Personalisation the future of the fashion industry — SwiftERM

  • Challenge: Changes to data privacy laws and restrictions on third-party data collection in various jurisdictions have rendered data management platforms and third-party cookies less relevant.
  • Solution: Brands need to maximise their first-party data collection to enable personalisation across platforms and channels. This can happen, for example, through loyalty programmes that help identify and link customer purchases online and offline. In-store apps can also track offline browsing behaviour, and brands can create campaigns that collect data in exchange for loyalty points or discounts. In these efforts, brands need to be mindful of adhering to data privacy regulations (e.g. GDPR in Europe).
  • Challenge: When shopping for fashion, customers can generate a vast amount of data across channels and platforms — ranging from location data to website or app engagement time. This data tends to be unstructured, in multiple formats and scattered across different databases. In isolation, this provides little or no insight.
  • Solution: Brands need to establish a complete customer profile connected to a unique ID across data sources and channels. A customer data platform is needed to host all data assets and consolidate the customer view, as are rigorous data standardisation and cleaning processes. The result is brands could create a single dataset that joins up customer preferences and behaviours at a granular level across platforms, channels and product categories. For retailers, this could also span data from different brands. Companies should consult current data legislation when creating these customer profiles.
  • Challenge: Fashion customer behaviour can be difficult to predict, not least because of fashion’s rapid trend cycles and the low levels of repeat purchasing among individual shoppers. Furthermore, a stand-alone personalised algorithm might not align with a brand’s strategic priorities without human intervention.
  • Solution: Players need to develop advanced AI models, such as those that display products and photo styles best suited to the individual customer, or models that use advanced size and fit algorithms. These models should incorporate behavioural data, such as login time and add-to-cart behaviours, along with nuances relating to the brand’s market and segment positioning.
  • Challenge: A significant platform upgrade is required to deliver sophisticated, hyper-personalised e-commerce content, which is informed by thousands of data points and delivered across multiple channels with ultra-fast loading times.
  • Solution: A company’s portfolio of design and distribution tools needs to include content management systems that can standardise, centralise and distribute digital elements to support marketing alongside content delivery networks that help deliver thousands of unique landing and content pages. Their portfolio should also include an e-commerce platform for their website and app, so that brands can deliver personalisation to every customer across all journeys.



Founder & CEO of SwiftERM the personalization SaaS. Microsoft partners.

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