actowiz

  Introduction India's food-delivery market runs on Swiggy and Zomato, with ONDC opening a new open-network dimension — and the data flowing through them is a real-time map of how India eats out: what restaurants charge, how delivery menus differ from dine-in, which items sell where, how fees vary by locality, and the explosion of cloud kitchens reshaping supply. For restaurant chains, cloud-kitchen operators, CPG brands, and analysts, a structured food-delivery dataset is one of the highest-signal assets in the market — but building one correctly is far harder than it looks. Step 1: Define the Unit — Restaurant, Menu, or Item? The first decision shapes everything. Restaurant-level (coverage and presence), menu-level (full menus and structure), or item-level (the dish, its price, customisations, availability). Most serious datasets are built item-level with restaurant and menu context preserved, because pricing, promotion, and demand analysis all happen at the item level. Worked ex...

  Introduction Automotive is one of the most data-rich retail categories online and one of the least systematically tracked. Vehicle listings, dealer inventory, OEM specification catalogues, tyre and parts pricing across marketplaces, and used-vehicle valuations are all publicly published and structurally extractable. This guide covers what exists across five data layers, what each is used for, and why automotive data collection is harder than general e-commerce. Why Automotive Data Is Structurally Different Most retail price monitoring compares a product to itself across sellers. Automotive rarely offers that convenience. A vehicle is not a SKU. The same model exists as dozens of variant-trim-fuel-transmission combinations, named inconsistently across dealer sites, aggregator portals and OEM catalogues. A tyre is closer to a SKU but is identified by a size code that appears in several formats. A used vehicle is genuinely unique — mileage, condition, ownership history and location ...