AI tailoring style and shopping in Asia
- