Latest news with #NLP-powered
Time Business News
4 days ago
- Business
- Time Business News
Precision-Targeted Ads: How Marpany's Artificial Intelligence Skyrockets Advertising ROI
AI advertising replaces human intuition with machine intelligence – using algorithms to automate targeting, bidding, and creative optimisation at unprecedented speed. 2010s Era: Teams manually adjusted bids based on weekly reports. AI Transformation: Self-learning systems process 1 M+ data points/hour. Key Shift: Reactive → Proactive campaign management. ML algorithms cluster audiences by behavior patterns (e.g., 'frequent travelers who click luxury ads post-8PM'). Marpany's models identify 47% more high-intent segments than rule-based tools. NLP analyzes emotional resonance in messaging: Scans 10,000+ social comments to refine CTAs. Generates context-aware variants for A/B testing. Real-World Impact: 'Limited Offer!' → 'Only 3 Left at This Price!' (22% conversion lift). Real-time location data Past purchase behavior Device usage patterns Result: Travel client saw 35% conversion lift with weather-triggered ad variants. Automatically generates 1,000+ ad combinations from core assets. Tests elements like: Background colors (Blue converts +9% in finance). CTA button placement. Emotional vs. rational messaging. Metric Manual Campaign AI-Driven Campaign CPC $2.17 $1.38 (-36%) Impression Win 41% 68% ROAS 2.8x 4.5x Median 29% higher conversion rates per IBM Watson. 22% lower customer acquisition costs. Automated budget reallocation to top channels. AI connects touchpoints across devices using: Cross-channel attribution modeling. Predictive path analysis. Churn risk scoring. Marpany's AI framework features: Anonymous data aggregation. Consent management integration. Automated DSAR processing. Our 3-layer approach: Diversity audits of training data. Continuous fairness scoring. Human oversight protocols. Future Trends: What's Next for AI in Ads? AI will forecast demand spikes 14 days out using: Social sentiment trends. Economic indicators. Competitor spend patterns. By 2026, 40% of ads will optimize for: Voice-activated queries. Image-based product discovery. AR try-on experiences. Challenge: Luxury travel brand struggling with generic ads (1.2% CTR). Marpany Solution: Implemented NLP-powered copy generator. Deployed ML-based audience clusters. Activated real-time DCO via our AI analytics platform. Results: 30% CTR increase in 8 weeks. 19% lower CPA. $2.7M incremental revenue. CTA: 'Ready to transform your advertising with AI? Schedule a free audit with Marpany's specialists and discover how our ADAPT™ platform delivers measurable ROI within 45 days. TIME BUSINESS NEWS
Scoop
19-05-2025
- Business
- Scoop
How AI Is Changing Web Scraping: From Coding To Natural Language
I still remember the first time I tried to scrape data from a website. It was a mess of Python scripts, tangled CSS selectors, and a lot of trial and error. I spent more time fixing broken code than actually getting the data I needed. Fast forward to today, and the landscape looks completely different. AI web scraping, powered by natural language processing (NLP), is turning what used to be a developer's playground into a tool anyone can use—no coding required. The numbers back it up: the web scraping software market hit $1.01billion in 2024 and is on track to more than double by 2032. AI-driven web scraping is leading the charge, with a projected 17.8% annual growth rate and businesses everywhere—from e-commerce to finance—jumping on board for smarter, faster data extraction. So, what's really happening behind the scenes, and why is AI web scraping suddenly the hottest ticket in automation? Let's dig in. Meet the New Era: AI Web Scraping and Natural Language Processing AI web scraping is exactly what it sounds like: using artificial intelligence to automate the process of pulling data from websites. But it's not just about speed. The real revolution is in how these tools 'think.' Instead of relying on brittle scripts that break every time a website changes its layout, AI web scrapers actually 'read' web pages more like a human would. They use machine learning and computer vision to understand the structure and context, so they can adapt on the fly. Natural language processing (NLP) is the secret sauce that makes this accessible to everyone. Instead of writing code or fiddling with CSS selectors, you just tell the AI what you want in plain English. For example, you might say, 'Get all the product prices from this page,' and the AI figures out the rest. It's like having a digital intern who actually listens (and doesn't ask for coffee breaks). Why does this matter? Because it breaks down the wall between technical and non-technical users. Suddenly, sales teams, marketing analysts, and operations folks can all get the web data they need—no IT ticket required. Why AI Web Scraping Matters for Business Automation Let's be real: most businesses don't care about the technical wizardry behind web scraping. They care about results—faster, more accurate data, with less hassle. That's where AI web scraping shines. Time Savings: Companies using AI-driven scraping tools report 30–40% time savings on data extraction tasks compared to old-school methods. That's time your team can spend on analysis, not copy-pasting. Efficiency: AI scrapers can handle everything from text and images to PDFs and even dynamic content, all in one go. Some platforms boast up to 99.5% accuracy, even on complex sites. Accessibility: No more waiting on IT. With NLP-powered tools, anyone can set up a data extraction workflow in minutes. In fact, when one platform launched a drag-and-drop AI interface, they saw a 200% jump in use by non-technical users. The bottom line? Automation isn't just for coders anymore. It's for anyone who needs data to do their job better. From Coding to Conversation: How Natural Language Processing Simplifies Web Scraping Here's where things get fun. With NLP, web scraping becomes a conversation, not a coding project. Instead of writing a script, you just describe what you want: 'Extract all job titles, company names, and locations from this LinkedIn search.' 'Get the dimensions of each product on this Amazon page.' 