The Future of Big Data in Fashion

The fashion industry is at a tipping point. From the race to sustainability to survival in a global pandemic, the economic and environmental challenges are burdensome – for everyone. In 2020, apparel brands lost 90% of their profits. Consumer confidence has yet to recover. And the clock continues to tick down to the 2030 Climate Target Plan, demanding the sector to innovate faster, better, and smarter. Can big data help?


After two years of disruption, the fashion industry is re-emerging. Big data, or large volumes of information, has played a critical part in the fashion lifecycle for years.[1] However, today it is taking a different, more profound presence in the potential of artificial intelligence (AI) and analytics. Large and small brands are seeking answers to how big data can help them understand their shoppers, become more agile in their supply chains, and create new revenue streams for sustainable growth.

Within this context, three rising trends will harness the power of big data and guide brands over the high fashion seas. Product passports, prediction precision and personalization are the three P’s that will determine how companies leverage deep consumer insights for fiscal growth and environmental progress. Looking at the pandemic period, the top 25 fashion brands with the highest market share growth were companies who shifted towards data, digital and analytics.[2]


Product passports for consumer trust

Functioning as a transparency tool, this approach shares information about an item, its manufacturing processes and distribution networks to the end consumer. The technology offers traceability as a compelling value proposition using blockchain, QR codes, near-field communication and radiofrequency identification. The objective is to give shoppers an insider’s look into the brand’s activities – from procurement to disposal – to earn their trust, confidence and loyalty. A recent survey showed that 80 percent of consumers are skeptical of sustainability claims by fashion brands and feel that companies could do better in being honest about their products and processes.[3]

Noticing rising consumer cynicism, Ralph Lauren moved swiftly to partner with EVRYTHNG, Depop and Vestiaire to develop digital platforms that provide in-depth information about the traceability of second-hand products.[4] Over 130 factories are involved in the program, and shoppers can validate the authenticity of an item and make purchases with full disclosure of the product’s origins and makings. “It matters from a trust-based perspective,” explains Keith Turco, president of EVRYTHNG. “They want to ensure that consumers trust that if a product has their brand on it, that it is authentic, it is real and it is worth paying the premium price for it,” he clarifies.[5]

But Ralph Lauren’s product passports go beyond authentication and trust. The platform provides an opportunity to support circularity and sustainable practices by tracking entire lifecycles with a fine-tooth comb. Companies can deep-dive into their supply chains and identify opportunities for improvement. In this way, brands are not only encouraging shoppers to buy second-hand items and participate in the circular economy, but they are reducing costs, slashing carbon offsets, and discovering new ways to create value.

Prediction precision for value creation

Big data enables companies to forecast trends with high accuracy, meeting fickle consumer demands while adapting to more environmentally-sound solutions.[6] Three decades ago, companies would regularly sell 85 percent of their stockpiles at full price. Today, brands struggle to reach 60 percent of their full-price sell-out ratios. In other words, for every five garments produced, two end up as inventory excess or unused deadstock.[7]

A “made-to-order approach using data and analytics leverages consumer insights to make goods that are custom-made for the buyer and guaranteed to sell.[8] This model not only tackles the industry’s waste dilemma but also helps companies conserve energy, water and raw materials needed to make clothes while avoiding releasing more emissions into the atmosphere. Brands that have integrated predictive platforms into their supply chain management have successfully streamlined inventory control and optimized distribution networks, slashing inventory costs by up to 15 percent and saving 24 percent of their carbon footprint compared to industry standards.[9]  a minimalist apparel brand aims to fill the gap between design and activewear by creating “on-demand” clothing that fits every unique body shape. The brand offers online personal fittings and creates pieces using closed-loop materials, such as ECONYL® nylon.

