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Can Big Data be the 'Next Big Label' in Fashion?
Wired Innovation Insights - 11 Sep 2013

When was the last time you bought apparel or accessories that were not on   sale? Customers never tire of a good sale, but for the fashion industry anything   on sale means that someone in the manufacturing process made a mistake. Fashion   is an exciting, but a volatile and dynamic business. From misinterpreting the   popularity and potential of a trend to miscalculated inventory, such challenges   have compelled the industry to think of efficiency. Though the fashion industry   is one of the slower adopters of big data, the industry is moving forward. Over   the past couple of years the industry has invested in Business Intelligence   systems, in-store data capture systems and point-of-sales data capture.

Unfortunately, the pace at which the industry is switching to more savvy BI   tools is adversely affecting the decision making process. Even today, the   fashion industry is largely driven by gut feeling and instinctive development of   design. Designers have always combined intuition and creativity with macro-level   knowledge of popular colors, styles, patterns etc. The absence of evidence   demands more tangible factors that give reasons for the success or failure of a   particular style. Despite the huge role creativity plays in designing a fashion   line, profit and business success needs metrics that provide empirical analysis.   And this goes beyond moodboards and focus group sessions for choosing the next   season’s styles, fabrics, colors, materials etc.

Here is where Big Data can play a big role in creating truly big business   impact in the fashion industry:

Big Data’s Unprecedented Potential

Fashion itself is infamous for being changeable, and is driven more by fads   and styles than by statistical data. Big Data can help fashion companies ride   the crest of each trend ahead of competition. Fashion companies are now able to   access or gather larger amounts of customer insights, with a greater depth and   clarity of detail and derive inputs by analyzing much more than historical sales   data and focus groups. And these insights can help creative design as well as   supply chain and pricing decisions.

There are some fashion websites today that go beyond sales trends or focus   groups. These websites collect and share data not just on what people are   buying, but also what they’re wearing. Behavioral data allows fashion companies   to correlate purchases with consumer trends.

Tap Into the Social

For fashion businesses, in particular, social media has immense marketing   potential. By tapping into vast online conversations, savvy companies can get   access to how customers put together brands online. Don’t forget that a simple   “Like”, or word of mouth shared via social media, is more powerful than any   survey or focus group. Big data tools can help you extract vast amounts of data   and analyze all conversations about trends, successful designs, preferences, and   fashion faux pas.

Make Predictions

Until now, consumer demand for fashion has been predicted and driven by   fashion experts and legacy analytics models which are less sophisticated. Focus   groups also churn out small data which may not represent true consumer trends.   Big data is changing the way actual consumer preferences are gathered and   analyzed. It gives the fashion industry access to individual preferences with   far greater detail. Businesses can base their decisions on valuable insights   into what styles may perform better and even into what styles will sell better   in specific locations. The predictive insights of big data go beyond   understanding customers, and can help fashion businesses select creative talent   and improve the effectiveness of their marketing and advertising as well.

How it’s Being Done?

With analytics, structured and unstructured data can now be processed with   the help of PLM tools, and be preserved for future reference. A PLM tool can   help plan the entire product range, or in the fashion context the clothing range   that is responsive to the demand, and can even forecast the demand for the   particular type of design. However for a relatively accurate forecast and   strategy, it helps to have a big data analytics solution that syncs seamlessly   with all aspects of business. There are some companies in the fashion industry   that are already doing this.

One such application of using insights for predicting fashion trends is ITC   Infotech’s Style Performance Analytics (SPA) solution, which combines PLM, ERP   and BI to revolutionize the process of fashion design. ITC Infotech’s SPA   analyzes past designs and brings out the aspects that have been popular and   feeds this information back into the design process. In this way, using   fact-based decision making, the fashion line becomes more adapted to the   customer’s preferences.

Accordingly, when designing for the next season, companies will be, to some   extent, able to predict the success of their design.

L .N. Balaji is President of ITC Infotech (USA). Rajnish Kumar, Global   Head – Softlines, ITC Infotech, and Viros Sharma, Head – BIDW & Analytics,   contributed to the article.

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