Gone are days of customers wearing whatever fashion houses created. Instead of designers dictating the fashion tastes of the masses, it's now the other way around. The fashion industry has acknowledged the role data can play in increasing sales and profits. Though the industry thrives on creativity and newness, they are turning towards personalized and a more customer-centric approach.
Brands are now using data to monitor consumer behavior and create products. AI, machine learning, and data analytics are being employed by designers to create more efficient merchandise, styles, and marketing strategies that resonate with consumers.
Data is abundant in the fashion environment ranging from consumer details, consumer categories, product information, cost, and revenue including other things. Data from the consumer side is collected from customers’ search histories, social media, and apps and later skimmed to know what customer wants, to create pieces customer are more likely to buy and deliver the desired experience. Pure online players like Amazon aggressively apply data analytics for merchandising, marketing, channel management, consumer targeting, and operations to take purely data-driven decisions. Not only e-commerce giants but even brick and mortar retailers in the fashion industry are collecting and analyzing data to predict their sales. This has revamped the industry from a top-down segmentation approach to bottom-up segmentation.
Designer labels employ data analytics for fashion forecasting (to predict the rise and fall of trends). Analytics is being used to gauge-
a) Consumer demographics- Age, race, gender, income, tastes to understand who the consumer is.
b) Behavioural data- To know past buying behavior, cart abandonment, and transaction channel to understand what the customer did in past to predict his future behavior.
c) Contextual data- Pages, sites, blogs, topics customer viewed/ searched to know which products he is interested in and to cater much better to his tastes.
Predictive analysis is also done on color preferences, climate, social media trends, and political and social movements. It tells fashion houses about what sells and doesn’t sell in a region, season management, etc.
It is clear that brands have become aware of the benefits of data analytics in the fashion industry. Data collection is important but so is deriving meaning out of it and aligning all the collected data to be of use for all stakeholders in the industry. But data cannot fully predict and solve all industry problems nor it can replace good old-school merchandising and creativity. If you are venturing into the fashion segment, a sound knowledge of fashion fundamentals and data analytics is a must. For more industry insights, stay connected with us and subscribe to our paper
