Methodology

CraftRank exists to surface numbers Etsy keeps fuzzy. Etsy does not publish exact sales or revenue for a listing, so we estimate them from data that is public. This page explains how, and just as importantly, how to use the estimates well.

Where the data comes from

CraftRank's research data is derived from publicly available Etsy data, the same information anyone can see on the site: listings, prices, views, favourites, shop sold counts, reviews, and tags. If you optionally connect My Shop, you authorise CraftRank through Etsy OAuth to verify ownership. We never receive your Etsy password and do not retain private seller, transaction, or private listing data.

Most metrics are refreshed daily. Shop and keyword results are cached briefly (typically within a day) so repeat lookups are fast, which is why two analyses close together can show identical numbers.

How estimated sales work

Etsy shows views but not how many of those views became orders. We estimate sales by applying a typical view-to-sale conversion rate to a listing's view count. The result is an estimate, not a counted figure, so treat it as directional.

When we analyse a whole shop, we use a shop-specific ratio instead of a generic rate: the shop's lifetime sold count divided by the views on the active listings we analysed. This is an allocation assumption that spreads the shop's lifetime sales across those listings, not a measurement of how any one listing converts.

How estimated revenue works

Revenue is estimated sales multiplied by the listing's price, converted to USD where needed. Because it builds on the sales estimate, it carries the same caveat: good for comparison and scale, not an exact dollar figure. Actual revenue varies with sales, promotions, and other things Etsy does not expose.

Listings with variations

Etsy publishes one price per listing. On a listing that offers variations, such as several sizes or finishes at different prices, that published price is the cheapest option. The exact per-variation prices are only available to the shop owner, so no external tool can read them for a competitor.

We do not guess at the difference. Instead we mark it. Wherever a price comes from a listing with variations, the app shows it as a starting price ("from"), and flags every figure built on that price:

• The listing's estimated revenue, on its own page and in the results tables.
• A shop's average listing price and gross lifetime sales, where the effect grows with the share of the catalogue that uses variations. Shop Analyzer tells you that share.
• The price bands in Shop Analyzer, which count those listings at their cheapest option.

Two separate assumptions sit behind these revenue figures. The price is the listed starting price today, not the options buyers actually chose, any discounts they received, or what the listing cost in the past. The sales volume is itself an estimate, and it can be too high as well as too low. So an estimate built on a starting price is not a guaranteed minimum: actual revenue can be above or below it. Treat it as an estimate like any other, and be cautious when comparing shops whose catalogues or categories differ.

Shop conversion rate is a sales-to-view proxy

The conversion rate in Shop Analyzer is a sales-to-view proxy, not a measured conversion rate. It divides the shop's lifetime sold count by the lifetime views of the active listings we analysed, which is capped at 200 listings. Sales from discontinued or unanalysed listings are counted, but their views are not, so large or long-running shops can read high.

It is not comparable with the conversion rate in Etsy Stats, which is based on orders and shop visits over a chosen time period. Do not use it to benchmark a shop; treat it as the assumption behind the shop's per-listing sales estimates.

How the opportunity score works

The Opportunity score in Keyword Explorer is a single 0-100 read of whether a search term is worth competing for. It balances two things:

• Demand how much buyers want the term, based on how many listings target it and how well the leaders sell.
• Competition how hard it is to break in, based on how crowded the term is and how entrenched the ranking listings are.

A term with strong demand and weak competition scores high; a crowded term with little demand scores low. The app labels the bands for you: 66 and above is high, 33 to 65 is moderate, below 33 is low, and the demand and competition meters use the same bands so you can see what is driving the score.

How to use directional numbers well

Estimates are most useful when you lean on their strengths:

• Compare, do not quote. The estimates are reliable for ranking shops or listings against each other, less so as a precise figure to put in a spreadsheet.
• Watch the trend. Because data refreshes over time, the direction a number moves is often more telling than its absolute value.
• Trust the measured signals more. Shop conversion rate, review counts, and prices are observed, not estimated, so weight them accordingly. The one caveat is a price marked "from", which is the cheapest of several variations rather than the whole picture.

Related

• FAQ for data, privacy, and plan questions.
• Keyword Explorer for the opportunity score in action.