The $5B Size Curve Shift: How GLP-1 Is Rewriting Retail Demand

Retail planning teams spend enormous energy chasing shifts in trend, pricing, and demand. Yet one of the biggest demand drivers may be changing in plain sight, outside the visibility of most planning systems.

Size curves, long treated as a stable planning input, are beginning to drift. Not dramatically, but consistently. Smaller sizes are gaining share. Traditional “core” sizes are losing their reliability. Fit-related returns are rising in otherwise healthy categories. These patterns don’t trigger alarms, but they quietly distort forecasts, allocations, and replenishment decisions across the business.

New research from Impact Analytics shows how these subtle shifts can scale into a significant financial issue, with more than $5 billion in inventory value and margin potentially at risk if planning systems remain anchored to historical size assumptions. This blog explores what’s behind the shift, why it matters now, and how retail leaders can respond before a gradual drift turns into a measurable performance.

Size Planning Assumption Retail Never Had to Question

Most retail planning challenges, such as demand shifts, trends, weather disruptions, or underperforming promotions, are familiar. These are variables the industry expects and has built entire systems to manage. Size curves were treated differently.

They sat in the background of planning, quietly shaping buys, allocations, and replenishment logic. While retailers continuously recalibrated forecasts for color, silhouette, and price sensitivity, size distributions were typically adjusted gradually, based on historical selling patterns that were assumed to be directionally stable.

Assuming that the overall distribution of customer body sizes moves slowly, rarely surfaced as a risk discussion. It was embedded in the mechanics of traditional planning.

Size Curves Became Planning Infrastructure

Size distributions were viewed as relatively stable; they became foundational inputs across multiple retail functions:

• Seasonal buy quantities were spread using historical size ratios
• Allocation systems distribute inventory based on store-level curve history
• Replenishment logic assumed predictable size-level velocity
• Financial plans baked in expected sell-through by size band

Over time, this turned size curves into operational infrastructure, and not a dynamic variable requiring frequent strategic reassessment.

Planning Systems Were Built to Track Preference Shifts, Not Body Shifts

Retail forecasting models grew increasingly sophisticated in sensing changes in what customers prefer to wear. They monitor trend signals, pricing elasticity, regional differences, and promotional response with growing precision.

They were not designed to detect the customer's size curves. That distinction matters because preference shifts tend to be seasonal and reversible. Structural shifts in body composition, by contrast, alter the baseline of demand itself. The Impact Analytics data shows multi-point size mix shifts occurring within a single two-year window, a pace much faster than most retailers recalibrate their size curves, which are often updated only seasonally or annually. When that baseline moves, historical size curves stop being a neutral reference point and start becoming a source of distortion.

When a Stable Input Starts Moving

The Impact Analytics report shows that size-level demand is already drifting toward smaller bands across multiple categories, while larger sizes are losing share in ways that extend beyond normal fluctuation. 

When the input, assumed to be stable, begins to move, the rest of the planning system lags.

This is where the risk compounds:

• Buys reflect yesterday’s distribution
• Allocations amplify the imbalance
• Replenishment reinforces the wrong depth
• Markdown and return pressure follow

What makes the current moment different is not simply that the size curves are changing. It is that they are changing faster than the planning infrastructure built around them was designed to adapt.

That raises a new kind of question for retail leaders, not about trend forecasting, but about whether one of the industry’s most trusted planning inputs is now the least examined.

Why GLP-1s Have Become a Retail Variable

Retail is accustomed to external forces reshaping demand. Economic cycles influence discretionary spending. Migration patterns affect regional assortments. Social platforms accelerate trend adoption. What is different about the current moment is that a key demand signal is emerging from a less familiar source: measurable changes in consumer body composition.

GLP-1–based medications, initially developed for diabetes treatment and now widely used for weight management, have reached adoption levels that make their downstream effects commercially relevant. When a health-driven shift begins altering the physical measurements of a meaningful share of consumers, apparel demand does not just fluctuate; it redistributes across size curves.

More than 12% of U.S. adults have used GLP-1 medications, with roughly 6% currently active, a scale large enough to influence national demand patterns in size-sensitive categories.

The significance for retail lies in the speed of change. Planning systems are designed around seasonal cycles and gradual demographic evolution. GLP-1 adoption compresses that timeline. Customers can move between size bands within a single planning horizon, while inventory commitments are still anchored to historical distributions.

This becomes even more commercially relevant when considering who is driving early adoption. In early acceleration markets, GLP-1 prescriptions have skewed heavily toward women, the same demographic that drives a substantial share of apparel purchasing and trend influence.

The result is not an immediate collapse of size curves, but a steady directional drift. A few percentage points of sustained movement toward smaller sizes can meaningfully alter sell-through patterns, fit behavior, and replenishment performance.

