Data Drives Fashion

Source: Business of Fashion, Aug 2011

Clearance sales point to a perennial problem in the fashion industry: the misalignment of supply and demand. Using traditional market research, brands and retailers are unable to predict with high accuracy what products consumers will actually purchase during any given season. As a result, merchandise that doesn’t sell is marked down, while demand for popular items goes unmet, leading to significant loss of income.

But better aligning supply and demand is a complex matter. That’s because, in trend-driven product categories like fashion, historical sales data never results in consistently better commercial decisions. What brands and retailers really require is information about what’s going to happen, not what’s already happened. But traditional fashion forecasting tools like panel-based research and trend reports are slow and unscientific, leaving buyers and merchants to make important business decisions based largely on intuition.

Crawling fashion retail sites, monitoring consumer opinions on social media and analysing output from key industry events, the platform blends machine-learning with human editing to turn vast amounts of raw data, captured in realtime, into the kind of actionable information that can give brands and retailers a competitive edge when making decisions like placing orders, determining pricing and managing merchandising.

The cleverest businesses can know exactly what their customers want by using technology. You can measure consumers and the entire trading environment. Customers express themselves constantly online either through Twitter, on their blog, clicking a ‘Like’ button, adding a product to a basket, or buying something. The retail market is measurable

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