ZooData: commerce data and extraction APIs

Before entering a new category, an operator can use Market Research to compare demand, price levels and competition across niches. The Web page lets the operator choose a category, time period and product sample, then filter by sales signals, brand concentration and the share of new products. This helps narrow the categories worth investigating and spot markets dominated by a few brands. These statistics describe product samples, rather than a complete account of every sale in the market.

Product Research helps an operator turn a broad category into a shortlist of products to investigate. The Web page offers presets such as new products, rising sales and beginner recommendations, together with filters for price, monthly sales, review count, rating, listing age and fulfillment method. For example, a team can look for products within its target price range that show demand but have relatively few reviews. Sales estimates and ranking signals help screen candidates; they do not establish sourcing costs or the profit the team could earn.

Competitor Research helps an operator identify comparable products and understand how they compete. A search can start with a keyword, brand, seller or Amazon product ID (ASIN); ZooData also provides current product details and historical records for price, sales rank, ratings and other supported fields. Comparing today’s position with the historical record helps investigate whether a competitor has changed price, improved its sales rank or accumulated more reviews. Product, history and current-detail queries provide different fields and update intervals, so one snapshot should not be treated as a continuously updated view of every metric.

VoC Analysis helps a team understand what buyers praise, complain about and want improved before developing a product or rewriting its listing. The operator enters product IDs or a category and chooses a period; ZooData aggregates sentiment, ratings, pain points, purchase reasons and usage scenarios. These findings can guide product-improvement questions and identify benefits or concerns that the listing should explain. The aggregate analysis is separate from individual review text, which is available through a different review-search function.

Keyword Research helps an operator investigate what shoppers search for and how products appear around those queries. Starting with a keyword or ASIN, the documented functions support related-term discovery, demand and trend analysis, competitor keyword lookup and product traffic-term analysis. The results can help build a keyword research list and investigate changes in search visibility. ZooData’s search and exposure signals are estimates; conclusions about actual conversion, advertising profitability or bid amounts also require the seller’s performance and cost data.

TikTok Shop research helps an operator find products gaining sales and compare the shops and items competing in the same niche. Its Web pages support product discovery and competitor lookup using categories, keywords, product IDs and shop information, with filters such as price, recent sales, growth, ratings and shipping. The documented data service also supplies current product details, inventory and variants for selected products. The current product-data documentation specifies the US market; Amazon’s research functions should not be assumed to have identical TikTok coverage.

For a team planning creator collaborations or researching content, ZooData provides APIs to find TikTok creators and videos by category, audience and commerce-related signals. Documented filters include creator attributes, video views, engagement, publication time and whether a video features a shoppable product. These results can supply a shortlist for the team’s own creator or content analysis; using this capability requires a connected tool or implementation, and the creator snapshot is currently scoped to the US. Sales of products a creator promoted are not automatically sales attributable to that creator.

Fashion teams can use ZooData’s image and search APIs to investigate similar styles from a picture or a text description. The documented functions detect clothing and accessories within an image and search for similar fashion products, returning information such as title, brand, price, image and retailer link. A separate ecommerce relevance function can reorder a supplied set of product descriptions against a search query. These are functions for a connected research or search tool; placing them in a store requires integration rather than simply activating a storefront widget.

Web extraction helps an operator or connected AI tool collect usable research material from a public product page or article. The homepage provides a URL input, while the WebTools service can return clean text or structured summaries containing the title, key facts and main entities. Its API also documents web search, site-link discovery and multi-page crawling for broader collection tasks. The result is source material for comparison or analysis; pages that refuse access may return an error instead of usable content.

ZooData publishes Skills: prepared research workflows that run inside a compatible AI assistant after the team connects its ZooData API key. They guide the assistant through jobs such as market-entry assessment, opportunity discovery and competitor analysis; the pricing and listing-audit Skills describe price recommendations, issue lists and suggested copy. Daily Market Radar uses a saved baseline to compare later observations and report changes, with recurring runs requiring scheduling in the host environment. Reports, price suggestions and copy drafts still need the team’s judgment and execution; they do not by themselves change store prices or publish listings.