Who are your major clients? Which geographies do you see increasing potential in for such fashion technology?
Our largest and most well-known customers include Rakuten (Ichiba), QVC, Billabong and Asda George. While we continue to see demand for our fitting technology in the US and European markets, we are increasingly seeing demand from expanding markets, especially in the Asia-Pacific region. Our most recent customer acquisition carries primarily Indian and Pakistani designer fashion clothing. This significant growth in ecommerce, and therefore the growing interest in our products, is supported by research; the ecommerce share of total retail sales in Asia-Pacific is expected to double by 2021.
Which three major factors are shaping the future of fashion retail?
Awareness of fair and ethical practices: We've noticed an ongoing pressure and commitment to driving fair and ethical practices within the apparel retail industry -- whether it's the environmental impact of apparel manufacturing, the wellbeing of those involved in apparel production, or the impact of their every-day shopping habits. This is something we believe will shape the retail industry going forward and become an important driver of brand loyalty-as shoppers become more aware of these issues, they become more loyal to certain brands whose ethos resonates with them. Recent examples include Levi's Waterless Jeans, whereby a technique is used that reduces water use in the finishing process by up to 96 per cent, and the growth of Toms Shoes. Toms Shoes experienced 300 percent annual growth over five years, and they have given away over 70 million pairs of shoes since inception.
Investing in omni-channel: Data is key to delivering a seamless cross-channel experience to all customers. The polarisation in fashion retail between experiential and transactional continues to grow; Amazon are aggressively shifting their focus towards fashion channel and are offering best-in-class search, purchase and logistics. To compete with this, retailers must offer more engaging and interactive experiences in-store and online to keep direct relationships with their shoppers. Channel fluidity is key here-for example, shoppers want to access consistent product and size recommendations both in-store and online, receive relevant promotions and offers, real-time stock availability and the ability to shop online in-store. We believe if retailers can get this right, omni-channel will continue to play a vital role in retail, as retailers invest in ensuring shoppers have truly unique, engaging and enjoyable experiences both offline and online.
Delivering "hyperpersonalisation": As shopper expectations change, we expect retailers to continually invest in delivering "hyper-personalisation;" where data is continually used to provide more personalised and targeted products, services, and content (both online and offline). Online, one area we believe can be significantly improved is search functionality. Since 70 per cent of shoppers use on-site search when shopping online, it is one area which is already being utilised to make the shopping experience more personal. However, there is potential to further enhance this experience. Our recently-launched product, Fit Match, allows retailers to add fit filters to their on-site search functionality which allows customers to search and filter for clothes which will fit them.
AI has a big role to play in ecommerce. What kind of operational efficiency can AI bring about?
AI, once the stuff of science fiction, is already part of our everyday lives. If you use a smartphone or have Alexa in your home; if you use Google Maps or if Amazon suggests products for you to buy, you're a user of artificial intelligence. In ecommerce, AI is becoming increasingly common, even essential, in every part of the shopping experience.
Machine learning, a fundamental building block of artificial intelligence, is the process of having machines learn from data, to create intelligent behaviours such as reasoning, planning, recognising pictures and understanding speech and language. For a machine to learn in this way, it needs huge amounts of data as well as massive processing capabilities. With the continual decrease in cost of processing power and data storage, ecommerce companies have been able to collect and keep more and more data about their business, their products, their customers and their shopping behaviour. Enabled by these massive data streams and the ability of machines to find patterns within them, ecommerce examples of AI abound: customer re-targeting, virtual personal shopping assistants, intelligent search, product recommendations and much more have come to be expected by the internet shopper. Intelligent machines offer great opportunities for improvement in operational efficiency for ecommerce business, as well as technology partners like Rakuten Fits me. Here are a few examples:
Personalisation: Much of the AI employed in ecommerce is used to increase the level of personalisation for the shopper. By understanding customers and targeting them with carefully selected and individual products, it's possible to increase sales, reduce returns, improve stock management and much more. At Rakuten Fits me, we enhance personalisation, using our machine learning algorithms to help shoppers find clothing that fits and flatters their individual body shape and size.
Intelligent search algorithms: Personalised, intelligent search helps shoppers find the right products fast, leading to fewer product returns. At Rakuten Fits Me, we use machine learning to guide shoppers straight to the products that fit and flatter their individual body shape. The result is happy shoppers who keep what they buy.
Image recognition and classification: Machines can process and classify images much faster than humans, and deep learning algorithms enable them to "learn how to learn", giving better and better accuracy over time. To make our fit recommendations, we categorise garments to understand their cut, fit, fabric type, stretch and other properties. Automated image recognition massively improves the speed, throughput and accuracy of the categorisation process.
Analytics and data mining: Data mining algorithms examine and process the huge amounts of data captured every day to find new patterns and derive new information from old. Using data from product catalogues, shopper body data and shopper behaviours, we can show our clients how small changes in their design and manufacturing can increase their sales and reduce over-stocks and logistics costs.
Virtual shopping assistants and chatbots: Running customer service teams is expensive and complex. Automated chatbots and shopping assistants can resolve customer queries and help shoppers find their perfect products quickly. By training machines to help shoppers, the cost and complexity of a good customer service can be controlled.
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