Retail has seen wellness movements before, but most influenced intent more than measurement. GLP-1s differ because they are medically prescribed, designed for sustained use, and associated with clinically meaningful weight changes. That makes the resulting demand shift less seasonal and more structural.

What This Means Operationally
Even modest, sustained movement in size demand can lead to:

• Slower turns in larger sizes that were previously “core.”
• Increased fit-related returns as customers transition between sizes.
• Replenishment logic is reinforcing outdated curve assumptions.

What Retailers Are Starting to Notice in the Curve

The shift in size curves is not showing up as a headline disruption. It is surfacing through small but persistent operational signals, the kind that merchants, planners, and allocation teams feel before they formally name the pattern. When viewed together, these signals point to a demand profile that is gradually moving away from the one historical planning models were built around.

Retailers are beginning to observe:

• Size-level performance that no longer follows historical norms, even in seasons where overall category demand appears healthy, making forecasting accuracy harder to maintain.
• Stronger, more consistent sell-through in smaller size bands, while larger sizes increasingly require markdowns or extended selling time to achieve comparable movement
• Traditional “core” sizes are losing their reliability as anchors of the curve, forcing planners to rethink which sizes truly represent the center of demand.
• Store assortments that look balanced on paper but behave unevenly in practice, with certain sizes selling out early and others lingering longer than expected.
• Replenishment systems are reinforcing outdated curve assumptions, as automated flows chase last year’s velocity patterns rather than emerging ones.
• More size experimentation in digital channels, where customers order multiple sizes or exchange more frequently as their fit changes, adds friction to e-commerce performance.
• A pattern that feels gradual and manageable in any single season, yet becomes strategically significant when the same directional drift repeats across multiple buy cycles.

The outcome is not an obvious assortment failure. It is a growing misalignment between inventory and a customer base whose size distribution is evolving in real time — subtly at first, but persistently enough to reshape performance over time.

Returns Are Signaling a Deeper Fit Misalignment

Long before size-curve shifts become obvious in inventory reports, they tend to surface in returns. When customers’ body profiles begin to change, purchasing behavior becomes less predictable. Shoppers order the size they are used to, find the fit different, and re-enter the system through exchanges and returns. What appears at first as routine fluctuation can signal something more structural: inventory planned around historical size curves meeting a customer base that is gradually moving away from those assumptions.

The Impact Analytics report shows that retailers have seen a growing return state, for example: in 2022, the women's bottoms had 12.3% returns, which increased to 15.2% in 2024. These returns do more than create operational friction; they distort demand signals, increase reverse logistics costs, and push inventory back into the system outside its optimal selling window. Sales may still look stable on the surface, but margin pressure quietly builds underneath.

By the time size imbalance is clearly visible in aged inventory or markdown performance, returns have often been signaling the misalignment for several seasons. The data was there early; it just required interpreting returns not as noise, but as a leading indicator of size-curve change.

From Size-Curve Drift to Financial Exposure

When size curves shift, the first signs appear operational. The consequences, however, show up in financial performance.

Where The Pressure Builds

• Inventory accumulates in the wrong size bands, tying up working capital in units that move more slowly than planned.
• Full-price sales are missed in faster-moving sizes, where depth no longer matches demand.
• Markdown reliance increases, as excess inventory in underperforming size ranges requires promotional activity to clear.
• Overall inventory productivity declines, with more capital required to generate the same level of sales.

These effects build gradually. Each planning cycle adds inventory based on historical size curves that no longer reflect current demand. The imbalance compounds rather than corrects itself.

At scale, even a small percentage shift in size demand becomes financially material. When millions of units are bought using outdated curve assumptions, the mismatch quickly reaches 400 million industry-wide. That translates into billions of dollars tied up in inventory that does not align with where demand has moved, not because consumers stopped spending, but because planning inputs failed to keep pace. 

The Scale Of The Shift

Size curves are no longer stable planning inputs; they are becoming active business variables.

Even modest shifts in size demand can scale into major financial exposure. Industry projections indicate that more than 400 million apparel units could be misaligned with demand in the coming years, representing roughly $5 billion in inventory and margin at risk. This isn’t about demand slowing. It’s about inventory being deployed against an outdated view of the customer.

That changes the role of size planning. What was once a background assumption is now a lever for margin protection, working capital efficiency, and sell-through performance.

Retailers that continue planning against historical size curves will manage the consequences reactively, through returns, markdowns, and inventory drag. Retailers that treat size curves as dynamic demand signals can rebalance earlier, protect margin, and stay aligned with how their customers are actually changing.

The shift is already in motion. The next advantage belongs to retailers whose planning systems can keep up. Explore the full Impact Analytics report to see how size curves are evolving, and how AI-native planning can help you act before imbalance turns into margin loss.