Picture a clothing store where the apparel is arranged according to its popularity on social media. Or a design studio which creates new clothes for the next season based on real-time feedback received from Facebook and Pinterest. Better yet, imagine a design studio where customers can recommend shades, patterns and colors for apparel and even play with virtual fashion games.
No, we’re not talking about the future. This is happening now. Shopper engagement has overtaken the retail, footwear and apparel (RFA) industry, bringing fashion crowd-sourcing to new heights. And while most businesses in the RFA space optimize data analytics for proactive business growth and ROI, even more will have to open themselves up to technology adoption and new analytic utilization to prepare for upcoming trends while responding to real-time activity in this space.
Getting social with fashion
With the obvious gains that social media brings to retail—from sales promotion to sneak peeks at upcoming inventory—it’s evident that the fashion industry must continue to find new ways to evolve with and engage customers. Manufacturers can no longer impose styles on consumers.
Fashion organizations launch new collections every season in hopes that their styles appeal to consumers to bring in the dollars. But with new aspirations created every moment through the plethora of digital mediums available to the customer, it becomes a huge challenge for design studios to match market expectations. Retailers and fashion giants continue to use Facebook, Pinterest and Twitter to gain ideas and suggestions from customers on everything from designs to retail preferences.
In fact, according to a recent survey from Econsultancy, social media activities are the fourth most engaged activity online as 10 percent of time spent online is spent on social networks. Facebook lists more than 500 million users—and continues to add more than 500,000 new users each day. YouTube records show that more than two billion videos are accessed in a day. And Twitter receives 190 million visitors a month. Such huge numbers gave the fashion industry an enormous opportunity to engage customers on a global scale. And all of this data is then routed to advanced product lifecycle management (PLM) systems that analyze it and help plan merchandising, advertising and seasonal planning.
And in this highly competitive environment—with consumer preferences changing often—real-time market intelligence is a must-have tool. How can a designer predict if paisley is going to work, or what colors are trending? Analyzing conversations on social networks and using that information as business intelligence will help with everything from predicting the preferred mix of colors and sizes, to pricing strategies that work. Retailers such as Marks & Spencer, J. C. Penney, Kohl’s and Kroger increasingly adopted analytics to address operation inefficiencies and cost savings—and with significant results.
Yet, despite these obvious challenges, the fashion industry is much behind other industries in using analytics as a business tool. According to a benchmarking study from SAP, “fashion companies track about 70 percent lesser strategic KPI’s than consumer products companies,” (see SAP Benchmarking graphic).
A new style of analytics
The fashion industry cannot ignore the wealth of relevant information buried inside each social media channel. But in specific technology adaptation, what analytic capabilities may deliver for other retail segments may not bode the same intelligence benefits for the fashion industry. In the case of the grocery industry, technology helped in this space more where past customer preferences and consumption patterns could help predict future assortments and even suggest replenishment levels. This would not work for the fashion industry which does not readily welcome repetition.