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Fashion's Technology Trends & Digital Disruption : An Analysis of the Emergence of AI/ML Technology in the Fashion Industry

Fashion's Technology Trends & Digital Disruption : An Analysis of the Emergence of AI/ML Technology in the Fashion Industry

Introduction 

Digital disruption has officially approached the doorstep of the fashion industry. The coronavirus pandemic has radically changed how consumers shop, shifting their consumption from taking place in in-person stores onto online e-commerce platforms. To accommodate this shift, fashion retail companies have had to integrate new technologies into their everyday business operations to better serve their consumers (as digital platforms were temporarily the only avenue in which they could serve their customers during lockdowns). Between March and August 2020, the retail sector squeezed ten years of e-commerce penetration growth into three months. Additionally, McKinsey retail research reports that companies have not only accelerated the digitization of their customer interactions and their core supply-chain operations by four years, but also their share of digital or digitally enabled products by seven years. 

With this digital growth encompassing both digitalization and digitization, it is important to note the difference between the two terms: digitalization refers to the conversion of business processes over to digital technologies, whereas digitization means the conversion of information into a digital format. The adoption of these technologies has not only re-emphasized the importance of e-commerce in the digital era, but also accelerated both the retail industry’s pace of digitalization on the customer-facing end and its rate of digitization on the non-customer-facing end. 

These pandemic-driven technological amalgamations only go to further build on relatively recent applications of artificial intelligence and machine learning (AI/ML) in the fashion industry; AI/ML not only advances both the forecasting and manufacturing procedures of the clothing production and buying process, but also enhances the marketing and personalization elements of the consumer’s shopping experience. These technological innovations will continue to revolutionize the inner workings of the industry from fast fashion to high fashion, ultimately resulting in more fluid customer shopping experiences, hence satisfying customers and consequently maximizing company profits.

The Scramble Toward E-Commerce and Phygital: Incorporating Pandemic Changes into the Business Model 

The pandemic-induced acceleration of digitalization in the fashion retail industry has established  experiential e-commerce as a long-term necessity in the business model rather than a “nice-to-have” revenue source. The prevalence of e-commerce in the fashion industry has consistently grown since 2003 and is expected to continue growing over the next five years. In 2020, pandemic lockdowns forced consumers to move onto digital platforms to consume goods and services, leading to an unprecedented rise in e-commerce sales. According to e-commerce data analytics firm Digital Commerce 360, consumers spent $861.12 billion online with US merchants in 2020, up 44.0% from 2019; e-commerce sales also jumped to account for 21.3% of total retail sales, compared to 15.8% in 2019 and 14.3% in 2018. The jump in e-commerce was particularly high in the luxury goods market: a Bain & Co. report asserted that the luxury-goods market’s revenue growth in the first quarter was partially driven by increased online demand. The consultancy estimated that “the share of Web purchases almost doubled to 23% last year from 12% in 2019 and will represent 30% of the market by 2025.” While digital usage is expected to slip back slightly post-pandemic, it is still expected to stay well above pre-pandemic levels moving forward– a new McKinsey survey of 98 US-based retail executives predicts an overall 6 to 13 percentage-point increase in online penetration compared with pre-coronavirus rates. In a second McKinsey survey (with data collected from over 20,000 participants), 70% of respondents indicated that they planned to continue using digital services just as frequently post-pandemic. “It is more efficient to transact digitally and, importantly, digital customers are more engaged,” says Paul Jenkins, a senior partner and leader at McKinsey digital. The pandemic’s long-term effects on consumer behavior only further highlight the importance of incorporating digital elements into the shopping experience. Consumers have developed preferences for the conveniences that digital tools and platforms have to offer, and companies must adapt by investing more heavily in the digitalization of the shopping experience. 

Digitalization manifests itself in two forms when it comes to the customer’s shopping experience: the pure e-commerce and the “phygital” experiences (i.e., digital tools and features are integrated into the physical shopping experience to enhance it). Post-pandemic, consumers now prefer to continue shopping online from home, and if they go in-person, prefer a more digitalized and thus more streamlined in-store experience. The migration of consumers from in-person to online has incentivized profit-maximizing companies to both improve e-commerce experiences and invest more heavily in phygital. Fashion retail companies that double down on their digital strategies and technological endowment (e.g., their overall technological capabilities, talent leadership, and resources) will be the ones that not only stay solvent and competitive, but also thrive and outperform their competitors. Innovations in digital service offerings are the future of customer retail shopping experience, and companies who invest boldly now in digital channels and expand their digital engagements will reap the benefits in the form of excess returns. From there, a cycle begins: as more customers use digital channels, firms can collect more behavioral data and learn from it to further improve their digital offerings, which in turn draws in more new users and thus generates more revenue. 

