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How to see a Shopify store’s sales: estimate traffic, revenue and best sellers

Want to see how much a Shopify store sells? Use this practical method to estimate traffic and revenue, identify best sellers, inspect its setup, and understand the limits of every estimate.

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.

1. Identify the platform and the stack

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.

2. Read the catalogue for its best sellers

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.

See the ads that are winning right now

3. Estimate traffic and revenue honestly

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.

4. Sort every signal: observed, estimated, inferred

Before drawing any conclusion, sort each signal into one of three columns. Observed: the catalogue, prices, best-seller order, apps, pixels, live ads — public facts you can verify yourself. Estimated: traffic and revenue — panel-based models, always read as a range. Inferred: profitability, the "winning product", the ad budget — conclusions you draw, not data.

The range takes two minutes to compute. Take the estimated monthly traffic, apply a standard ecommerce conversion rate of 1–3%, then multiply by the listed price of the best sellers. Example: 30,000 visits → 300 to 900 orders → at an average order around $40, between $12,000 and $36,000 per month. The width of the range is the information: if even the low end confirms the niche is working, the signal is good.

What this method cannot prove: exact revenue, margins, refunds, and customer acquisition cost. No public source has access to them. A tool that sells you a precise number instead of a range is selling you false confidence.

5. Check advertising activity

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.

6. Turn the analysis into a decision

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.

How can I see a Shopify store's revenue?

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.

How do I know which apps a Shopify store uses?

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.

Is it legal to analyze a competitor's store?

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.

What is the fastest signal that a store is doing well?

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.