How Data Is Used To Predict the Next Big Colours in Fashion
See how data is used to predict the next big colours in fashion, turning social signals and market insights into winning palettes for retailers and apparel brands.
In fashion, colour is everything. It defines a season, tells a story, sparks emotion and often determines what sells. But have you ever wondered how brands seem to “know” which shades will trend before they’re everywhere? The answer isn’t guesswork or a crystal ball, it’s data.
From catwalks to consumer clicks, the fashion world is harnessing data science and AI to predict the next big colour waves, turning trend forecasting into a precise, tech-powered art.
The Colour of Data: Where It All Begins
The prediction process starts by collecting data from a wide array of sources, including:
- Fashion Week Runways: By analyzing colour palettes across global runway shows, AI can detect emerging patterns in designer collections.
- Retailer Assortments: Monitoring which colours are increasing in product lines across e-commerce platforms helps forecast commercial trends.
- Social Media & Influencers: Trending colours in photos, hashtags, and influencer content provide early clues about consumer preferences.
- Search and Sales Data: What shoppers search for or buy (e.g., “sage green pants” or “lilac dresses”) gives direct insight into demand.
- Street Style & User-Generated Content: Everyday fashion choices offer grassroots-level data on what’s gaining momentum.
How AI and Machine Learning Decode Colour Trends
AI acts like a digital merchandiser, scanning thousands of images and descriptions across platforms to detect recurring colour themes and shifts in consumer attention. Once the data is collected, algorithms come into play to:
1. Cluster and Classify Shades
AI breaks down images to detect and classify exact colour codes (like Pantone or HEX values), identifying common tones across thousands of visuals.
2. Track Shifts Over Time
By comparing colour popularity season over season, data models spot which shades are gaining traction—and which are fading out.
3. Correlate Colour with Context
Advanced tools can link specific colours to product types, materials, or moods (e.g., “dusty pink in loungewear” or “bright orange for festival wear”).
4. Predict What’s Next
Using historical patterns and real-time signals, predictive models estimate what colours are poised to rise in popularity next season—often months ahead of the curve.
Why Colour Forecasting Matters for Brands
Accurate colour predictions are a huge competitive advantage. Here’s why:
- Design Alignment: Creative teams use colour forecasts to design collections that resonate when they drop—not after trends have peaked.
- Inventory Confidence: With better colour forecasting, brands avoid overproducing in off-trend hues that won’t sell.
- Marketing Sync: From lookbooks to ads, brands can tailor visuals and messaging around colours consumers are already leaning toward.
- Faster Trend Response: Retailers using real-time data can capitalize on fast-moving colour trends before the competition catches up.
Conclusion
From tech-runways to TikTok, the next big colours aren’t chosen, they’re detected. As fashion embraces analytics, colour forecasting is no longer just an art, it’s a science, and it’s painting a vibrant future for brands bold enough to use it.
About Woven Insights
Woven Insights is a comprehensive market analytics solution that provides fashion brands with real-time access to retail market and consumer insights, sourced from over 70 million real shoppers and 20 million analyzed fashion products. Our platform helps brands track market trends, assess competitor performance, and refine product strategies with precision.
Woven Insights provides you with all the actionable data you need to create fashion products that are truly market-ready and consumer-aligned.
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