 

Many fashion retail businesses have already started to both invest in their technological endowments and incorporate technological developments into their business models. During the pandemic, 65% of companies cut costs by reducing resources to various parts of the businesses, but increased funding directed towards their digital and technological initiatives, strategies, and operations. With fashion e-commerce sales expected to continue rising into 2024, firms must further invest in their digitalization initiatives in order to adapt their store operating models and procedures to accommodate the new generation of digital-friendly users and thereby sustain outsize revenue growth.  

 

Improved Fashion Forecasting: Predicting Supply Chains and Demand Projections 

Technology in the form of artificial intelligence and machine learning is revolutionizing how the fashion industry forecasts upcoming trends and predicts the supply and demand for products. These digitized, accuracy-optimized forecasts minimize overproduction and excess supply of clothing, thereby not only preventing wasted costs, but also allowing firms to meet their environmental sustainability goals. 

WSGN, the world’s leading fashion forecaster, uses artificial intelligence and machine learning to help designers predict fashion trends and to advise department store buyers, with the end goal of driving top-line growth for the companies that consult them. To forecast trends, WSGN collects and digitizes vast sets of data through global street style images, live reports from trade show floors, and social media buzz. By using cognitive computing algorithms to analyze and interpret the data, the forecaster can then form hypotheses for the popular colours, fabrics, and styles of the next season. These data-backed forecasts inform designers on the trends they should incorporate into their designs for the next season’s commercial collections so that they produce the right products at the right time, hence maximizing revenue during key retail drops. Similarly, buyers (i.e. employees at department stores who plan and make buying decisions and determine the stock and selection that their stores will be carrying, not to be mistaken with consumers) can consult fashion forecasters to advise them on their buying choices. WSGN, in turn, uses their data to provide phasing plans, product checklists, and mix charts so that buyers know what to buy, when to buy it, and how much to buy; these services ultimately help buyers strategize for future seasons and adapt their offerings to consumers’ changing tastes. 93% of executives agree that WGSN Fashion has improved its business by validating product direction, strengthening collaboration and information sharing, and informing new product lines and extensions. Overall, the digitized trend intelligence that WGSN provides allows companies to make better decisions from designing to buying, ultimately maximizing profitability and revenue growth. 

 

Fast fashion companies within the industry are further revolutionizing their production procedures by turning their design process over to AI fashion designers. These “designers” are generative adversarial network algorithms (in short, a machine learning process that generates new data from existing data)  that digitally collect and analyze thousands of images of clothing in order to learn what an item of clothing looks like structurally. From there, the algorithm synthesizes the popular styles found in its existing database of images and uses them to build new designs that resemble hybrids and derivatives of previous trendy styles. AI fashion designers’ ability to not only gather and interpret data at a large scale, but also generate new designs and styles at an unprecedented pace, speeds up the trend cycle and incentivizes consumers to always buy the newest styles, generating a constant and hefty stream of revenue for fast fashion companies. The tangible results that these algorithms produce exceed human designers’ capabilities and impacts, which may lead to job losses in the fashion industry to AI. Workers in current roles should be job-trained to understand and operate these new technologies so that they can adapt with the evolving technological landscape within the industry, given that the applications of artificial intelligence and machine learning in fashion are here to stay. 

 

Furthermore, using AI/ML to forecast supply and demand allows companies to produce just enough supply to match existing and meet future demand, such that they can regulate production quantity in order to avoid overstock. This not only protects companies from deadweight losses by reducing the volume of unsold clothing and wasted costs, but also allows firms to engage in more environmentally-friendly production practices that minimize overall industry emissions (given that the apparel industry is responsible for 10% of global carbon emissions). In 2018, it was leaked that Burberry had burned their unsold inventory to preserve the exclusivity of their products, with the cost of the waste totaling to £90 million over five years, drawing the ire of environmentalists around the globe. Similarly, in the same year, fast fashion retailer H&M admitted to an unsold inventory valued at $4.3 billion, citing both their ineffective e-commerce platform and their errors in trend forecasting as the main culprits. According to the Economist, AI/ML can reduce forecasting errors by up to 50%, such that companies will be able to more accurately gauge what clothing to produce and how much of it given the estimated demand for it. As such, these innovative technologies are a new tool for merchandisers to use to adjust both prices and supply to meet demand accordingly, thus eliminating excess supply when managing inventory and sales. Additionally, big data can be harnessed to understand not only the production of clothing, but also optimize its distribution: understanding regional differences in consumer preferences can inform clothing companies on how to allocate an appropriate level of stock to meet the demand. By using data-backed decisions to understand their consumer base and distribute their stock, companies avoid understocked or overstocked styles and designs (i.e. mismatches in supply and demand). 

H&M’s chief data and analytics officer Arti Zeighami agrees with the efficacy of these new digital technologies, referring to AI as “amplified intelligence” that provides tools and data to enhance human understanding and empower decision-making during the garment design and production, transport, storage, and allocation processes along the supply chain. Zeighami currently works to integrate AI technologies into H&M’s supply chain through trend forecasting, product allocation, and pricing and merchandising. He led a pilot initiative that fused AI and human capabilities to set prices for midseason and end-of-season sales, conducting an experiment to see which of the three subject groups (algorithm, in-person merchandising team, and fusion of algorithm and in-person merchandising team) could set the prices that yielded the highest sale revenue. The algorithm performed a few percentage points better than the in-person merchandising team, while the fusion performed twice as well as the algorithm, enforcing Zeighami’s idea of “amplified intelligence.” Overall, in using AI/ML to conduct prediction analysis and to amplify creativity and potential for sales, all companies within the fashion industry will be able to reduce both sunk costs and clothing waste, hence increasing their profitability and decreasing their carbon footprint.  

Advanced Marketing: Using Data to Target the Right Audiences 

To attract new customers and retain, cross-sell, and upsell existing customers, fashion companies are also working to incorporate AI technology into their marketing strategies. Modern marketing relies on understanding buyers’ tastes through the digital collection and interpretation of consumer data from their online activity and in-store behavior. With AI/ML technology, this data can be converted into insights about a retail company’s consumer base, and how that consumer base’s preferences can differ by age or region. Using these newfound data-driven insights, companies can make better-informed decisions on where and when to run ads to target specific groups within their consumer base, such that the right products are advertised to the right people. Advertisements that are relevant to a consumer’s lifestyle are much more likely to raise marketing conversion rates (i.e. the rate at which marketing switches people from browsers to buyers) and thus company revenues. When the world emerged from pandemic lockdowns last year, many marketing teams employed precision marketing by employing epidemiological statistics, municipal reporting, and traffic data to track reopenings on a county basis and from there, devised their media spending strategies. Their use of data-backed marketing during a time of convulsive change resulted in significant rates of customer acquisition and a double-digit increase in sales. 

 

Modern marketing must further develop their platforms and strategies to utilize dynamic data sets in order to respond to a consumer’s real-time needs, interests, and behaviors to grow their company revenues; the winning strategy comes in the form of the Customer Data Platform (CDP). Previous marketing platforms that companies have employed include customer relationship management (CRM), master data management (MDM), and marketing resource management (MRM); McKinsey marketing research reports these strategies streamline data collection and segmentation, organize workflow, and improve customer relationships, but also rely on antiquated “list pulls,” basic segmentation, and broad campaigns. They lack automated decision-making, adaptive modeling, and effective data utilization to scale personalized interactions; CDP can make up for these shortcomings. As a data discovery and automated decision making (“decisioning”) platform, marketers can use CDP to scale data-driven and instantaneous customer interactions. CDP consists of four core elements:

Data: Data is the foundation of CDP. CDP is fed consumer data on personal demographics, style preferences, and behavioral patterns so that it can form customer profiles and make them accessible to all departments in the company. 

Data science, marketing, sales, and consumer-experience teams will need to work together to leverage the digitized data into a marketing tool through AI/ML. Machine-learning algorithms that employ advanced analytics can cluster customer profiles that behave and generate value similarly, and the model “learns” how to further classify and sort profiles into smaller subsegments. 

