There is a thing that experienced dropshippers and DTC researchers do that sounds almost too simple to be a competitive advantage.
They watch Shopify stores.
Not in a vague, browsing sense. They monitor specific stores' product catalogues systematically, checking regularly for new products that have been added and tracking inventory changes that might signal sales velocity. They are looking for the moment a store adds something new — particularly something that appears to move from "added to catalogue" to "out of stock in popular variants" quickly — because that pattern is one of the most reliable early signals that a product is gaining traction.
The reason this works is a feature of Shopify's architecture that the platform has never tried to hide. Every public Shopify store exposes a JSON endpoint at /products.json — for example, storename.com/products.json — that returns a structured list of all products with their titles, descriptions, prices, variants, inventory availability, and timestamps. This endpoint is unauthenticated. It was designed as a public product feed, roughly equivalent to an RSS feed for a store's catalogue. Shopify built it in. Every public store has it.
For several years, sophisticated DTC researchers and dropshippers have been manually checking these endpoints for stores they were watching. Some had built custom scripts. Most were doing it inefficiently — opening a bookmark in a browser, scrolling through JSON, trying to remember what was there last time.
What nobody had built was a clean, scheduled, delta-tracking actor that monitored a watchlist of Shopify stores, automatically identified new products added since the last run, detected price changes, flagged inventory movements, and delivered structured alerts — all without any manual checking.
The Shopify Product Tracker fills that gap.
You give it a list of Shopify store URLs to monitor. It fetches the /products.json endpoint for each store, normalises the product data, compares it against the stored snapshot from the previous run, and identifies three types of changes: new products added since last run, price changes on existing products, and stock status changes (products going from in-stock to out-of-stock or vice versa).
The new product alert is the highest-value signal for product research. When a store that curates winning DTC products — a successful general store, a niche store with a track record of early-trend identification, a store operated by someone whose product judgement you have come to trust — adds a new product, you know about it within hours rather than days.
The price change alert is valuable for competitive pricing intelligence. If a direct competitor drops the price on a core product, you want to know about it before customers start comparing prices in your checkout. The stock change alert serves a more nuanced purpose: a product that repeatedly goes out of stock in specific variants is a product generating genuine demand.
The technical implementation is clean precisely because the data source is clean. The /products.json endpoint is structured JSON, not scraped HTML. It does not require a browser, does not trigger bot detection, and does not vary by region or device. The actor fetches a URL, parses JSON, computes a diff against stored state, and emits events. It is the most reliable actor in the portfolio from a maintenance perspective because it depends on an intentionally public, stable API rather than scraping a dynamic page.
Commerce Inspector and Dropship.io charge £49 to £99 per month to provide similar product intelligence. UNISPY, one of the more affordable alternatives, requires users to keep a Chrome extension running in an open browser tab continuously — effectively outsourcing the monitoring burden back to the user's own machine.
The Shopify Product Tracker charges £0.01 per store-day monitored and £0.08 per new product detected. A dropshipper monitoring ten competitor stores daily pays £0.10 per day — £3 per month — for intelligence that was previously costing £49 per month or several hours per week of manual checking.
The insight is not complex. The data is public. The implementation is straightforward. The value is in the consistency — running every day, reliably, without anyone needing to remember to check.