Information is Reshaping the Fashion Business LONDON, Uk ?a Clearance sales indicate a perennial issue in the fashion industry: the misalignment of supply and demand. Using traditional researching the market, brands and retailers are unable to predict rich in accuracy what products consumers will actually purchase during a season. Consequently, merchandise that doesn?ˉt sell is burberry sale coats, while interest in popular items goes unmet, resulting in significant damages. But better aligning demand and supply is a complex matter. That?ˉs because, in trend-driven product categories like fashion, historical sales data never leads to consistently better commercial decisions. What brands and retailers really require is details about what?ˉs going to take place, not what?ˉs 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. Now, an ambitious London-based startup called Editd ?a which, earlier this summer, raised a $1.6 million round of seed funding led by Index Ventures, investors in Net-a-Porter, Etsy and burberry nordstrom a is offering a realtime data monitoring and analytics platform which makes commercial decision-making in the fashion industry more scientific. Crawling fashion online stores, monitoring consumer opinions on social networking and analysing output from key industry events, the platform blends machine-learning with human editing to show huge amounts of raw data, captured in realtime, into the kind of actionable information that may give brands and retailers a competitive edge when creating decisions like placing orders, determining pricing and managing merchandising. BoF spoke using the founders of Editd, Geoff Watts and Julia Fowler, to find out more about how data-driven intelligence is revolutionising retail and reshaping the business of favor. BoF: What?ˉs wrong with the way most fashion forecasting works today? GW: A tangible insufficient data and facts, plus the collapse of seasonal fashion is placing a large amount of pressure on the way the works today. Most businesses have sales reporting or business intelligence to know what is selling, so that they already understand the worth of data in a trading level. This sales data combined with an excellent knowledge of their customer, inspiration from trend services or their own scientific studies are what they use to create an informed guess about where situations are going. But the geniuses can?ˉt have it 100 percent right ?a otherwise clearance sales wouldn?ˉt exist because everything would sell through! JF: Seasonal fashion is dead and speed-to-market now's the market ?a even about the high end. Many brands that actually work around are doing 10 or even more drops a year, so even though weather is seasonal, fashion is constantly variable. People be prepared to see new garments on every trip to a store and also the production capacity can there be to get it done. Traditional forecasting isn?ˉt a good fit when production can be so close to the market. BoF: Just how can technology make this process more scientific? GW: The cleverest businesses can know exactly what their clients 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?ˉ button, adding a product to a basket, or buying something. The retail marketplace is measurable ?a there?ˉs never been more accurate, factual information on exactly what?ˉs happening in realtime than now. It?ˉs an amazing strategic advantage. However the breadth of information available is too great for people to process and synthesise into actionable information. That?ˉs why we developed Editd. BoF: Last year, researchers learned that they might predict, with astonishing accuracy, how well a film would sell in its initial few weekends by analysing mentions on Twitter. Can an identical analysis of realtime social data accurately predict demand for fashion products? JF: Definitely. Though fashion is much more nuanced than movie releases. People express opinions about fashion constantly ?a we now have a lot more than 100 million opinions sourced over the past 12 months specifically on individual garments, fabrics, prints and designs. One great example is our data on the longevity of skinny jeans ?a a trend that endured considerably longer than traditional forecasting might have predicted. The demand curve was obvious in our data. Making calls on burberry jackets trends depending on information is tremendously valuable as well. A chance to know if coloured denim, or leopard print will endure for the following 3 months is vital. BoF: What kinds of data should fashion brands be monitoring to create the most accurate predictions? GW: Brands should become familiar with their competition and the full market. Your personal sales reporting can?ˉt let you know about something you never produced. Social data should be used at night marketing department; buyers and designers should understand what people are saying ?a it?ˉs an incredibly powerful channel. But good data is useless without good execution. Last week it had been 105 degrees in Manhattan and retailers had lots of notice. Despite the fact that, almost all summer apparel was for sale and visual merchandising centred around coats and knitwear. It?ˉs an ideal illustration of lost profit opportunities. BoF: Inside a product category as emotional as fashion, as to the degree should data drive design, buying and merchandising decisions? Can data-driven intelligence ever completely replace human intuition? What's the right mix? GW: Some decisions will be handed on technology, like when to discount, replenish, or what quantities to order. Computing can never replace human creativity, but designers and buyers should always keep their eye on the data ?a there?ˉs nothing more satisfying than creating a best-seller. BoF: Who is doing this well today? JF: Burberry are a great example. They have strong creative direction while blurring the road between being a technology and a fashion company. There?ˉs no doubt that they?ˉve directly interacted with their customers, understand social and may interpret the whole market. They've short-circuited the risk of production and holding inventory by introducing capsule collections, taking pre-orders before garments are produced, and achieving iPads in shops to view and order stock that?ˉs not held on-site. Having much data and being that near to their customers makes traditional forecasting irrelevant. BoF: How will the rise of data-driven intelligence alter the fashion industry in the a long time? GW: One of the greatest wins is to reduce wastage, which is an epidemic within the fashion business. We?ˉre excited about the creative benefits too. With production capacity evolving as it is and also the capability to understand consumers, we believe it won?ˉt be long before the style industry can be more experimental and less homogenised, yet still be profitable.
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