Decisioning: Decisioning mines the data to act on signals. In this stage, the algorithm 

recognizes and interprets behavioral patterns, and proceeds to score customers on their potential value. Machine-learning regression models will use those values to match specific messages, offers, and experiences to the various customer scores, such that each consumer gets a more relevant, personalized advertisement experience. The decisioning engine “learns” to pick up on signals and to use them as triggers to invoke actions, and to repeat actions that have yielded the highest rate of return. McKinsey retail research provides an example: “An algorithm might learn, for instance, that customers who make more than two visits to a store’s website within a two-week period are 30 percent more likely to make a purchase. Such indicators can trigger tailored offers to convert browsers into buyers, allowing marketers to direct their acquisition efforts and spend toward the most profitable segments.” Over time, the algorithm learns effective decisioning by repeatedly testing signals and triggers, and figuring out which ones consistently result in successful outcomes. As the model learns more and more, it becomes more sophisticated and will prioritize the messages, offers, and experiences that prove to be the most profitable.

  1. Distribute: Distribution consists of the channels that marketers use to communicate their content to their consumers. Previously, the ads were delivered manually and blasted to broad swaths of customers without any personalization. With CDP’s developed triggers and personalized content, distribution systems are more precise in sending the right messages, offers, and experiences to the right consumers at the right time through the right channels, essentially tailoring each communication to each specific customer or their subsegment. Companies that have invested heavily in their AI/ML marketing have integrated APIs into their CDPs such that it not only delivers, but also follows experiences. In doing so, a feedback loop is created: CDP sends out promotions, then tracks customer response and engagement, and finally sends conversion data back to improve future promotions.

It is expected that with the implementation of CDP in marketing, retailers’ total sales can jump up to 20 percent. 

At the end of the day, retailers’ precision marketing models rely on two core ideas: digitized big data (on consumer behavioral patterns) and AI/ML algorithms to interpret that data. Given that data serves as the foundation of modern marketing, firms must ensure that the data they collect is both accurate and up-to-date to reflect shoppers’ preferences. Marketing teams should also constantly update their modeling by expanding their data sets and retraining their algorithms to accommodate changing consumer behavioral patterns. The coronavirus pandemic has radically and permanently changed how consumers shop, and businesses that invest in precision marketing will be able to transform the COVID-19 crisis into an opportunity for further innovation and increased profits. 

McKinsey’s marketing research suggests that one such way to approach marketing in the new normal is to “take a wide-angle approach to data collection by gathering not only behavioral trends and location-based insights but also third-party analytics on their business, customers, and competitors to complement their in-house customer data.” In expanding their data sets, companies can better identify changes in demand by assessing shifts in both new customer engagement and existing customer activity. Companies with both robust in-house and third-party data will ultimately be able to gain an information advantage over their competitors through their superior personalization of messaging, content, and offers. Furthermore, marketing teams should explore more AI/ML applications that will lead to not only a more agile operating model, but also technology that will learn at scale. Algorithms can then yield improved analysis of signals of consumer intent and more detailed tracking of consumer responses to marketing campaigns. With more specific data, the CDP marketing engine can learn what works and what doesn’t; using those insights, the algorithm can adjust its content and strategy accordingly to increase customer engagement. Overall, by collecting more data, finding new behavioral relationships, and enabling more marketing strategy experimentation, marketing teams can capitalize on growth opportunities and emerge from the crisis with greater returns on investment. 

Enhanced Personalization: Revolutionizing the Customer Shopping Experience 

Personalization is the future of commercial fashion due to its potential to increase e-commerce and in-person sales, provide feedback data for more accurate forecasting, and ensure successful marketing campaigns. The widespread embrace of this relatively new feature to the shopping experience means that fashion companies will use AI/ML technology to analyze consumer data to better understand each individual consumer’s particular tastes, and from there, tailor the consumer’s recommendations, content, offers, and shopping experience to suit those preferences. According to McKinsey’s marketing research, personalization has already yielded tangible results for the retail companies that employ it; it reduced acquisition costs by as much as 50 percent, lifted revenues by 5 to 15 percent, and increased the efficiency of marketing spend by 10 to 30 percent. Personalization at scale has the potential to create $1.7 trillion to $3 trillion in new value. 

With e-commerce traffic growing fivefold during the pandemic, it is essential for fashion retailers to integrate more AI-driven features into the online shopping experience in order to bring it up to par with the in-person one. These AI/ML features must act as sufficient, if not better, substitutes for in-store salespeople, who understand the client’s tastes and point consumers toward products that reflect those preferences. Amazon is a leader in the retail space in terms of the integration of personalization onto its e-commerce platform. Examples of personalization on their site include algorithms reviewing what a shopper has bought before or has expressed interest in buying, and leveraging that data to ask the buyer if they would like to buy the same product again, recommend similar or related products, or email the customer reminding them of the items in their abandoned cart. All of these strategies culminate in the company converting, cross-selling, and/or upselling their existing customers. The machine learning technology that these firms employ essentially enhances e-commerce shopping to mirror the in-store shopping experience, but accessible right from home. 

