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Formations/Data in fashion/Data in fashion/Reading sell-through by size and color to drive markdowns
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Data in fashion

1Reading sell-through by size and color to drive markdowns+1502Trend and demand sensing from search, social, and early POS signals+1503Turning returns data into margin: sizing, quality, and bracketing+1504End-to-end supply-chain visibility for allocation and replenishment+150

Reading sell-through by size and color to drive markdowns

# Reading Sell-Through by Size and Color to Drive Markdowns

A buyer opens Monday's report. A women's poplin shirt in "sky blue" is at 78% sell-through by week 4. The same shirt in "sage" is stuck at 22%. Same style, same price, same shelf. One is a winner screaming for a reorder. The other is dead weight heading for a markdown. If you read only the total style number (a blended 50%), you would miss both signals and make the wrong call twice.

This lesson teaches you to read sell-through at the level that actually drives decisions: the individual size and color.

What sell-through actually measures

Sell-through rate is the percentage of received inventory that has sold in a given period.

Formula:

Sell-through % = Units Sold / Units Received × 100

If you received 200 units of a SKU and sold 90, your sell-through is 45%.

SKU (stock-keeping unit) is the most granular product identifier. In apparel, one style splits into many SKUs, one per size and color combination. A single shirt in 5 colors and 6 sizes is 30 SKUs. Sell-through at the style level hides what is happening across those 30 lines.

The number most teams watch is the weekly sell-through curve: sell-through plotted week by week from the day product lands. The shape of that curve, not just the endpoint, tells you what to do.

Why the size and color cut matters

Total-style reporting lies by averaging. Two failure modes get buried:

Broken size runs. A style can look healthy at 60% overall while the mediums and larges are gone and only XS and XXL remain. That is a broken size run: the sizes most customers want are sold out, so incoming demand cannot convert. The remaining stock will never sell at full price. You need a markdown on the tail sizes, and possibly a size-specific reorder on the core.

Dead colorways. A colorway is a specific color version of a style. One trend-right color can carry a style's average while a safe basic color sits untouched. The blend looks fine. The sage shirt does not.

Reading by size and color separates "the product works but I bought the wrong depth" from "this color is a miss."

The seasonal assortment: a worked example

Take a Spring assortment for a mid-tier women's brand. One key style: a linen-blend blazer, 4 colors, 6 sizes, landed in stores the last week of February 2026 for an 8-week selling window before markdown season.

Here is week 5 sell-through by colorway:

| Colorway | Received | Sold | Sell-through |

|---|---|---|---|

| Ivory | 300 | 231 | 77% |

| Black | 300 | 210 | 70% |

| Butter yellow | 250 | 200 | 80% |

| Olive | 250 | 78 | 31% |

Blended style sell-through: about 65%. That number alone says "fine, no action." Wrong.

Butter yellow at 80% by week 5 is the trend color of the season and is about to break its size run. Check its size curve.

Olive at 31% is a miss. It will not recover in 3 weeks. Start planning the markdown now, before every competitor floods the market with olive at the same time.

Now the size cut on butter yellow:

| Size | Received | Sold | Sell-through |

|---|---|---|---|

| 0 | 20 | 12 | 60% |

| 2 | 40 | 39 | 98% |

| 4 | 50 | 50 | 100% |

| 6 | 50 | 49 | 98% |

| 8 | 60 | 38 | 63% |

| 10 | 30 | 12 | 40% |

Sizes 2, 4, and 6 (the core) are effectively gone. That is a broken run. The 80% blended number was inflated by the tail sizes still sitting there. New customers who want a size 4 leave empty-handed, so your sell-through curve will flatten early even though demand is still strong.

Two decisions fall out immediately:

1. Reorder the butter yellow core sizes if lead time allows delivery inside the selling window.

2. Markdown or hold the size 0 and 10 tails; they will not clear at full price once the core is gone.

Reading the shape of the curve

Sell-through curves have recognizable shapes. Learn the three that matter most.

Steep and early. Sharp climb in weeks 1 through 3, then flattening. This usually means a hit product that broke its size run. Flattening does not mean demand died; it means you ran out of the sizes people want. Action: reorder core, do not mark down.

Slow and flat. Weak from week 1, no acceleration. A genuine miss. Olive above. Waiting will not help. Action: markdown early while the season is live and other retailers have not yet discounted the same trend.

