future-proof your SEO efforts for the long haul. TIME BUSINESS NEWS
Yahoo
03-04-2025
- Science
- Yahoo
The Definition of Transfer learning
Credit - Getty Images This article is published by a partner of TIME. Transfer learning is a machine learning technique that allows a model trained on one task to be repurposed or fine-tuned for a related task, drastically reducing the amount of data and computational resources needed. This method leverages pre-trained models on large datasets to perform well in new, often smaller, domains with limited labeled data. It has become increasingly popular in fields such as natural language processing, computer vision, and speech recognition, where vast amounts of data and time are typically required for training models from scratch. Pre-trained models: In transfer learning, models are initially trained on large datasets, often unrelated to the target task. For example, models like BERT and GPT-4o in natural language processing are pre-trained on diverse text information. Fine-tuning: After training on a large dataset, the model is fine-tuned on a smaller, domain-specific dataset. This involves adjusting the weights of the neural network to optimize performance for the new task. Feature extraction: One key characteristic is that lower layers of a neural network trained on a large dataset capture general features, while higher layers are fine-tuned to specific features related to the target task. Domain adaptation: Transfer learning allows models to adapt to tasks in a different but related domain. For example, a model trained on general images can be fine-tuned to identify specific objects like medical images or satellite imagery. Image classification: A model trained on a large image dataset such as ImageNet can be repurposed for a new, smaller dataset. Natural Language Processing (NLP): In NLP, large models like GPT-4o and BERT are trained on billions of words from the internet. These pre-trained models can then be fine-tuned for specific tasks such as sentiment analysis, question-answering, or text summarization with a much smaller amount of task-specific data. Speech recognition: A speech recognition system trained on a broad dataset can be fine-tuned for recognizing specific accents or dialects in different languages. For example, a general English speech recognition system could be adapted to recognize Australian English or Indian English with limited labeled data. Reduced training time: Since the model has already learned general features from the pre-training phase, the training process for a new task is much faster, often requiring fewer resources and less time. Less data required: Transfer learning allows models to achieve high performance even with a limited amount of labeled data, making it particularly useful in situations where data collection is expensive or time-consuming. Better performance with small datasets: Transfer learning often results in better performance on smaller datasets than training a model from scratch, because the model has already learned a robust representation from the large dataset. Cross-domain applicability: It enables the use of knowledge from one domain (e.g., image recognition) to be applied to another related domain (e.g., medical imaging), enabling a wider range of applications for pre-trained models. Task similarity requirement: Transfer learning works best when the source task (the one used to pre-train the model) is similar to the target task. If the two tasks are very different, transfer learning may not be effective or may even degrade performance. Overfitting risk: When fine-tuning a model on a small dataset, there is a risk of overfitting, where the model becomes too specialized on the limited new data and fails to generalize well to unseen examples. Computational resource requirements for pre-training: Although transfer learning reduces the resources needed for fine-tuning, pre-training large models on vast datasets is still computationally expensive and often requires high-performance hardware such as GPUs or TPUs. Knowledge transfer limitations: Not all knowledge learned from one domain can be transferred effectively to another. For instance, a model trained on natural images may not transfer well to more specialized areas, like recognizing satellite images, where features are quite different. Transfer learning is a powerful technique in machine learning, allowing models to adapt to new tasks efficiently by leveraging pre-trained knowledge. This approach not only reduces the need for large amounts of labeled data but also accelerates the development of AI systems across various domains, from healthcare to NLP. However, it does have its limitations, especially when the source and target tasks are not closely related or when the pre-training phase is highly resource-intensive. Despite these challenges, transfer learning remains one of the most effective methods for improving model performance and accelerating AI research in numerous fields. Copyright © by All Rights Reserved Contact us at letters@
