Algorithms curate the information we look at online and serve us the content they determine we most want based on our behaviour. But how useful is our past behaviour in determining when we want new ideas?

Robert Gentz, Co-Founder & Co-Chief Executive of the online fashion retailer Zalando, in an interview he gave for McKinsey’s ‘The State of Fashion Technology’ report talked about the music industry influencing the fashion industry when it came to creating new products. It was not clear entirely what he meant by this but if we think about the old world of the music industry the ‘next big thing’ was decided entirely by the arbiters of taste who were A&R people, journalists, DJs and studio executives in a process that was always an incredibly imprecise science and packed with stories about the ‘band that got away’ or the ‘best new band you have never heard of’.  

So, when Robert referenced this in regard to “fashion people helping technology people” was he effectively saying the same thing? The cool kids deciding what people should be wearing without necessarily paying much attention to their opinions and certainly not using market research data to help test out ideas and refine them through what is learnt? Old templates of the past need to be modernised, and whereas know-how is still important, data to strengthen gut feel should be the norm. Or was he more likely referencing a new approach that the music industry has begun to rely on to find the next U2, One Direction or Drake?

It’s surprising that market research feeding into new collection design, via a robust test and learn process, is not common in the fashion industry. There’s plenty of information about the use of AI and machine learning when it comes to predicting our behaviour and even in some cases anticipating trends but not so much when it comes to designing new clothes. There are time and cost issues when you consider market research around fashion. Who do you use for the market research? How much to spend on creating patterns and manufacturing garments? How to capture the feedback? Plus, could there be reputational damage if we ask for feedback on an item that may be a flop?

A potential solution to this problem is beNew, be Retail Social’s latest platform accelerator module, that provides customers with hyper-personalized targeted social marketing of a latest collection, or seasonal promotions, dynamically worn by them – but, for the retailer, can include test and learn items even before a single garment is manufactured – allowing brands to reduce risk and make informed decisions before committing to investment. 

Using data, derived from personal history, abandoned baskets and other sources such as lookalike cohorts and promoted items, beNew allows the marketing of data-informed, curated items to customers as well as suggesting both sponsored and upsell items such as bags and accessories. 

Put simply, beNew allows retailers to share with customers – on traditional marketing, social or instore platforms – dynamically moving digital merchandising of their new collections, virtually worn by their customers, personalized by them in terms of appearance and curated by the retailer by newly available sources of data and insight – all without physically making a single garment. Not only will your hyper-targeted customers be more engaged in consideration, but retailers also have market research data based on the customers’ reaction to the new items. Quick, easy and accurate with less environmental impact.  

References

https://cordis.europa.eu/article/id/418235-using-data-to-understand-and-predict-fashion-trends 

https://www.cbinsights.com/research/fashion-tech-future-trends/