Size and color analytics: what variant data reveals

Variants are different products

A blue dress in size small and the same dress in red size large might share a product page, but they’re different products in terms of demand and performance. Aggregate product-level metrics hide these differences.

Analyzing variant-level data reveals insights about customer preferences, inventory decisions, and merchandising opportunities that product-level analysis misses.

Color performance varies dramatically

The same product in different colors can have wildly different performance.

The pattern:

Black and neutral colors often convert at 2-3x the rate of bright or unusual colors. A product showing 2% aggregate conversion might have black at 3.5% and yellow at 0.8%.

What this means:

Aggregate conversion understates how well your core colors perform and overstates how well statement colors perform. Buying decisions based on aggregate product performance miss these important distinctions.

Track conversion by color at the variant level. Use this data to inform future color range decisions.

Color and price interaction

Color preferences shift at different price points.

The pattern:

At lower price points, customers experiment more with color. The risk of a bold choice is lower. At higher price points, customers choose safer options—black, navy, neutrals.

Track color preference by price tier. Your $30 tops might sell well in bright colors. Your $150 blazers might need to emphasize neutrals.

Size demand patterns

Size demand isn’t evenly distributed. Understanding your demand curve helps with inventory planning.

The typical curve:

Most apparel brands see demand concentrated in middle sizes—M, L, and common numeric sizes. XS and XXL have lower demand but still serve important customers.

Your specific curve:

Your customer base might differ from averages. If you’ve built a brand around inclusive sizing, your size demand curve will look different from traditional brands.

Track size demand at aggregate and category levels. Use this to set inventory allocation by size.

Size and conversion rate

Conversion rate varies by size, often for unexpected reasons.

The availability effect:

Popular sizes sell out faster, reducing their conversion rate (customers want them but can’t buy). Less popular sizes stay in stock, maintaining availability but often showing lower demand.

The fit confidence effect:

Customers at size extremes might have less fit confidence, leading to lower conversion or higher returns. Size-inclusive messaging and fit details can help.

Track conversion by size, adjusted for availability. A size with 50% availability and 2% conversion is actually outperforming a size with 100% availability and 2.5% conversion.

Size and return rate

Return patterns vary by size and reveal fit issues.

Size-specific return rates:

If certain sizes have dramatically higher return rates, the product might not fit that size range well. This is valuable feedback for product development.

Track returns by size for each product. Products with size-specific return spikes might need fit adjustments or better size guidance for those sizes.

Exchange patterns:

Track what sizes are exchanged for what. If medium consistently exchanges to large, your sizing might run small. This pattern reveals sizing accuracy issues.

Variant-level view patterns

How customers browse variants reveals preferences before purchase.

View-to-purchase ratio by variant:

If a color gets many views but few purchases, something blocks conversion. It attracts interest but doesn’t close. This might indicate the color photographs well but doesn’t meet expectations in person.

Track variant views and calculate view-to-purchase conversion at the variant level. Outliers in either direction are worth investigating.

First variant viewed:

Which variant shows first on the product page affects which variant gets viewed most. Track whether first-position variants convert better, and test whether changing the default variant improves results.

Color family analysis

Grouping colors into families reveals higher-level preferences.

Family performance:

Instead of tracking individual colors (navy, cobalt, royal blue), group into families (blues). This reveals whether customers prefer warm versus cool tones, neutrals versus colors, earth tones versus jewel tones.

Use color family insights for range planning. If blues consistently outperform greens across products, adjust your color mix accordingly.

Seasonal variant shifts

Color and size preferences shift by season.

Color seasonality:

Pastels peak in spring. Jewel tones peak in fall. White is summer. Black is year-round but peaks in winter. Track color performance by season to optimize seasonal inventory.

Size seasonality:

Some brands see size demand shift slightly by season—smaller sizes might peak before summer as customers shop for warm weather. Track whether your size demand has seasonal patterns.

Variant stock-out impact

When key variants sell out, the entire product suffers.

The halo effect:

When the most popular color sells out, overall product conversion drops even for available colors. Customers who came for the popular color leave rather than substitute.

Track product-level conversion alongside variant availability. Quantify how much overall conversion drops when key variants stock out.

Merchandising implications

Variant data should inform how you present products.

Hero color selection:

Which color shows in category pages and ads affects click-through. Use variant data to select the best-performing color as the hero image.

Variant sort order:

Present best-converting variants first in the selection options. Don’t bury your best colors at the end of a long list.

Using variant data for buying

Variant analytics directly informs inventory buying decisions.

Color depth decisions:

For a new product, how many colors should you buy? Variant data from similar products tells you how color breadth affects performance.

Size allocation:

Historical size demand curves help you allocate inventory appropriately. Don’t overbuy XS because you assume even distribution.

Reorder priorities:

When products need restocking, variant data tells you which colors and sizes to prioritize. Restock the variants that actually drove sales.

Metrics to track for variant analysis

Focus on these variant-specific metrics:

Conversion rate by color and size. Return rate by size. View-to-purchase ratio by variant. Color family performance patterns. Size demand distribution. Variant stock-out impact on product conversion. Exchange patterns between sizes. Seasonal shifts in variant preference.

Product-level analytics miss the variant-specific insights that drive better buying, merchandising, and product development decisions. Build variant-level analysis into your regular analytics routine.