How Big Data can effectively be used in Apparel Retailing

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How Big Data can effectively be used in Apparel Retailing

Since the advent of online shopping, apparel retailing has undergone dramatic change and created an almost borderless world for consumers. Initially, e-commerce seemed to threaten the traditional, brick & mortar apparel retailers. However, the digital trails and unstructured information created by consumers can be harnessed and unlock many insights that help retailers to plan better and execute their category strategy while building and maintaining consumer loyalty.

 

Predicting the Trends

To be successful, apparel retailers constantly need to pinpoint, predict, and stay in touch with ever-changing trends. Historically, retailers listened to the opinions of Fashion designers, observed the trends from Fashion Weeks across the globe, and integrated those inputs into their planning process. This was usually aligned within the trade channels. In today’s consumer-driven environment; however, this process leaves retailers at risk of missing out on top consumer-driven trends.

 

With the advent of social media, retailers can closely follow these customer trends by leveraging data from Instagram, Pinterest, and other social media networks and communities.  These data sets can reveal quite a bit about buying behavior – especially among millennials whose purchasing power continues to increase. According to a recent Forbes article, 62% of millennials say they are more likely to become a loyal customer if a brand engages with them on social networks.[1] In addition, a simple search on women’s fashion results in more than 9 million results for Instagram bloggers and  4000 Pinterest boards, with several thousand followers and millions of Pins. This wealth of consumer data, if harnessed properly, has the power to help apparel retailers achieve large financial gains.

 

Once retailers utilize this trending data to understand their consumers, the next challenges lies in how best use this understanding to plan and price based on these learnings.

 

 

Assortment Planning

Previously, historical purchasing data analyzed seasonal trends across multiple years to determine the appropriate mix of categories to effectively predict inventory needs and optimize sales, but did not account for this season’s latest “it” craze.

 

Big data can also help unlock merchandising opportunities based on trends in other arenas outside of fashion. For instance, is it possible that an increase in skin cancer awareness and prevention leads to more SPF SKUs in cosmetics and beauty retailers, which leads to an influx in skin protectant (rashgaurd) SKUs in swimwear retailers?

 

Until recently, special sun-blocking clothing was not widely available. However, with the rise of the skin cancer prevention trend, options for these garments has increased. Designers Anne Botica and Monique Moore noticed this shift in sunbathing fashion trends and co-founded a sun-blocking clothing company called Mott 50.[2] In 2016, Mott 50’s sales doubled and can now be found in large retailers like Nordstrom.[3]

 

By collecting and analyzing data in adjacent areas and consumer-facing industries, retailers have a chance to identify incremental opportunities within their own space.

 

 

Targeted Marketing

 

The days of mass marketing are quickly coming to an end as personalized and targeted solutions become more efficient and effective. The goal of any marketing campaign is to get the right products or brands in front of the right people at the right time. In the apparel industry specifically, the consumer base is extremely segmented. Big data is critical to determine the potential target segment for a specific product range and also predict the uptick of such products in a season.

 

For instance, search on famous models, and social media influencers, Kendall Jenner and Gigi Hadid led to a 612% rise in the search for bomber jackets on Google and eventually the bomber jacket became one of the biggest fashion trends of 2016.[4] The sales for MA-1 jacket reportedly tripled over the year for Alpha Industries, the manufacturers of the jacket, after both Jenner and Hadid were photographed in this jacket.[5]

 

Timely insights pulled from the big data can help plan a tactical campaign and target the right audiences. Small quick wins over the long term can lead to continued success for apparel brands as the millennials start showing confidence and stay loyal to these brands over time.

 

In the apparel industry, big data can provide a competitive edge. Many retailers are still unsure of how to access or leverage large information sets. To start, find a retail analytics partner who can help build intelligent data sets and can provide predictive and prescriptive analytical tools that unlock better commercial decisions across assortment, pricing, and marketing strategies. Big data is an extremely valuable resource, and when mined correctly, has the potential to garner insights that result in bigger shopping carts and long-term, brand-loyal shoppers.

 

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