'Pull all the emails and phone numbers from this directory.' The AI interprets your request, figures out what's on the page, and gets to work. It's like having a super-powered assistant who actually understands you—no need to explain what a is. Comparing Traditional Coding vs. AI-Powered Natural Language Scraping Aspect Traditional (Coding-Based) Scraping AI/NLP-Powered Web Scraping Setup Time Days or weeks to write and debug scripts Minutes to set up with a no-code interface Required Skills Programming knowledge required Basic computer skills and plain English Learning Curve Steep Shallow—point, click, and describe Adaptability Breaks when sites change AI adapts automatically Dynamic Content Needs extra coding Built-in handling Maintenance High—constant updates Low—self-healing scrapers Scalability Custom code for scaling Cloud-native, easy scheduling Integration Manual data exports One-click export to Sheets, Airtable, Notion, etc. The difference is night and day. With AI and NLP, web scraping goes from a specialized skill to something anyone can do over lunch. How AI Web Scraping Works: A Step-by-Step Overview Curious what it's like to use an AI web scraper? Here's a typical workflow, using Thunderbit as an example: Open the Thunderbit Chrome Extension. Navigate to the Website: Go to the page you want to scrape. Describe Your Data Needs: Click 'AI Suggest Fields' and let the AI recommend what to extract—or type your own instructions in plain English. Review and Adjust: Tweak the suggested fields if needed (e.g., add 'price' or 'rating' columns). Start Scraping: Click 'Scrape.' The AI does the heavy lifting, even visiting subpages if you want. Export Your Data: Download as CSV, or send it straight to Google Sheets, Airtable, or Notion. Key Features That Set AI Web Scraping Apart Natural Language Commands: Just describe what you want—no code, no selectors. 2-Click Automation: Set up and run scrapers in seconds. Automatic Subpage Navigation: Gather details from linked pages (think product listings or profiles). Pagination Handling: Scrape across multiple pages or infinite scroll with zero setup. Pre-Built Templates: One-click scrapers for popular sites like Amazon, Zillow, and more. Data Transformation: Summarize, categorize, or format data as it's scraped. Flexible Export: Push data to your favorite tools, or just copy-paste. Scheduling and Monitoring: Set up recurring scrapes and get notified of changes. You can see why users rave about the 'ridiculously easy' setup and the time savings. (And yes, I've had my fair share of 'why didn't this exist sooner?' moments.) The Role of Automation: Scaling Data Extraction with AI One of the coolest things about AI web scraping is how it scales. Need to scrape 10,000 product pages? No problem. Want to monitor a competitor's price changes every hour? Just set it and forget it. AI scrapers run in the cloud, handle proxies and CAPTCHAs automatically, and can parallelize jobs for speed. Scheduled scraping means your data is always up-to-date, feeding directly into your dashboards or analytics tools. It's like having a team of digital interns working around the clock—minus the HR paperwork. And the impact is real: businesses are doubling sales, improving pricing strategies, and making faster decisions thanks to real-time, automated web data. Overcoming Challenges: How AI and NLP Address Common Web Scraping Pain Points Let's face it, traditional web scraping is fragile. Sites change their layouts, data comes in weird formats, and anti-bot measures can stop you in your tracks. Here's how AI and NLP tackle these headaches: Site Changes: AI scrapers use pattern recognition and context, so they adapt when a site's HTML shifts. No more endless script updates. Messy Data: NLP models can clean, format, and even summarize data as it's scraped. Want all prices in USD? Done. Need to extract sentiment from reviews? Easy. Dynamic Content: AI tools handle JavaScript-heavy sites, infinite scroll, and interactive elements out of the box. Anti-Blocking: Built-in proxy rotation, CAPTCHA solving, and error handling keep your scrapers running smoothly. User Error: If your instructions aren't clear, many AI scrapers will ask follow-up questions or highlight what they're about to grab—so you always know what you're getting. The result? Web scraping that's more robust, less stressful, and way less likely to break at 2am on a Friday. The Future of AI Web Scraping: Towards More Human-Centric Automation Looking ahead, the future is all about making web scraping even more natural and proactive. Imagine telling your AI assistant, 'Find all the stores in California selling Product X and give me their prices,' and having the answer ready before your coffee cools off. We're already seeing scrapers that anticipate data needs, handle richer media (like images and video), and integrate directly with analytics platforms. Domain-specific AI agents—think legal, real estate, or healthcare—are on the rise, offering even deeper insights with less effort. And as regulations and ethics become more important, expect smarter compliance features: scrapers that respect privacy, flag sensitive info, and keep your data collection above board. The big picture? Web scraping is becoming as easy and common as using a spreadsheet. The barriers are falling, and the only limit is your curiosity. Conclusion: Unlocking Data for Everyone with AI Web Scraping AI web scraping and natural language processing are rewriting the rules of data automation. No more coding headaches, no more waiting on IT—just describe what you need, and let the AI do the rest. It's faster, smarter, and open to everyone, not just the folks with a computer science degree. So move Faster with the Right No-Code Tools, if you're still building everything from scratch, you're missing out. No-code AI tools aren't just a shortcut—they're a way to experiment, iterate, and solve real problems without getting bogged down in infrastructure. The future belongs to those who move fast and let automation handle the grunt work. So go ahead—let AI do the heavy lifting, and spend your time on what really matters: turning data into action. BUSINESS, SCIENCE & TECH
