Information is Reshaping the style Business LONDON, Uk ?a Clearance sales point to a perennial problem in the fashion industry: the misalignment of demand and supply. Using traditional market research, brands and retailers cannot predict with high accuracy what products consumers will in reality purchase during a season. As a result, manufactured goods doesn?ˉt sell is marked down, while interest in popular burberry factory outlet goes unmet, resulting in significant loss of income. But better aligning supply and demand 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, the 2009 summer, raised a $1.Six million round of seed funding led by Index Ventures, investors in Net-a-Porter, Etsy and ASOS ?a is offering a realtime data monitoring and analytics platform that makes commercial decision-making within the fashion industry more scientific. Crawling fashion retail sites, monitoring consumer opinions on social media and analysing output from key industry events, the woking platform blends machine-learning with human editing to show huge amounts of raw data, captured in realtime, in to the type of actionable information that can give brands and retailers an aggressive edge when making decisions like placing orders, determining pricing and managing merchandising. BoF spoke with the founders of Editd, Geoff Watts and Julia Fowler, to find out more about how exactly data-driven intelligence is revolutionising retail and reshaping the company of fashion. BoF: What?ˉs wrong using the way most fashion forecasting works today? GW: A tangible insufficient data and fake burberry bags, as well as the collapse of seasonal fashion is putting a lot of pressure in route the industry works today. Most businesses have sales reporting or business intelligence to know what is selling, so they already view the value of data at a trading level. This sales data coupled with a great understanding of their customer, inspiration from trend services or their own research is the things they use to create an informed guess about where things are going. But even the geniuses can?ˉt get 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 is the market ?a even about the top end. Many brands that work with us are doing 10 or more drops annually, so although the weather conditions are seasonal, fashion is constantly variable. People expect to see new garments on every trip to a store and also the production capacity can there be to make it happen. Traditional forecasting isn?ˉt a good fit when production can be so close to the market. BoF: Just how can technology get this to process more scientific? GW: The cleverest businesses can know exactly what their customers want by utilizing technology. You can measure consumers and also the entire trading environment. Customers go to town constantly online either through Twitter, on the blog, clicking a ??Like?ˉ button, adding a product to a basket, or buying something. The retail market is measurable ?a there?ˉs never been more accurate, factual information on exactly what?ˉs happening in realtime than now. It?ˉs an incredible strategic advantage. However the breadth of information available is simply too ideal for people to process and synthesise into actionable information. That?ˉs why we developed Editd. BoF: Last year, researchers learned that they could predict, with astonishing accuracy, how well a film would sell in its first couple of weekends by analysing mentions on Twitter. Can a similar analysis of realtime social data accurately predict interest in fashion products? JF: Definitely. Though fashion is much more nuanced than movie releases. People express opinions about fashion constantly ?a we have more than 100 million opinions sourced over the past 12 months specifically on individual garments, fabrics, prints and styles. 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 within our data. Making calls on short-term trends based on information is tremendously valuable as well. The ability to know if coloured denim, or leopard print will endure for the following 3 months is essential. BoF: What kinds of data should fashion brands be monitoring to generate probably the most accurate predictions? GW: Brands should become familiar with their competition and also the nordstrom burberry cashmere scarf. Your own sales reporting can?ˉt tell you 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 a remarkably 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 that, virtually all summer apparel was on sale and visual merchandising centred around coats and knitwear. It?ˉs an ideal example of lost profit opportunities. BoF: In 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 is the right mix? GW: Some decisions is going to be handed off to technology, like when you should discount, replenish, or what quantities to order. Computing can never replace human creativity, but designers and buyers must always keep their eye about the data ?a there?ˉs anything satisfying than creating a best-seller. BoF: Who is doing this well today? JF: Burberry are a good example. They have strong creative direction while blurring the road between being a technology along with a fashion company. There?ˉs without doubt that they?ˉve directly interacted with their customers, understand social and can interpret the entire market. They have short-circuited the risk of production and holding inventory by introducing capsule collections, taking pre-orders before garments are produced, and having iPads in stores to see and order stock that?ˉs not held on-site. Having that much data and being that close to their clients makes traditional forecasting irrelevant. BoF: The way the rise of data-driven intelligence change the fashion industry within the a long time? GW: One of the biggest wins is to reduce wastage, that is an epidemic in the fashion business. We?ˉre excited about the creative benefits too. With production capacity evolving because it is and the ability to understand consumers, we think it won?ˉt be well before the style industry can be more experimental and fewer homogenised, while still being profitable.