Every Shopify store leaks information. Its theme, its apps, its tracking pixels, the order of its collections, the products it pushes to the homepage — all of it is public, and all of it tells you what the store has learned about its own market.
Reading that correctly is faster than any survey: instead of guessing what sells in a niche, you look at what a store that already sells there has decided to bet on.
This guide walks through the five layers worth checking on any competitor store, what each one actually proves, and where the estimates start lying.
Start by confirming the platform, because it decides what else you can see. Shopify stores expose far more than most: their product catalogue is readable through a public endpoint, which means the full product list, prices, variants, images, and creation dates are available without any tool.
The stack follows the same logic. Review apps, upsell apps, subscription tools, page builders, and email platforms all leave traces in the page source. A store running an aggressive upsell app plus a review platform plus a page builder is a store that has invested in conversion rate — a sign it has moved past testing.
A Shopify store's best-selling collection is a public, self-declared ranking. It is the single most valuable page on a competitor site: the store is telling you, in order, what its customers actually buy.
Cross-check it against the newest products. A store that adds ten products a week is still testing; one that adds two a month and pushes the same three products everywhere has found its winners and is scaling them. That difference tells you whether you are looking at a laboratory or a proven catalogue.
Traffic estimates come from public panel data and behave predictably: reasonably indicative for large stores, noisy for small ones. Revenue estimates multiply that traffic by an assumed conversion rate and average order value — two assumptions stacked on an estimate.
Use them as an order of magnitude, never as a number. The useful question is not "does this store make $47,000 a month" but "is this store in the thousands or the hundreds of thousands, and is the trend rising or falling". A tool that presents estimates as precise figures is selling you false confidence.
A store's ads tell you where its money goes and how confident it is. Look at how many ads run simultaneously, how long the oldest one has been live, and whether creatives are being renewed.
The most informative pattern: a store running few ads for a long time has found an angle that works and does not need to keep testing. A store running many short-lived ads is still searching. Both are useful — the first shows you a proven product, the second shows you a niche in motion.
Doing all five layers by hand takes about an hour per store, spread across a platform detector, a traffic estimator, an ads library, and a lot of page-source reading.
Trackira collapses that into one screen: paste a domain and it returns the platform, the apps and pixels, the ranked product catalogue with prices and publication dates, estimated traffic and revenue with their history, and every ad the store is running across Meta, TikTok, Pinterest, and Google Shopping. Estimates are labelled as estimates.
From there the same workspace continues the job — sourcing the product from suppliers, and preparing your own listing.
You cannot see it exactly — no public source discloses real revenue. Estimates combine traffic panel data with assumed conversion rates and average order values, which is reliable as an order of magnitude for large stores and unreliable for small ones. Treat every figure as a range, not a number.
Apps inject scripts and markup into the storefront, so they are visible in the page source. Review platforms, upsell tools, page builders, and email apps each leave recognizable traces — a store analysis tool simply automates the detection.
Reading publicly published information — pages, prices, public catalogue endpoints, advertising libraries — is standard competitive research. What matters is respecting the site's terms and rate limits, and never touching private or customer data.
Advertising longevity combined with a stable best-seller list. A store that has kept the same products at the top while running ads for months is profitable; nobody sustains both without margin.