Etsy Listing Performance: Which Numbers Matter and What Good Looks Like

To judge an Etsy listing's performance, look at five numbers: views, favourites, orders, conversion rate (orders divided by views) and visit sources, over a period of at least 30 days. Judge each listing against your own shop's typical figures rather than an industry average, because category, price and season move the numbers more than anything you do. A listing is a winner when it converts well above your shop's norm, and it is fading when its views fall while the rest of the shop holds steady.

This post is about reading individual listings. For the shop-wide funnel (impressions, click-through, search analytics), see the Etsy shop analytics guide.

Which Etsy numbers matter for a single listing?

Etsy Stats shows a lot. For one listing, five numbers do the work:

• Views. How often the listing page was opened. This measures whether the listing is being found and whether its thumbnail and title win the click.
• Favourites. How many people saved it. A favourite means the first impression worked and the buyer wanted to come back, but it is not a purchase.
• Orders (sales). The only number that pays. On its own it is misleading, because a listing with 2,000 views and 6 orders is not better than one with 200 views and 4.
• Conversion rate. Orders divided by views for the same period. This is the number that tells you whether the listing page closes the sale, and it matters for search too, because Etsy tends to rank higher the listings that sell when shown.
• Visit sources. Where the views came from: Etsy search, Etsy Ads, direct links, social, or other sites. A listing whose views are mostly from paid ads is in a different position from one earning them in organic search.

Everything else is either an input to these (tags, photos, price) or a shop-level number.

What does "good" look like for an Etsy listing?

There is no universal good number. A £12 digital download, a £45 framed print and a personalised map all convert at very different rates, and the same print converts differently in November and in March. So the only benchmark that holds up is your own shop over the same period.

Work out three reference figures once a month:

1. Typical views per listing. Use the median rather than the average. A few bestsellers drag the average up and make every normal listing look weak.
2. Typical conversion rate. Total orders divided by total views across active listings.
3. Typical favourites per 100 views. This tells you how often your listings earn a save.

Then place each listing against those figures. "Good" means at or above your shop's norm on the number that matters for that listing's stage.

A worked example

Say a 400-listing wall-art shop looks at the last 90 days. Across the shop: 48,000 views and 360 orders, so a shop conversion rate of 0.75%. The median listing had 70 views.

These figures are illustrative, not benchmarks. The method is the point: the fern set and the tube map need opposite fixes, and the raw order counts alone would never have shown that.

• High views, low conversion (the fern set): the listing page loses the decision. Look at photos beyond the first, price against the listings shown beside it, delivery cost and time, and whether size and scale are obvious. The favourites guide goes through this case in detail.
• Low views, good conversion (the tube map): the listing sells when people find it, so the problem is discovery. Work on the title's opening phrase, the tags, attributes and the thumbnail.
• Low views, no orders (the arch print): not enough data to know. See the guide to listings with no sales for how to decide what to do with these at scale.

Small numbers deserve caution. With 60 views, one extra sale moves the conversion rate from 1.7% to 3.3%. Do not draw conclusions from a listing with fewer orders than you have fingers on one hand.

How do you spot a fading listing?

A fading listing is one that used to perform and is quietly losing ground. Catch it early and a refresh often brings it back; catch it late and it has already lost its position.

Compare each listing's last 30 days with a fair reference period:

• The same 30 days last year, if the listing is old enough. This cancels out seasonality, which is huge for prints (gifting peaks, January slumps).
• The previous 30 days, if it is newer, but check the whole shop moved the same way before blaming the listing.

The signal you are looking for is this listing falling while your shop holds level. If views are down 40% and the shop is down 35%, that is the season. If views are down 40% and the shop is flat, that listing has lost search position, often because a competitor has a stronger thumbnail for the same search or because your own newer listing now competes with it (see listing cannibalisation).

Fading usually shows in views first and orders later. A listing whose conversion rate holds but whose views slide is still a good listing being found less, which points at search and the thumbnail rather than the listing page.

How do you spot a winner, and what do you do with it?

A winner converts clearly above your shop's typical rate on a meaningful number of views, say double the shop rate over at least a few hundred views. Winners are worth more attention than any other listing, for two reasons:

• Protect them. Avoid casual edits to a listing that is converting well. If you must change something, change one thing and watch it.
• Copy what works. A winning listing tells you something about subject, style, price or photo layout that buyers respond to. Make the next listings in the same direction rather than guessing.

Check what drives a winner's visits. If most of its views come from Etsy Ads, turning ads off may also turn the winner off. If most come from organic search, it is earning its position.

Why do visit sources matter?

The same view count means different things depending on where it came from:

• Etsy search: the listing ranks for real queries. This is the traffic SEO work affects.
• Etsy Ads: paid. Useful for testing, but a listing that only performs while ads run is costing you per view. The Etsy Ads guide covers judging that.
• Direct and social (Pinterest, Instagram): traffic you built yourself. It often converts differently from search traffic, so do not mix it into a search-ranking judgement.
• Off-site ads: Etsy's own advertising on other sites, charged per sale. These sales carry a fee, so they are worth less per order.

When a listing's conversion rate changes suddenly, check whether its source mix changed first. A burst of Pinterest traffic can drag a rate down without anything being wrong with the listing.

How do you test a change for 14 days before judging it?

Etsy has no built-in split testing, so the practical method is a before-and-after test on one listing:

1. Pick a listing with enough traffic. A listing with 15 views a fortnight cannot produce a readable result. Use one with at least a few hundred views in 14 days.
2. Record the 14 days before the change: views, favourites, orders, conversion rate, and the share of visits from search.
3. Change one thing. A new thumbnail, or a new title opening, or a new price. Not two. If you change the photos and the title together, you will not know which one did it.
4. Write down the date. You will not remember it in a month.
5. Wait 14 days without touching the listing. Etsy needs time to re-evaluate an edited listing, and early days are often noisy.
6. Compare ratios, not totals. Conversion rate and favourites per 100 views are fairer than raw orders, because shop traffic rises and falls on its own.
7. Check the shop moved the same way. If the whole shop rose 30% in the after period, a 30% rise on the listing proves nothing.

Example. The fern set above: 14 days before, 230 views, 0 orders, 21 favourites. You replace slot two with a scale shot showing the set on a wall above a sofa, and add the sizes to slot three. 14 days after, 240 views, 3 orders, 22 favourites. Favourites held steady, so the thumbnail did the same job; orders moved, so the change helped the listing page. Three orders is still a small number, so run it another 14 days before rolling the same change across similar listings.

Small improvements need far more traffic to prove than most shops have. The A/B testing guide explains why, and why big changes on high-traffic listings are the only tests most shops can read.

A monthly routine for a large catalogue

With hundreds of listings, checking each one by hand is not realistic. Once a month:

• Update your three shop reference figures (median views, conversion rate, favourites per 100 views).
• List the top 20 listings by orders and check none of them is fading.
• List listings with high views and conversion well below the shop rate: these are your fix list.
• List listings with good conversion and low views: these need discovery work.
• Leave everything else alone.

If you would rather not do this in a spreadsheet, the Performance page in PSDmate shows views, favourites and sales per listing, flags winners, fading and zero-traction listings, and tracks 14-day before-and-after tests when you rewrite a listing.