 

Looking into the future, it is important to recognize that the retail environment is more competitive than ever because of price pressures from discounters, market disruption from online players, and increased price transparency for shoppers. Traditional differentiation factors, such as unique selection or strategic pricings and promotions, are not as effective as before due to susceptibility to imitation. Furthermore, a survey of 1,000 US adults by Epsilon and GBH Insights indicated that 80% of consumers are more likely to make a purchase when brands offer personalized experiences. McKinsey marketing research indicates that these consumers want to see “multiple, personalized touchpoints that enable them to allocate their time and money according to their preferences” and “receive offers that are targeted not just at customers like them, with brands targeting at the segment level with broad-based offers, but at them as individuals, with products, offers, and communications that are uniquely relevant to them.” Thus, in order for a retailer to differentiate themselves from others and maintain a competitive advantage, they must provide the best personalization services within the industry. To foster long-term growth and stay ahead of their competitors, companies need to continue investing in AI/ML features to improve consumer engagement, encourage self-service transactions, and convert site visits to sales. 

Given that fashion retailers’ e-commerce platforms have been developed to equal in-store shopping experiences, consumers now also have rising standards for what they consider a satisfactory in-store retail experience; companies must work to incentivize shoppers to continue visiting their brick-and-mortar stores through elevated in-person experiences. The primary way to achieve a more fluid in-store “phygital” shopping experience is to integrate personalization into the process. Algorithms that collect data on individual consumers’ tastes and payment information from previous online and in-store purchases can inform salespeople on how to better serve their customers for future in-store visits. When salespeople are given access to this data, they can immediately tailor each in-person shopping experience to suit each individual consumer’s preferences upon their arrival at the store. Personalization through AI/ML technology essentially eliminates the need for a salesperson to re-acquaint themselves with customers each time they visit that store; instead, the salesperson will know the shopper as well as a close friend or a personal stylist who knows which items they may want to re-purchase and which new pieces they would like to consider buying. Additionally, the collection of data from previous purchases allows for salespeople to already have consumers’ payment information on file, which can be used to expedite the checkout process. 

With the implementation of personalization in brick-and-mortar stores, retailers can streamline each shopper’s in-person browsing and checkout experiences and deliver on customer satisfaction. Successful personalization ultimately results in engaged customers and hence profit growth; McKinsey retail research reports that personalization at scale yields a 20 percent jump in customer satisfaction rates and a 10 to 15 percent boost in sales conversion rates, as well as cuts marketing and sales costs by around 10 to 20 percent. Furthermore, retailers with consistently high rates of customer satisfaction have provided shareholders with three times the return of retailers with lower rates. To maintain customer satisfaction and ensure a continued revenue stream from physical stores, companies should consider redirecting investment funds from opening new brick-and-mortar stores to further enhancing AI/ML personalization technology in existing locations. 

 

Retailers still have a long way to go in order to incorporate advanced AI/ML-driven personalization into the in-store experience. While e-commerce platforms already offer various personalized features such as targeted digital advertisements, personalized emails and recommendations, and tracking of behavior browsing and cart abandonment, in-person stores do not offer customers those same digital luxuries. Consumers who shop offline see generic storefront advertisements and displays, and there are no current means to collect their offline browsing data, hence leaving companies unable to personalize their next in-person visit. These non-personalized and lacking elements of the in-store experience cause companies to lose out on opportunities to upsell and convert their customers. To remedy this loss, retailers must find the means to enhance data collection and AI/ML capabilities such that brick-and-mortar stores can run advertisements that sparks traffic and awareness pre-visit, convert during visits, encourage engagement post-visit. Using the omnichannel (e.g., a multichannel approach to sales that seeks to provide customers with a seamless shopping experience, whether they're shopping online or in a brick-and-mortar store, as defined by TechTarget) decisioning engine CDP, retailers can deliver optimal experiences. For example, as McKinsey’s retail research suggests, if a customer agrees to be identified by an app or facial recognition, the decisioning engine can send automated and personalized offers or messages when the customer enters a brick-and-mortar store location. If a customer cannot be identified, the decisioning engine can still make live decisions based on contextual or locational data, such as weather conditions, time of day, trending purchases in a specific location, or complementary basket items. By continuing to develop the capabilities of CDP, fashion companies can expand the degree of personalization that in-store experiences can offer to the consumer. 

