Personalisation
Personalization refers to tailoring products, experiences, and services to meet the individual preferences and needs of customers. It involves utilizing data provided by users as input to create personalized offerings.
Customized product offerings
Personalization allows customers to actively participate in the design process by providing their preferences, such as color, style, or fit. This input can be used to create customized fashion products that align with individual tastes and requirements. By offering personalized options, companies can reduce the production of generic, mass-produced items, thereby minimizing waste and resource utilization.
Reduced overproduction
Personalization helps in reducing overproduction, which is a common challenge in the fashion industry. By producing items based on specific customer requests and preferences, companies can avoid creating excessive inventory that may go unsold or end up as waste. This targeted production approach ensures that garments are made to meet actual demand, reducing the environmental impact associated with overproduction.
Extended product lifespan
Personalization can contribute to extending the lifespan of fashion products. By involving customers in the design process, they develop a sense of ownership and attachment to the customized items. This emotional connection often leads to increased product care and a higher likelihood of continued usage, reducing the frequency of discarding and promoting a culture of sustainability.
Enhanced customer engagement
Personalization fosters a deeper level of customer engagement and satisfaction. By involving customers in the design process, companies can build stronger relationships and create a sense of loyalty. This engagement can lead to a shift in user behavior, with customers valuing and cherishing their personalized items rather than embracing fast fashion trends and disposable clothing.
Data-driven insights
Personalization generates valuable data on customer preferences and behavior. Companies can leverage this data to gain insights into user trends, allowing them to make informed decisions about production, inventory management, and sustainability initiatives. These data-driven insights help optimize the fashion supply chain, aligning production with demand and reducing waste.
Circular business models
Personalization can support circular business models in the fashion industry. For example, by offering personalized repair services or customization options for pre-owned garments, companies can extend the life of products and divert them from landfill. Personalization allows for the transformation and repurposing of existing items, enabling a circular flow of fashion products and minimizing waste generation.
Overall, personalization in the fashion industry empowers customers to actively participate in the design process and creates products that align with their preferences. By reducing overproduction, extending product lifespans, enhancing customer engagement, and leveraging data-driven insights, personalization contributes to a more sustainable and circular fashion economy
Case studies
Heuritech – AI trend forecasting for reduced overproduction
Heuritech provides AI-powered visual recognition and trend forecasting for fashion brands, analysing large volumes of social media imagery to predict demand at SKU level. Data-driven insights support more accurate buying and planning, helping brands avoid overproduction, reduce unsold inventory, and lower associated environmental impacts.
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Stitch Fix – Data-driven personalization and inventory optimization
Stitch Fix is an online personal styling service whose core business model is built around data science and machine learning. Algorithms match clients with stylists and optimize product recommendations and purchasing decisions, enabling smaller, better-targeted inventories and reducing waste from unsold garments while delivering highly personalized assortments.
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H&M Group & Google Cloud – ML-enabled design and demand intelligence
H&M Group collaborates with Google Cloud to combine data on weather, search behaviour, and local context with machine-learning models to inform design, assortment, and material choices. These insights support better demand prediction, more efficient sourcing, and the exploration of lower-impact materials, aligning digital intelligence with sustainability and circularity goals.
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TextileGenesis – Data-driven fiber-to-retail traceability
TextileGenesis uses a data platform with digital tokens and advanced analytics to trace preferred fibers such as TENCEL™ and LENZING™ ECOVERO™ from origin to finished product. High-resolution traceability data enables brands to substantiate sustainability claims, plan material demand more accurately, and reduce process losses in the value chain.
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Zalando – Machine learning for sizing, returns, and stock efficiency
Zalando integrates machine-learning models into merchandising and logistics to forecast demand, optimize replenishment, and improve size and fit recommendations. By aligning purchasing and distribution with real user behaviour and reducing return rates, these systems cut waste, transport emissions, and the environmental cost of misaligned stock.
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References
Akhtar, W. H., Liu, X., Han, H., & Qin, Y. (2022). A new perspective on the textile and apparel industry in the digital transformation era. Textiles, 2(4), 633–656.
Chan, H. H. Y., Henninger, C. E., Boardman, R., & Blazquez Cano, M. (2023). The adoption of digital fashion as an end product: A systematic literature review of research foci and future research agenda. Journal of Global Fashion Marketing, 15(1), 155–180. https://doi.org/10.1080/20932685.2023.2251033
Jain, S., & Sundström, M. (2021). Toward a conceptualization of personalized services in apparel e-commerce fulfillment. Research Journal of Textile and Apparel, 25(4), 414–430.
Nachtigall, T. (2021). Data as a material for fashion. In Data as a Material for Fashion (pp. 1–15). Amsterdam University of Applied Sciences. https://hbo-kennisbank.nl/details/amsterdam_pure:oai:pure.hva.nl:publications/13162f72-6fa8-41a0-ba68-fe3a3bd34157
Nobile, T. H., & Cantoni, L. (2023). Personalization and customization in fashion: Searching for a definition. Journal of Fashion Marketing and Management: An International Journal, 27(4), 665–682.