Linear and steady. Predictable climb tracking toward roughly 80 to 85% by end of window. This is a well-bought, correctly depthed item. Action: leave it alone.

The mistake beginners make is treating a flattening curve as a failure. A steep-then-flat curve is often your best seller starved of stock.

When to reorder versus mark down

The deciding variable is lead time: how long from placing a reorder to product on the floor.

A simple rule most planning teams use:

  • Reorder only if remaining selling weeks are greater than lead time weeks AND the size run is still sellable. A butter yellow reorder with a 3-week lead time and 3 weeks left is marginal; with 6 weeks left it is a clear yes.
  • Markdown when the curve is slow-and-flat, or when the core sizes are broken and cannot be replenished in time. A broken run left at full price simply ages into deeper markdowns later.

The cost of waiting is real. Markdown cadence (the schedule of successive price cuts) tends to deepen the longer you wait, because you are competing against every other retailer clearing the same seasonal trend at the same time. Early, shallow markdowns on a known miss often recover more margin than a delayed deep cut. For a clear primer on the mechanics of retail markdowns and margin, see Shopify's retail markdown guide.

Retail Math: Sell Through Rate Explained

Watch on YouTube

Setting size-level thresholds

Do not eyeball it. Set thresholds tied to your selling window so the report flags SKUs automatically.

A common approach: define a target curve, the ideal cumulative sell-through by week, then flag any SKU that deviates.

python
# Weekly sell-through flags by SKU
def flag_sku(sold, received, week, target_by_week):
    st = sold / received
    target = target_by_week[week]
    if st >= target + 0.15:
        return "REORDER CANDIDATE"   # running hot
    elif st <= target - 0.15:
        return "MARKDOWN CANDIDATE"  # running cold
    else:
        return "ON PLAN"

# Example target: 65% cumulative by week 5
targets = {5: 0.65}
print(flag_sku(200, 250, 5, targets))  # butter yellow -> REORDER CANDIDATE
print(flag_sku(78, 250, 5, targets))   # olive -> MARKDOWN CANDIDATE

The threshold band (here plus or minus 15 points) is a business choice. Fast-fashion players run tighter bands and shorter windows; premium brands with longer windows can tolerate slower curves. The point is a consistent, size-level rule instead of gut feel on a blended number.

Vérification des acquis

1. Why can a blended style-level sell-through of 50% lead a buyer to make the wrong call twice?

2. A SKU received 300 units and has sold 120. What is its sell-through rate, and what does it represent?

3. Why does the shape of the weekly sell-through curve matter more than just the final endpoint?

CHOIX MULTIPLES

4. Select ALL correct answers about a 'broken size run.'

Sélectionnez toutes les réponses correctes.

CHOIX MULTIPLES

5. Select ALL correct answers about why the size and color cut is essential for markdown and reorder decisions. (Select ALL correct answers)

Sélectionnez toutes les réponses correctes.

Common traps when reading the data

Confusing sold-out with sold-through. A SKU at 100% sell-through might have been under-bought. It sold everything you gave it, but you gave it too little. The signal is "buy deeper next time," not "great job."

Ignoring store-level distribution. National sell-through can hide that a color sells in warm-climate stores and dies elsewhere. If your data supports it, cut by region before you mark down chain-wide.

Markdown by style, not by SKU. Marking down the whole blazer to clear olive also discounts the butter yellow tail sizes you might still move at full price to a customer who wants that exact size. Where systems allow, mark down at the color or size level.

Reading too early. Week 1 sell-through is noisy, driven by launch traffic and store placement. Wait until at least week 2 or 3 before trusting the curve's shape.

Key Takeaways

  • Blended style sell-through averages away the two signals that matter most: broken size runs and dead colorways. Always cut by size and color.
  • The shape of the weekly curve drives action. Steep-then-flat usually means a hit that broke its run (reorder the core), while slow-and-flat means a genuine miss (mark down early).
  • Reorder only when remaining selling weeks exceed lead time and the core sizes are still sellable. Otherwise, mark down.
  • Set size-level thresholds against a target curve so winners and losers flag automatically, instead of trusting a single blended percentage.
  • Early, shallow markdowns on a confirmed miss often protect more margin than delayed deep cuts, because you clear before competitors flood the same seasonal trend.

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Trend and demand sensing from search, social, and early POS signals