Sell-through rate: did the buy work?

The question buyers avoid

Every order you place is a prediction. You are saying: I think we will sell roughly this many of these, at roughly this rate, over roughly this period. Money goes out on the strength of that prediction weeks or months before any of it is tested.

Most stores never go back and mark the prediction. The stock arrives, some of it sells, the season ends, and attention moves to the next order. Whether the original call was good is a question nobody asks, which means the next call is made with exactly as much information as the last one.

Sell-through rate is the marking scheme. It is the proportion of received stock that sold in a given period, and it is the closest thing retail has to a score for a buying decision.

The calculation

Units sold divided by units received, over a defined period.

Our glossary example: you received 200 units at the start of the month and sold 150 by the end. Sell-through is 75 per cent.

Two details decide whether the number means anything.

The period has to be deliberate. Sell-through over an undefined window is meaningless, because any product reaches 100 per cent eventually if you wait long enough. The period should match how you think about the product: a season for seasonal goods, four weeks for a launch, the promotion window for a promotion.

Received means received, not ordered. If a third of the order landed three weeks late, measuring against the full quantity scores the buyer for a supplier's failure. Measure against what was actually available to sell in the window, and note the delay separately, because it is a different problem with a different owner.

Why the percentage alone cannot answer the question

This is the part that matters, and it is the part most sell-through reporting misses entirely.

Seventy-five per cent sounds healthy. Whether it is depends on something the percentage cannot contain: what the stock was doing on the last day of the period.

Two stores both hit 75 per cent on the same product.

In the first, 50 units were still on the shelf on the final day, demand had tailed off naturally, and the remainder will clear in the following weeks at full price. That is a well-judged buy with a comfortable margin of error.

In the second, the product sold out on day 22. Demand continued for another week and was turned away. The same 75 per cent describes a buy that was too small, cost real revenue, and sent some customers to a competitor to find out they sell it too.

Identical number, opposite verdicts. Which is why sell-through has to be recorded alongside the stockout date, or it will systematically reward under-buying. A buyer who consistently orders too little will post excellent sell-through forever and look like your best performer.

What different results are usually telling you

Rough interpretations, holding the stockout question to one side:

• Very high, with an early sellout. Under-bought. The demand was there and you could not serve it. Increase the next order and look at whether the lead time allows a mid-season repeat.
• Very high, selling through smoothly to the end of the window. The best outcome, and harder to achieve than it looks. Repeat the approach rather than congratulating yourself and doubling the order.
• Moderate, with stock remaining. Normal, and usually fine. Worth watching the rate over the following weeks to see whether the tail clears at full price or needs discounting.
• Low, with most of the stock left. Either the buy was wrong or the product was not given a fair run. Check placement, pricing and whether it was actually visible before concluding anything about demand.

That last distinction is worth protecting. A product buried on page four of a collection did not fail to sell, it failed to be seen, and treating that as a demand signal means you will stop buying something that was never properly tested.

The repeat-order window

The most valuable thing sell-through does is not scoring the past. It is catching an under-buy while there is still time to fix it.

Measure sell-through at a fixed early point, a week or two into the window, and compare it against where it should be at that point for the stock to last the full period. If a four-week product is at 60 per cent after eight days, it will be gone in roughly a fortnight.

That is only useful if your supplier lead time is shorter than the time remaining, which is why the check has to be early rather than thorough. A perfect analysis at week three of a four-week window tells you about a stockout you can no longer prevent.

In practice this means the early sell-through check belongs in a weekly routine rather than a monthly report. The weekly review is the natural home for it, because that is already the meeting where slow signals get caught.

Sell-through and markdown

For seasonal goods, sell-through at a given point drives the discounting decision, and the mistake is almost always waiting.

If a product is tracking well below where it needs to be with a fixed end date approaching, the choice is to discount now at a smaller reduction or discount later at a larger one. Later is almost always more expensive, because the remaining stock is larger and the time to shift it is shorter, so the discount has to work harder.

Setting a sell-through checkpoint in advance makes this a rule rather than an argument. "If we are below 50 per cent at the halfway point, we mark down by 20 per cent" is a decision made calmly, in advance, by people who are not yet emotionally invested in the product having worked.

Running the number

The sell-through rate calculator takes units received and units sold and returns the percentage, plus what remains. Free, no signup, nothing stored.

What it cannot do is remember the stockout date for you, and that is the field worth adding to however you record this. Sell-through plus "did it run out, and when" is a genuinely useful buying record. Sell-through alone is a number that rewards timidity.

Building the habit

You do not need a system. You need a row per product per buying cycle, with five columns: units received, units sold, the window, whether it ran out, and one line on why you think it went the way it did.

That last column is the one that compounds. A percentage tells you what happened. A sentence written while you still remember tells you why, and after four or five cycles the pattern in those sentences is worth more than any of the individual numbers. You will find you are consistently generous on one category and consistently cautious on another, and that is actionable in a way a spreadsheet of percentages never is.

If you run a post-peak review, this is the evidence it should be built on. The post-peak retro covers running that session so it produces one change rather than a list of complaints, and sell-through by product is the least arguable input you can bring to it.

One number this does not replace

Sell-through tells you how much of what you bought has sold. It says nothing about whether the product was worth selling, because it is blind to margin. A product that clears at 95 per cent on heavy discount has excellent sell-through and may have made you nothing at all.

So the buying record wants a margin column beside the percentage, built on what the units actually cost you rather than the supplier invoice. A line that looks like a star on sell-through and mediocre on realised margin is a line you are buying too much of at the wrong price, and you cannot see that from either number alone.

That is the whole case for keeping these measures together rather than reporting them in different places to different people. Each one is easy to game in isolation and hard to game as a set.

Common questions