Posts

Grocery Price Comparison App Data

  Introduction Consumers increasingly rely on price comparison applications to identify the best grocery deals before making purchasing decisions. To remain competitive, grocery brands need real-time visibility into product prices, promotions, discounts, and inventory across multiple retailers. Manual monitoring is time-consuming, inconsistent, and unable to keep pace with rapidly changing market conditions. A centralized pricing intelligence platform enables brands to react quickly and improve customer value. Actowiz Solutions partnered with a leading grocery brand to build a scalable solution powered by Grocery Price Comparison App Data and a robust Web scraping API . The platform automatically collected product prices, promotional offers, availability, and category information from multiple grocery retailers in near real time. By transforming millions of pricing records into structured datasets, the client gained actionable market intelligence, optimized dynamic pricing strategi...
Post a Comment
Read more

Grocery Price Data Feed for a Comparison App

Image
  Industry: Consumer Tech (Price Comparison / Savings App) Region: United Kingdom (model replicated for US and South Africa clients) Retailers covered: Tesco, Sainsbury's, Asda, Morrisons, Aldi, Lidl, Waitrose, M&S, Ocado Services used: UK Grocery Data API, Product Matching, Real-Time Price Scraping The Client A pre-launch UK startup building a basket-comparison app: a shopper enters their weekly list, and the app shows the total cost of that basket at every major supermarket nearby, including current promotions and loyalty prices. The Challenge The product's entire promise rests on one thing — accurate, current prices. That created a brutal data problem for a small team: No tracked SKU list. Unlike a brand monitoring its own products, a comparison app needs broad coverage — initially 25,000 everyday grocery SKUs per retailer across nine retailers. Prices change constantly. UK supermarkets reprice and rotate promotions weekly (and sometimes daily), including loyalty-c...
Post a Comment
Read more

Why 70% of AI Models Run on Scraped Web Data — 2026

  Introduction Seventy percent of generative AI models are trained primarily on scraped web data (Actowiz Solutions Industry Report, 2026) — a statistic now cited across the AI-data industry. This deep dive unpacks what's behind the number: why web data became the foundation, why that foundation is under pressure, and where training data goes next. Why Web Data Won AI models learn from examples; the internet is the largest, most diverse corpus of human-generated text, images, code, and structured data in existence — which is why web scraping became the primary method for assembling training datasets (Tendem, 2026). Every major LLM was built on datasets assembled by crawling billions of pages. The economics were unbeatable: no licensing negotiation scales to web breadth, and no curated alternative matches its diversity. The Number Behind the Number What "70% trained primarily on scraped data" looks like operationally: 65% of companies now feed scraped data into AI systems ...
Post a Comment
Read more

Wayfair SKU-Level Product Data Collection for Retail Intelligence

Image
  At a Glance Industry Home & furniture Market United States Source Wayfair Scope ~114 defined SKUs, tracked continuously Focus Price, variants, availability, ratings, product content Delivery Scheduled structured feed (CSV / JSON / API) Who is this for? (ICP) Best fit: A home, furniture, or décor brand, seller, or category team selling on (or competing against) Wayfair, who needs precise tracking of a specific SKU list rather than a whole-category crawl. Core pain points this solves: Furniture SKUs carry many variants (size, color, material), each priced differently — manual tracking is impossible. Price and availability change without notice; a competitor undercut goes unseen for weeks. The team needs their SKU list watched, not a generic category dump. Success looks like: A clean, scheduled feed covering every tracked SKU and variant, so pricing, merchandising, and content decisions are made on current data. What was the challenge? The client cared about a specific, finite S...
Post a Comment
Read more