While personalization is an efficient tool to enhance shopping experiences, retailers must always keep in mind what shoppers really want. In a modern world where people are increasingly concerned about how much data they give companies and how companies will use their data, retailers must be careful to only use the information they collect to market relevant or solicited advertisements to consumers. Shoppers want their data to be leveraged to enhance their shopping experience, not to create a bothersome or invasive presence in their life. Therefore, marketing teams must carefully regulate their content and its frequency so as to not appear neglectful or overbearing to their customers. When targeted communications are relevant and useful, they can create lasting customer loyalty and drive revenue growth by 10 to 30 percent. A McKinsey survey of consumers allowed the firm to create a list of personalized marketing that they would like to see:

  1. Relevant recommendations that the consumers may not thought of themselves
  2. Being advertised to when they are in “shopping mode” (e.g., holiday season)
  3. Remind them of products they are interested in but do not keep track of
  4. Understanding the consumer’s preferences whether they are shopping online or in-store
  5. Share value in a way that is meaningful to the consumer (i.e. loyalty programs, specialized discount offers, and personalized communications)

The degree of usefulness that personalization holds in increasing revenues is contingent on firms maintaining a balance in which they take risks and advertise new products to consumers, but without making the content seem irrelevant or unsolicited. Overall, proper use of personalization will revolutionize how consumers think about shopping by easing their omnichannel retail experiences, ultimately increasing customer satisfaction and hence fostering revenue growth. 

Case Study: LVMH-Google Cloud Partnership 

A recent development in the intersection of the fashion industry and AI/ML technologies came in the form of the new partnership between European luxury goods conglomerate Moët Hennessy Louis Vuitton (LVMH) and American technology firm Google. In a press release, LVMH and Google Cloud announced that they would design new cloud-based artificial intelligence and machine learning solutions to “empower LVMH’s Maisons” and revolutionize fashion industry practices. Among these practices include innovating business operations like demand forecasting and inventory optimization, as well as elevating customer service through the creation of personalized shopping experiences. The integration of technology into processes along the value chain enhances human decision-making at every step from product development to user interface, testifying to Zeighmaer’s concept of “amplified intelligence.” Through this collaboration, Google will have the opportunity to explore new applications of their AI/ML technologies, while LVMH will benefit through more efficient business operations and elevated customer service, with the end result of achieving cost efficiency, technological agility, and security, and long-term growth at scale. Together, their synergies will yield benefits for both companies and innovate processes within both the fashion and technology sectors. 

 

LVMH managing director Antonio Belloni explained the motivation behind their partnership, citing that the pandemic has “been transformational” and made clear the “need to leverage data” to make a customer’s experience “more fluid.” His colleague LVMH Group Managing Director Toni Belloni also spoke on the partnership, saying “This new, unprecedented and significant partnership with Google Cloud is the reflection of our high ambitions in this area. By combining our best-in-class approaches in our respective industries, it will take us a step forward in the use of data and AI. For us, privacy, personalization, and luxury are synonymous, and that will always remain true. The new opportunities offered to our customers are exactly what our talented teams are working for at LVMH: a unique and unforgettable experience,” Thomas Kurian, CEO of Google Cloud, followed up with the statement that “We are proud to be entering into such an innovative and extensive partnership with LVMH to power its innovation through cloud technology and AI capacities. Together, we can help drive the future of customer experience in the luxury industry.” 

Conclusion 

With the pandemic revolutionizing how we shop, fashion retailers must start investing heavily in the technological tools that will help them adapt to the long-term changes in consumer behavior and stay ahead of their competitors. Funding must be re-directed toward finding applications of AI/ML to business operations and customer service, and from there, integrating them into the business model. Technology’s rising strategic importance within retail makes it a critical aspect to revenue growth, necessitating rapid innovation and experimentation of cutting-edge AI/ML algorithms. The adoption of digital technology at both the organization and industry level have allowed retailers from fast fashion to high fashion to understand consumers on both a personal and aggregate level, such that they can not only forecast and produce widespread trends, but also personalize marketing to recommend specific products to each individual consumer. Continued advancements to these algorithms will allow for more accurate forecasting of trends and supply-demand equilibrium; precise 1-on-1 marketing and personalization; and enhanced online and in-person shopping experiences. Through these improvements, companies can cut costs, decrease their carbon footprints, and satisfy customers, ultimately achieving long-term profit growth. 

Written by Vivian Fang 

Edited by Jay Devon

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