How See Now, Buy Now Enabled by Artificial Intelligence
Walter Mischel, a psychologist famed for the 1960’s Marshmallow Test, which he elucidated the virtue of delayed gratification for youngsters. Today, the data economy promotes the dichotomy of an on-demand culture. Enabled by AI on divinatory consumer demand and predictive supply chain, front-line companies are charged up to provide a See-Now-Buy-Now (SNBN) experience to the consumers.
Amongst the US retail categories, grocery ranks first with $770 billion (30% of dollar share), while apparel second at $310 billion. After the acquisition of Whole Foods (2017), and with online apparel sales already surpassed Macy’s; Amazon is well positioned to deliver this SNBN contentment, to further cement customer intimacy with over 100 million Prime members. Food supply chain typically has longer lead time than fashion supply chain; the reason that crop harvesting and protein sourcing are multifaceted, as benchmarked to finishing and dyeing in fiber/yarn production. Fiber makes yarn, yarn forms fabric, cut-and-sew of fabric into garments. This linearity of fiber management raises degrees of freedom to the fashion supply chain in delayed production differentiation. Fashion of Business (FoB, a fashion blog) tracks the global fashion industry currently at $2.4 trillion. Beside Zara, many disruptive and smaller companies are leveraging AI to pursue this SNBN selling strategy.
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How it works
How does SNBN work?
Digitization has become an integral enabler in mass customization. Footwear manufactures like Adidas and Nike, are known to interface with their customers in designing their own shoes. Amazon was granted a patent (US 9623578, in 2017), to custom produce clothing after receiving an order. This on-demand apparel manufacturing system can quickly fill online orders for a variety of garments. Upscale fashion brands are all migrating to this SNBN business model. In practice, runway styles were made available for purchase immediately after fashions shows, in lieu of the old business model of waiting for months.
Such paradigm shift has strategic implications to the entire fashion ecosystems, from conceptual design, to marketing and supply chain. Logistical complexity requires data analytics, coordination on operations management, and merchandising execution. This is where AI does its best, integrating data with analysis to drive decisions. Notably fashion brands like Rebecca Minkoff, Burberry, Tommy Hilfiger, Ralph Lauren, Michael Kors, Tapestry (formerly Coach), and others, have all embraced this SNBN model, focusing on premium segments with aspirational price points. High-end luxury is certainly an exception, where long lead time and craftsmanship are still valued.
Traditional fashion cycles no longer reflect today’s consumers’ demands for instant gratification. The Marshmallow Test of the 1960’s has long been swapped by ubiquitous live streaming contents for instantaneous consumption. Instagram, Snapchat and Twitter further fuel the propensity to purchase. Globalization and eCommerce had rendered fashion seasons irrelevant, as much as global sourcing of fresh produces making seasonality of food crops trivial from a consumer’s perspective. Sweeping climate changes in recent years have also made seasonal collection dated in the fashion industry. Hence, the conventional pre-season sale to move merchandise is no longer effective to clear inventory or boost revenue.
Manufacturing advances aided by digital technologies continue to enhance the agility of modern day supply chain; paving the way for Fast-Fashion in shrinking retailer cycles, and significantly swaying purchasing behavior. Consumers today are expecting endless product newness from most retailers and service providers. Department and other Big Box stores are victims of this transformation.
What are the strategic implications?
Streamlining decision cycles
‘Recommend a movie and win a million bucks’, was Netflix’ early attempt (2006) in using algorithm to foretell viewer’s preference. Stitch-Fix, a subscription-box apparel company is using AI to do same. AI now has branched into deep learning (to recognize images) to machine reasoning (to acquire skills to improve solving wicked problems), to being predictive on consumer’s prospect in making a purchase; more importantly, what variety, shape, color, etc. would register the highest probability that generate retail velocity. Google acquired UK based Deepmind (2014) signified the importance in today’s data economy.
The algorithm from the early days of factory floor automation, improving production efficiency and system balancing led to optimizing line planning (OLP). This AI platform allows for the pooling of data from a plethora of sources, such as historic and future trending, CRM with social media feeds, and consumer queries via search engines, to create a distinct customer persona. A digital footprint of his/her VALS psychographic canvas.
This singularity segmentation of one, the unique buyer with all his/her exclusive data attributes, would be synchronized to designers, merchants, marketers, suppliers and all-inclusive in the supply chain. Such exchange of ideas coupled with computations, could lead to collaborate and triangulate to the final delivery of the right product, at the right price to the right place. AI is well suited for these cross-channel data analytics.
Shortening product fulfillment
Additive manufacturing, as in 3D Printing, has been recognized to be a key driver in mass customization. Computerized knitting machine allows for the design personalization to direct the production of any garment, as well as the upper of athletic footwear (the top portion of a pair of sneakers). Whole-garment knitting machine now could produce a variety of apparels at the command of downloading a file with a specific design. Textile and fashion designs are now simply digital files, which can be purchased and downloaded just like buying a MP3 music file, or audio book. Manufacturing is becoming more distributed, where one could be producing garment locally with design inputs from anywhere, in the form of a digital file. Today’s fashion supply chain is truly disaggregated.
Those whole-garment machines can produce and deliver to a customer in a matter of hours, not months. Amazon’s recent patent in on-demand apparel production is an example. Zara has been known to photograph fashion mockups from Paris, WIFI back to their headquarters in Spain, to shorten the product development supply chain. Additive manufacturing realizes speed-to-market by distributed fabrication locally to deliver finish product to customer in the shortest amount of time.
Local sourcing is a distributed manufacturing business model, that fits into today’s Circular Economy in lowering carbon footprint.
Small is beautiful
The launch of capsule collection. Zara has mastered the art of scarcity, by limiting production run, inducing the urge to purchase in the consumer’s mind before stock out. In spring 2017, the US designer, Alexander Wang unveiled a capsule collection with Adidas Originals; after a show, launched online sale immediately and leveraged a New York pop-up truck tour the following day. Pop-up kiosks, or showrooming are key venues to provide the ‘touch-and-feel’ to the consumers. Some Digitally Native Vertical Brands (DNVB), such as Warby Parker with eyewear, and Jack Irwin with shoes, are following this trend in showrooming.
Rebecca Minkoff hosted a SNBN show at her New York boutique in the fall of 2016, with shopping allowed immediately afterward. Consumers went online to virtually try on styles via an app. Amazon’s patent (US 9858719 in 2018) Blended Reality View for a virtual fitting room is an example of this technology trend. Minkoff’s store sales that day doubled while eCommerce sales up 50% vs. year ago. Another SNBN example is Maybelline, an US mass-market beauty and cosmetics firm, embraced the instant-fashion trend by an exclusive partnership with Amazon, having product available immediately after a Rebecca Minkoff’s runway show.
The future of retail reimagined
See Now Buy Now will further turbocharge the changing retail landscape. Attention span for most consumers is in the nanosecond level. Modern retailers must meet consumers’ preferences and demand before they move to the next purchase decision. AI can play a role in mapping the consumer’s temperament and their propensity to purchase. Companies have to reconfigure their design and manufacturing processes to a SNBN framework; more importantly, to seamlessly integrate their supply chain with their customers’ AI personas.