Sell-Through Curves: The Weekly Signal That Tells You When to Markdown

Markdown by the calendar is wrong

Most retailers markdown on a schedule. Week 6 of the season, 25% off. Week 10, 40% off. Terminal clearance, 60% off. The calendar is predictable, the schedule is operationally clean, and the math is wrong for most of the assortment.

Sell-through curves vary by SKU, by store, by season. A SKU running 8% sell-through per week is going to land at 80%+ terminal sell-through with no help. A SKU running 3% per week is going to terminal-out at 30%, and the calendar markdown at week 6 is too late. The first SKU got an unnecessary margin cut; the second SKU needed earlier intervention.

Calendar markdowns exist because they are easy to administer. Margin was never what they were tuned for. The retailers that switched to curve-driven markdowns recovered 80-160 basis points of season margin, on average.

The weekly sell-through curve

Sell-through rate is simple: units sold divided by units received, expressed as a cumulative percentage of the season. Plotted weekly, it produces a curve. The slope of the curve in weeks 1-3 is highly predictive of terminal sell-through.

Empirically, across thousands of seasonal SKUs at mid-market apparel and specialty retailers, weekly ST trajectory in weeks 1-3 predicts terminal ST at week 12-14 with 75-85% accuracy. The retailers that ignore this signal spend the rest of the season reacting to lagged data and burning margin on calendar-based discounts.

A healthy 12-week seasonal curve looks like this: week 1 ST 6-9%, week 3 cumulative 22-28%, week 6 cumulative 50-58%, week 9 cumulative 72-80%, week 12 cumulative 88-94%. Terminal clearance handles the last 6-10%. Margin is preserved because most of the season sold at full or near-full price.

A troubled curve diverges early. Week 1 ST of 3-4%, week 3 cumulative 12-15%, week 6 cumulative 28-34%. The terminal projection is 55-65%, leaving 35-45% of buy stranded. By the time the calendar says "markdown" at week 6, you're already 4-5 weeks behind where intervention would have mattered most.

Early intervention math

A 15% markdown at week 3 on a troubled SKU often clears the inventory at 80-85% terminal ST. The same SKU, untouched until calendar markdown at week 6, requires a 30% cut to hit 70% terminal ST, leaving 30% for clearance at 50-60% off.

Run the numbers on a $20 retail SKU with $8 cost, 1,000 units bought:

• Curve-based path: 15% off starting week 3. 85% sells at average $17.50 (mix of full price weeks 1-2 and 15% off thereafter), 15% clears at 50% off ($10). Total revenue: $14,875 + $1,500 = $16,375. Margin: $8,375.
• Calendar-based path: Full price weeks 1-5, 25% off weeks 6-9, 40% off weeks 10-12, clearance at 60% off after. 28% full price ($20), 32% at $15, 25% at $12, 15% clears at $8. Total revenue: $5,600 + $4,800 + $3,000 + $1,200 = $14,600. Margin: $6,600.

The curve-based intervention preserved $1,775 of margin on a single 1,000-unit SKU. Multiply by 400-600 troubled SKUs per season at a mid-market retailer: $700K-$1.1M in margin recovery per season, $1.4M-$2.2M annually across two seasons.

Read the curve as a signal

Weekly ST is one input among several. A SKU running below curve in week 2 might be experiencing a fixable problem: blocked planogram placement, missing signage, an out-of-stock at a peer store creating localized inventory imbalance, or weather affecting the trade area.

The discipline is to investigate before discounting. Most fixable weekly ST shortfalls are concentrated in one or two stores out of fifty. If 47 stores are tracking curve and 3 are missing it, you have an execution problem in three buildings. Operational fixes don't cost margin. Markdown does.

The retailers running curve-driven markdowns at scale use a tiered framework:

• Tier 1: Investigate. Chain ST on-track, but 2-5 stores below curve. Fix the local issue: planogram, replenishment, signage, fixture.
• Tier 2: Localized markdown. Chain ST on-track, but a regional cluster of 8-15 stores running 25%+ below curve. Region-specific markdown rather than chain markdown.
• Tier 3: Chain markdown. Chain ST 20%+ below projected curve at week 3-4. Take the chain action, take it early, and use a smaller percentage than the calendar default.
• Tier 4: Reorder freeze + accelerated clearance. SKU is so far below curve that recovery is impossible. Freeze open POs, accelerate clearance markdown immediately, free shelf space for the next launch.

Store-level ST variance hides the signal

Chain ST is an average. A 28% chain ST at week 3 includes stores at 18% and stores at 38%. If you markdown based on the chain number, you discount the 38% stores unnecessarily and don't discount the 18% stores aggressively enough.

Store-level ST decomposition is where the real signal lives. Cluster stores by trade area demographics, climate, and competitive density. Run sell-through curves by cluster. Cluster A might be tracking curve at 32%, cluster B at 24%, cluster C at 16%. The markdown strategy should differ across clusters.

This requires data infrastructure that most retailers don't have natively. SKU-store sell-through at weekly cadence, joined to cluster assignment, with curve baselines for each cluster from prior-season data. Most BI tools can produce this report once, but they can't run it continuously across 5,000 SKUs and 100 stores without a data engineer pulling shifts to maintain it.

This is where signal-based monitoring infrastructure earns its keep. The SKU-store-week sell-through curve runs continuously. Cells diverging from curve trigger alerts. The merchandising team sees the divergence in week 3, not week 6. The intervention happens with margin intact.

How to calculate sell-through rate

Sell-through rate is units sold divided by units received, times 100. Receive 1,000 and sell 620, and sell-through is 62%. The formula is not where teams go wrong. The denominator is.

Received, not available. Use units received into the selling channel for the period. Chains that use units available, which includes carryover from prior seasons, produce a number that flatters current-season buying and hides aged stock inside a healthy-looking rate.

Cumulative, not weekly. Weekly sell-through (units sold that week over units received) is useful as a velocity read. The curve that predicts terminal outcome is the cumulative one. Mixing the two in the same conversation is the most common source of confusion in a markdown meeting.

At full price, tracked separately. Track sell-through at full price alongside total sell-through. A SKU at 88% total sell-through with 40% of it discounted is a different outcome than 88% with 12% discounted, and only the split shows it.

Sell-through and days of supply answer the same question from opposite ends. Sell-through asks how much of the buy has cleared. Days of supply asks how long what remains will last. Seasonal buyers should be reading both weekly.

Sell-through benchmarks and the weekly curve

Terminal sell-through targets vary by how disposable the assortment is. The tighter the season window, the higher the required sell-through, because there is no second chance to sell it at anything close to full price.

The holiday row is worth calling out. Applying a fashion curve baseline to holiday product triggers false interventions in weeks 2 through 4, and a chain that marks down holiday inventory in week 3 has given away margin it would have earned in week 8. Every category type needs its own curve baseline from its own prior-season data.

The year-over-year improvement loop

Sell-through curves compound across seasons. Each season's curve data improves the baseline for the next season. SKUs are classified, store clusters are refined, intervention thresholds are tuned. Year 2 curve-based markdowns are 30-40% more accurate than year 1 because the model has actual data instead of category averages.

One mid-market apparel retailer ran the curve-based system for three seasons. Year 1 margin recovery was $1.6M. Year 2 was $2.4M. Year 3 was $3.1M. The same operational discipline applied with progressively better data produced compounding margin returns. Most importantly, terminal clearance percentage dropped from 18% of receipts to 9%, which freed shelf space for trend-right product earlier in subsequent seasons.

The retailers stuck on calendar markdowns are leaving this entire compounding curve on the table. The first season of switching is the smallest gain. The fourth season is when the math actually shows up.

Key takeaways

• Calendar markdowns are built for administrative ease, and margin pays for it. Curve-driven markdowns typically recover 80-160 basis points of season margin.
• Weekly sell-through trajectory in weeks 1-3 predicts terminal sell-through at week 12-14 with 75-85% accuracy. The signal is available early enough to matter.
• Early intervention with smaller discounts beats late intervention with larger discounts. A 15% cut at week 3 typically outperforms a 30% cut at week 6 by $1,500-$2,000 per 1,000-unit SKU.
• Treat weekly ST as a signal that something needs a look. Most off-curve performance in weeks 1-3 has fixable operational causes: planogram, signage, replenishment, fixture. Investigate before discounting.
• Sell-through is units sold over units received. Use received rather than available, track the cumulative curve rather than the weekly rate, and split full-price sell-through from total or the number flatters itself.
• Terminal targets run 88 to 94% for a 12-week fashion season, 90 to 96% for holiday, and 82 to 90% for specialty seasonal. Each needs its own week-3 baseline.
• Holiday curves are back-loaded by design. Applying a fashion baseline to holiday product triggers false markdowns in weeks 2 to 4 and gives away margin that would have arrived in week 8.
• Chain-level ST averages hide store-level variance. Cluster-based markdown strategies are needed when stores diverge 15-30% from the chain curve.
• Sell-through curve data compounds across seasons. Year 3 curve-based markdowns are typically 30-40% more accurate than year 1, with margin recovery growing season over season.
• One mid-market apparel retailer recovered $7.1M in margin over three seasons of curve-driven markdowns, with terminal clearance dropping from 18% to 9% of receipts.