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Days to Sell

The time metric that turns inventory into a rate.

Nine items listed on a calendar with their exit dates marked, the middle value at 14 days highlighted beside a 41-day outlier
Key takeaways
  • Days to sell = date the item left the market − listing date, with one fixed rule on whether withdrawals end the clock.
  • Use the median across a category, never the average: one 41-day stale listing drags a 14-day median readout to 16.7.
  • It measures time per item; sell-through rate measures share of stock per period — you need both to see speed and volume.
  • No EU marketplace publishes time-on-market averages, so your own log per category is the only benchmark you can price on.
  • Two decisions run on it: a lower max buy price for slow categories, and a markdown review at roughly 1.5× your median.

Days to sell is the time between listing an item and it leaving your stock: a sweater listed on 3 September and gone on 17 September took 14 days. A handful of such numbers tell you how fast a category moves for you. Below: the formula, median vs average, the difference from sell-through rate, and the two decisions the number drives.

Definition and formula

days to sell = date the item left the market − date you listed it

The one judgement call is what "left" means. The clock can stop at a completed sale, or also at a withdrawal — both are defensible, but pick once and never mix: sold-only weeks and sold-or-withdrawn weeks give numbers you cannot compare. Listed 3 September, withdrawn 25 September → 22 days by the same rule.

For other sellers' listings you only ever see that a listing vanished, not why — which limits what market-level numbers can honestly claim.

Median vs average: why one outlier ruins the mean

Say nine hoodies, same brand and size, left your stock in these times: 6, 9, 11, 12, 14, 15, 18, 24 and 41 days. The median — the fifth of nine — is 14 days. The average is 16.7. The 41-day listing sat mispriced for three weeks before a cut moved it; it drags the mean, not the median.

One worked example: nine exits of one hoodie line
14 daysmedian — the 5th of nine exitshalf the items went faster
16.7 daysaverage of the same ninepulled up by one stale listing
41 daysthe outliermispriced for weeks, then reduced
Worked example, not market data. Plan on the median; the average answers a different question.Illustrative example, not market data

Plan on the median.

Days to sell vs sell-through rate

Days to sell Sell-through rate
Measures time per item share of stock sold in a period
Answers how long does one item take? what share of stock moved this month?
Blind spot says nothing about volume hides a slow tail behind fast sellers

A category can post a healthy sell-through while its slowest third sits for months; only the per-item times expose that tail. The full comparison is in sell-through rate.

What changes days to sell: category, price level, platform

  • Category. Current-season basics in common sizes move faster than niche collectibles — a typical pattern, not a law. No EU marketplace publishes real averages, so a "typical time on market" chart without a method is a guess.
  • Price level. Within one category the cheapest matched asks tend to leave the market first; a median across all price levels blends different markets.
  • Platform. Buyer pools differ per marketplace and country site; the same jacket can be fast on one and dead on another.

Your own log absorbs all three: it measures your prices, your photos, your city.

Using it: buy decisions and markdown dates

Buy decisions. The slower the expected exit, the lower the price you can afford to pay — time is risk. How capital parked in slow stock starves the rest is in reseller cash flow; the stock ceilings it feeds are in how much inventory to keep; the money-at-cost view of the same speed is the inventory turnover calculator.

Markdown dates. Set the review date when you list: a workable rule is roughly 1.5× your category median. Median 14 days → review at day 21. Favourites but no offers by then: the price is the suspect. Neither: fix the listing first.

Tracking it: one spreadsheet column

One row per item with listed_date and the exit date, and the number computes itself — the blog's inventory spreadsheet template ships with that column, and fields worth keeping are in the inventory management guide. Review the per-category median monthly. What you can and cannot see about other sellers' timing on Vinted — exits, relists, why vanished listings prove nothing — is on Vinted time to sell.

Frequently asked questions

How do you calculate days to sell?
Subtract the listing date from the date the item left the market — a completed sale, or a withdrawal if you count those. A sweater listed on 3 September and gone on 17 September is 14 days. Decide once whether withdrawals end the clock and keep that rule fixed: sold-only weeks and sold-or-withdrawn weeks give numbers that cannot be compared.
What is a good number of days?
No public average is worth copying: EU resale platforms do not publish time-on-market statistics, and charts claiming typical figures rarely show their method. Your own median per category is the reference: if your jackets have left your stock at a median of 14 days over three months, a listing sitting at day 30 has a problem, and a category whose median doubles needs a price review.
Is days to sell the same as inventory turnover?
No. Days to sell measures one item's time from listing to market exit; turnover measures how many times your capital cycled through stock in a period, in money at cost. They agree in direction but catch different problems: a shop can hold decent turnover on a few fast sellers while its slow tail sits for months — something only the per-item times reveal.
Why use median instead of average days to sell?
One stale listing wrecks the mean. In the worked example, nine items exit at a median of 14 days while the average reads 16.7 — dragged up by a single 41-day listing that sat mispriced for three weeks. The median answers the question you actually have: how long does the typical item take? The average includes the disasters, which is a much less useful number for planning.
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