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纷纷红紫已成尘·布谷声中夏令新

山西财院78jitong 19781017--19820715

 
 
 

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78jitong.......................................................... 高三李五七弓长,三赵九刘七大王,阎吴谢孙崔氏双,柴米余侯箩万堂, 毛邓陈宋任申杭,曾肖徐翁程董梁,储曲祁解韦国强,男女七十学跟党。

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2016-05-08 09:04:36|  分类: 默认分类 |  标签: |举报 |字号 订阅

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Put your fashionable foot forward as Big Data meets Beauty

The retail sector in India is emerging as one of the largest sectors in the economy and is currently valued at US$ 600 billion. Of this about 90% is unorganized retail and only 10% is organised however the balance seems to be increasingly tilting towards the organised space. In fact a recent study by Google stated that by 2020 India is expected to generate $100 billion online retail revenue out of which $35 billion will be through fashion e-commerce. Liberalization of the FDI policy in retail sector will also provide further impetus to the entry of large international retailers, especially in the category of fashion and apparel.

The Indian Fashion Scenario

The fashion industry in India is rapidly evolving with a constant blur of new and enviable trends, patterns, designs and colors. Today, with millennials being the primary target market, these trends emerge and subside with mind boggling speed and preferences patterns change in the blink of an eye. Keeping up with these trends and being able to predict customer demand is the key prerequisite for fashion companies to thrive today.

For years, fashion companies have had previous data and intuition at its disposal to predict customer demands which is now however becoming irrelevant considering the fast-changing fashion trends and the tough competition in the market. More so, with more and more people getting brand conscious, it is becoming tougher for fashion companies to predict fashion trends on a real time basis. This is where Big Data steps in to save the day. 

Trend forecasting with Big Data

In true fashion industry style, bloggers and media alike have coined a different term for big data meeting beauty –“trend forecasting” is the term Wall Street Journal contributor Kathy Gordon used to describe information analysis. Big Data is all about turning extremely large quantities of data into useful information. When companies aggregate data and analyse them effectively, patterns emerge, ideas are born, and fashion companies become trend setters. In an industry where the success of next season’s collection hinges on picking the right patterns, colors, fabrics, shapes and sizes, Big Data is a big deal. Fashion companies and retailers can leverage Big Data analytics to quickly understand which trends are gaining momentum and which ones are losing ground at any point in the product life-cycle. With that insight they can make smart adjustments to designs, production and marketing before launching a new collection, reducing the risk that the line won’t sell. Additionally, companies to optimize their supply chains as they can now decide what to produce more and should be stocked in inventory while what can be kept for made-to-order or what is needed immediately.

Big Data as a Customer Behaviour Tool

Fashion companies today understand that the more data they collect and analyse basis their interactions and engagements with customers, individual preferences will become easier to predict, more comprehensively and in greater detail than ever before. This will in turn provide valuable insights for the fashion industry, from what products might perform best, in general, down to what will likely sell well in which store locations, what products are successful when placed next to each other and how to optimize retail experiences. Big data is extremely useful in a marketing capacity, using information like customer demographics and spending habits, in terms of how much they spend, on what and where. Data analytics helps the companies understand what their customers are looking for basis their search history, previous purchases and buying patterns and analyses trends arising from it. This is what helps companies stay ahead of competition – the ability to analyse and make actionable information and trends from the data collected.

Tapping the Social Media with Big Data

As difficult it is to believe, Big Data is gradually is becoming an integral appendage of one of the most intuition-based and unpredictable industry. In a universe where outfits and trends become dated with the launch or release of the next big thing, even top fashion companies like Prada, Gucci, Burberry, Chanel, Ralph Lauren and the rest of the bandwagon are relying heavily on Big Data Analytics and related technologies. In fact designers and fashion companies often share release photographs of their exclusive collections on Social Media (Facebook, Twitter, Instagram, Pinterest) which help them understand the trends and people’s response much before the curtain-raiser. Sentiment analysis through collection of the responses (likes, shares, comments, re-tweets) helps the industry analyse every aspect of consumers demand— from the most loved color to the most acceptable fit.

 E-Commerce and Big Data

With venture capitalists backing e-commerce platforms, the importance of the companies being profitable is heightened even further. Revenue generation and in turn increasing profitability is the key focus of E- commerce companies and this is where big data analytics steps in to save the day. Understanding customer preference, predicting customer demand and targeting customers through their chosen medium is possible through the undeniable contribution of big data analytics.  Fashion companies in order to stay ahead of the curve are increasingly looking at not only adopting data analytics and related technologies but also implementing across departments to generate results for real time predictions and results.

With the liberalization of the FDI policies, increasing demand for e commerce and the dizzying pace of changing trends in the market place, fashion companies need to up their game to stay ahead in the race.  Without a doubt, Big Data is starting to make a big impact on fashion, and more is yet to come. While the adoption is taking place slowly but gradually, it is the real time implementation and the customer experience which is the true differentiator. The brands and companies who can extract most wisdom from customer data and react to it most effectively, will ultimately emerge as the winner.

 
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