How Do Beauty Brands Decide Which Foundation Shades to Launch?

Foundation shade selection involves market analysis, demographic targeting, and manufacturing constraints rather than pure inclusivity ideals. According to [NewBeauty](https://www.newbeauty.com/foundation-shades-inclusivity/), brands like ColourPop work on shades for over a year using focus groups and multiple shade matching exercises, while Huda Beauty spent two years shade-matching hundreds of people. Manufacturing minimums often require 5,000 to 10,000 units per shade according to Arise Cosmetic, making low-demand shades economically risky. This creates the gap between theoretical inclusivity and actual shelf availability. On-demand manufacturing removes these constraints by eliminating minimum orders and forecasting requirements.

Brands study census data, customer surveys, and regional sales patterns to estimate demand across skin tone spectrums. According to Umbrex, companies catalog existing shade ranges, analyze distribution across fair to deep categories, and assess sales by shade to understand performance. They identify concentrations of potential customers in specific depth ranges and prioritize shades where demand concentrates.

This analysis often reveals that some shades would have insufficient buyers to justify production. Arbelle found that over one-third of beauty consumers still struggle to find foundation matches, with over 60% of shades clustered around mid-tones while very light and very deep tones remain underrepresented. A shade needed by only 2% of the population may not generate enough sales to cover manufacturing and inventory costs under batch production.

Contract manufacturers require minimum order quantities per shade that represent significant capital commitment. According to Arise Cosmetic, large manufacturers typically require around 5,000 units for premium products, while GideaPac notes that typical MOQs range from 5,000 to 20,000 units depending on packaging customization. If a shade does not sell, the entire production becomes dead inventory.

Brands respond by clustering shades. If data shows demand in the deep range, they may offer three deep shades instead of ten, hoping to capture the range with fewer SKUs. Arbelle's research shows this creates redundancy instead of representation; some brands offer 20+ shades but cover only 4 out of 10 Monk Skin Tone scale tones. This creates the "olive gap" or "deep range compression" where specific undertones within a depth range are unavailable.

Retailers influence shade selection through shelf space allocation. According to Berg Export, shelf space is valuable, competition is intense, and retailers prioritize products with strong margins and low return rates. ScienceDirect research on omnichannel retail notes that in brick-and-mortar stores, limited shelf space requires careful product selection; offering more diverse products limits space available per product.

They may reject shades they believe will not turn quickly in their specific locations. This creates geographic variation in shade availability that frustrates consumers in less diverse markets seeking inclusive ranges. Dotactiv explains that deep assortment strategies require building strong supplier relations and using data analytics to optimize stock levels, but retailers must balance breadth with inventory turn.

On-demand manufacturing removes minimum order constraints. Arise Cosmetic notes that high-tech flexible production lines now enable MOQs as low as 500 units to promote inclusivity. Six base pigments create millions of shades without separate forecasting for each. Brands can serve the full spectrum without betting on individual shade demand.

This enables true inclusivity without economic compromise. Arbelle found that 50% of consumers choose brands that champion diversity, and inclusive brands grow 1.5 times faster than less inclusive competitors. For brands seeking to expand shade ranges without assuming inventory risk, infrastructure partnerships provide on-demand manufacturing capabilities that eliminate MOQ barriers and forecasting requirements.