Further challenging the status quo is Levi’s, an American clothing company that plans to leverage prediction accuracy throughout its entire business, turning regular personnel into analytics experts.[10] The fashion house developed an engine that mobilizes big data, including transactions, fashion trends, weather conditions, and social media activity to optimize its distribution grids, delivering products to its customers in a fast and low-cost way.[11] “AI and tech initiatives have enabled us to transform our business,” comments Levi’s finance chief Harmit Singh. “It is a priority for the company as we unlock value for all our stakeholders,” he says.[12]

Personalization for hyper-differentiation

Brands can leverage big data to tailor apparel and experiences to stay ahead of the competitive curb – just not at the planet’s expense. Cutting-edge technologies such as 3D printing are gaining critical mass to make clothes and accessories to fit each shopper. This has positive implications for the industry, extending product lifecycles and moderating clothing waste.[13] Companies that have embraced personalization tools experience up to a 30 percent reduction in product returns.[14]

Big data can also drive one-to-one engagement with shoppers. With nearly 1 in 3 people making purchases online, many fashion companies have personalized their e-commerce journeys. Brands have been able to offer customers what they want and when they want it to cultivate loyalty. Those who have harnessed data to personalize such interactions have doubled their digital sales and boosted price margins by up to 10 percent.[15]

Taking an example from the beauty industry, retailer Sephora gathers data from quizzes and past purchases to generate recommendations for a seamless shopping experience.[16] Instead of tackling personalization channel by channel, the brand synchronizes its offerings across multiple digital platforms for each specific shopper.[17] With over 25 million members in its loyalty program, Sephora claimed first place in Sailthru’s Retail Personalization Index last year.[18],[19]


Caveats & future regulations

There are risks. New consumer privacy regulations may hamper data collection initiatives. Apple’s current policy allows users to decide whether or not they want to share their unique Identifier for Advertisers (IDFA), a tool used to measure how people interact with ads along the customer journey.[20] Google’s 2023 data protection campaign could also pose future challenges. Next year, the conglomerate aims to block all third-party cookies in Chrome, limiting individual online tracking and behavior monitoring.[21] Although it is a step forward for user privacy, such policies could hamper fashion companies from receiving real feedback in the e-commerce space, potentially stifling online growth.

In the long haul, brands that embed analytics across multiple business dimensions come up top. The winning strategy mixes old methods with new ones, relying on various data sources – such as sign-up forms, social media, subscriptions, newsletters, searches, sales data, and many more to tailor unique experiences. Although privacy laws may prevent companies from retrieving granular data, shaping a multichannel approach can help brands know their customers better and fully see the bigger picture.


[1] SAS “Big Data: What it is and why it matters.” Available at:,day%2Dto%2Dday%20basis.&text=Big%20data%20can%20be%20analyzed,for%20making%20strategic%20business%20moves

[2] McKinsey (2021) “Jumpstarting value creation with data and analytics in fashion and luxury.” Available at:

[3] Wovn (2022) “Consumer fashion shopping trends.” Available at:

[4] Sourcing Journal (2021) “How Ralph Lauren’s Product Digitization Partner Creates Circularity and Consumer Trust.” Available at:

[5] Sourcing Journal (2021) “How Ralph Lauren’s Product Digitization Partner Creates Circularity and Consumer Trust.” Available at:

[6] Harvard Business Review (2021) “AI Adoption Skyrocketed Over the Last 18 Months.” Available at:

[7] Business of Fashion (2021) “At Platforme, Making Made-to-Order Production Accessible to All.” Available at:

[8] Sustainably Chic (2022) “How AI Is Making the Fashion Industry More Sustainable.” Available at:

[9] Forbes (2020) “H&M Group Experiments with Made to Order ‘Fast Fashion’ To Tackle Overstock.” Available at:

[10] The Business of Fashion (2021) “Can Levi’s Turn Regular Employees into Data Scientists?” Available at:

[11] Silicon Republic (2021) “How Levi’s is using AI to change its jeans business.” Available at:

[12] The Wall Street Journal (2021) “Levi’s AI Chief Says Algorithms Have Helped Boost Revenue.” Available at:

[13] “Fashion Industry as a Big Data Enterprise for Sustainability.” Available at:

[14] McKinsey (2021) “Jumpstarting value creation with data and analytics in fashion and luxury.” Available at:

[15] McKinsey (2021) “Jumpstarting value creation with data and analytics in fashion and luxury.” Available at:

[16] Hubtype (2020) “6 Ways Sephora Creates Personalized Digital Experiences”. Available at:

[17] Women’s Daily Wear (2019). “How Sephora Gets Personalization Right.” Available at:

[18] McKinsey (2020) “Personalizing the customer experience: Driving differentiation in retail.” Available at:

[19] Sailthru (2021) “2021 Retail Personalization Index.” Available at:

[20] “Identity for Advertisers (IDFA).” Available at:

[21] “Building a more private, open web.” Available at: