Elevating Retail, Apparel, and Footwear Industries with Advanced Size Curve Analysis

Introduction

A deep understanding of consumer behavior and preferences is a crucial differentiator and a key driver of profitability in the fiercely competitive apparel and footwear industry. Ideally, retailers would maintain a perfect assortment and size mix at every distribution center and store, but the reality of managing countless SKUs across many locations makes it almost impossible to achieve. This mismatch contributes to an annual $1 trillion loss globally. (Source - IHL)

In retail, "broken sizes" refer to incomplete size ranges when in-demand sizes sell out. Retailers initially offer a full-size spectrum of options to ensure a comprehensive collection, but in-demand sizes sell out as seasons progress, leading to disappointed customers and lost sales. Retailers and brands mitigate losses by consolidating unsold sizes in specific stores and offering end-of-season discounts, but accurately predicting a sell-out size curve through manual means seems unthinkable.

Moreover, the rising use of weight loss drugs like Ozempic may shift size profiles to smaller sizes, challenging apparel retailers to dynamically update size profiles, plan size distributions effectively and enhance supply chain flexibility to match evolving consumer body shapes and demands.

Is there a more effective method for handling these complexities? This article explores automation in size curve analysis, its importance, operational insights, and its impact on business performance.

What is a Size Profiler?

Size profiling evaluates the contribution of each product size (S, M, L, etc.) at every store by transforming historical sales data into actionable intelligence at the size level. This process facilitates smart buying and allocation, ensuring shelves are stocked with the right sizes to meet specific customer needs. It enhances assortment productivity, countering the ineffective ‘one-size-fits-all’ approach in apparel and footwear.

As depicted in the image below, size curves for the same t-shirt can vary significantly between regions due to differences in build, lifestyle, age, and demographics. For instance, populations in Minnesota (North), Texas (South), and California (West Coast) exhibit unique physical traits shaped by climate and lifestyle, influencing their eating habits and preferences for clothing fits, such as snug versus loose. Using a uniform size curve can create sales imbalances and inventory shortages at the point of sale, resulting in customer dissatisfaction. Size profilers help retailers tailor size ratios for each location: optimizing inventory, reducing markdowns and stockouts, and enhancing revenue, margins, and customer satisfaction.

Why Size Profiling matters

Retailers and manufacturers used generic sizing in the past, leading to inventory-demand mismatches. Advanced data analytics and consumer insights make size curve analysis a game-changer in the retail industry. Size profiling presents a unique challenge in fashion retail because demand for specific sizes is non-transferable and cannot be substituted. Overstocking and under-stocking across sizes can lead to excess unsold inventory or potential sales loss. For example, size profiling is vital for fashion retailers like Nordstrom, Levi Strauss & Co, and Adidas, where precise sizing impacts customer satisfaction and sales, unlike Walmart, where size variation in FMCG products is less critical.

By using a size profiler, retailers gain the following advantages:

1. Increased sales and minimized stock-outs — The size profiler helps allocate purchase quantities by size, optimizes SKU orders, and improves inventory planning and replenishment, thus reducing stockouts and boosting sales.
2. Increased margins and reduced markdowns — Size profiler aligns store-size inventory with demand, increasing full-price sales and margins while reducing trapped inventory and markdowns in stores and distribution centers.
3. Increased efficiency — Automating the processes of size curve generation, store clustering, and pack optimization boosts assortment productivity and efficiency, reducing the need for manual effort.

How can we ensure consistent availability of key sizes across all products and channels?

Effective size profiling requires detailed data that reflects variations by region, channel, and product type, shaped by demographic and customer preferences. Analyzing historical sales enables manufacturers to predict high-demand and fringe sizes accurately. This helps prevent the underproduction of popular sizes while allowing them to reduce, discontinue, or review the inventory distribution of underperforming sizes across locations and demographics.

For instance, a women's shoe size 4 is less common in Texas, USA, where most consumers wear sizes 6 to 10. The size would be labeled as fringe for this location, requiring careful management to avoid excess, versus a location such as Singapore where the same size would be core. This precision in size forecasting helps reduce tooling costs in the footwear industry, optimizing inventory and reducing waste. This strategy boosts sales potential by aligning inventory with actual market demand.

An optimized approach to size profiling should include three distinct stages:

Historical analysis

Generating statistically optimized size profiles at the right level of granularity, often category, class, sub-class, or style.

1. Aggregate and analyze large volumes of data: Gathering granular store-size data creates complex, large datasets that are challenging to analyze for decision-making. Past sales are grouped by attributes like brand, color, and fit to ensure adequate sales volume.
2. Create multiple profiles pre-approval: Planners set aggregation levels for historical sales data, creating multiple profiles from detailed product attribute combinations to optimize size ratio calculations while preserving regional and class variability.
3. Rectify sales data: Historical sales are adjusted to estimate lost sales during the observed store, partner, or distribution center stockouts.

Profile application

Linking individual products to a size profile, disaggregation of colorway-level forecasts, and the potential time-phasing of profile application.

1. Size profile generation: Products are aligned with their aggregation group based on historical sales, and a size profile is formulated to determine the allocation of styles and sizes to specific stores.
2. Time-phased profiling: Replenish to optimized size ranges throughout the selling season to avoid broken size runs and excess fringe sizes.

Seasonal post-game & adjustment

Size profiles should be periodically refined and re-leveled after every selling season to accommodate changing trends and consumer preferences.

Conclusion

Retailers and Apparel/Footwear Brands analyze size distribution patterns to spot popular sizes, seasonal preferences, and trends, guiding them to make huge investments in their inventory and targeted marketing. Implementing size curve analysis uses advanced data analytics to produce detailed size insights and align inventory with customer demand. A retailer utilizing a size profiler can get input for the following key processes:

• Size level forecasting — Inventory accurately matches customer needs at specific size levels using historical sales data.
• SKU-location level buys — It analyzes location-level demand to optimize purchase quantities at SKU levels.
• Optimized allocation — Enhances inventory planning and replenishment by aligning location-specific size allocations with demand.
• Tailored pre-packs — It customizes size distribution and pre-packs for store clusters and wholesale segments, aligning with local sales patterns and improving merchandise quality.

In conclusion, size curve analysis is crucial for retailers and brands aiming to excel in a dynamic and competitive market. The o9 Platform can aid in meticulously analyzing size-wise sales data, rectifying lost sales data, creating multiple size profiles based on location and product attributes, and approving the optimal size curve. This helps retailers and brands derive deep insights, enhance decision-making, optimize inventory, and boost customer satisfaction.

As retail evolves, integrating size curve analysis into assortment planning and pack optimization gives retailers a strategic advantage in meeting consumer demands and fostering sustainable growth. This automation enhances planner efficiency and streamlines the process of planning and approving diverse size profiles, positioning size curve analysis as a transformative tool across the industry.

For more on size profile applications in Retail, Apparel, and Footwear Demand Planning, see the related eBook, “Fashion Forward: Demand Planning Challenges in the Apparel, Footwear, & Luxury Industries”.

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