Blog
Summary
Whitespace analysis is the difference between reacting to the market and shaping it. It’s what helps retailers uncover unmet demand, identify oversaturated spaces, and build assortments that perform. But to do it well, you need more than just a snapshot of your competitors — you need complete data and the flexibility to explore it from multiple angles.
True whitespace insight comes from seeing the whole picture: how your mix compares to others, what’s trending by fabric or color, how price strategies differ, and how timing plays into performance. Retailers who can look at data dynamically move faster, reduce risk, and capture more opportunity.
The best whitespace analysis starts with smart benchmarking. It’s not just about how you perform next to your direct competitors – it’s also about the brands you aspire to compete with. By analyzing both sets, retailers can identify where they have room to stretch their brand, elevate positioning, or close assortment gaps.
It’s equally valuable to compare across markets and regions. A product category or colorway that performs in one country may lag in another. Having the flexibility to view localized performance uncovers regional preferences and allows teams to fine-tune assortments by geography, rather than applying a one-size-fits-all strategy.
Timing is everything in retail. The ability to look at performance through multiple timeframes, whether short-term for in-season adjustments or long-term for future range planning is essential.
Near-term analysis helps teams make quick, high-impact decisions like increasing depth in bestsellers or phasing out underperforming colors. Broader time views, such as comparing across multiple seasons or fiscal years, reveal evolving trends and cyclical patterns. This dual view helps retailers act fast today while planning smarter for tomorrow.
Retail success often comes down to the details. Being able to analyze data by fabric type, blend, color mix, or pattern helps retailers understand what’s truly resonating with customers.
A surge in technical fabrics, for example, might signal a shift in consumer preference that’s still in early stages. Or color analysis could reveal that competitors are leaning heavily into certain tones while others remain underrepresented — an opening to differentiate. The ability to slice and filter data this way helps teams pinpoint microtrends before they hit the mainstream.
Whitespace isn’t just about product mix — it’s also about pricing strategy. Comparing how competitors price within categories helps identify where there’s opportunity to expand or reposition.
Are you missing key price buckets? Are competitors selling out full-price items where you’re relying on markdowns? Evaluating good, better, best price structures across competitors provides a clearer view of where your brand can win, both in perception and profitability.
Even the most experienced teams can miss patterns hidden within massive datasets. This is where AI becomes a competitive advantage. AI can highlight unexpected signals — a rise in certain color families, a new pattern combination gaining traction, or a subtle shift in phasings and sell-outs — insights that might not be on your radar yet.
By surfacing what’s unseen, AI helps teams stay proactive rather than reactive, uncovering whitespace opportunities before they’re visible to the broader market.
To uncover real whitespace, you need complete, connected data and the flexibility to explore it from every angle. EDITED brings together more than 14 years of market data, covering over 5,000 retailers and billions of products, all within a platform designed for deep, customizable analysis.
From competitive benchmarking and multi-season views to filtering by fabrication, color, or price point — EDITED gives retailers the clarity to move faster and make more confident, data-driven decisions. And with AI-powered insights built in, you can identify emerging trends before they take off.
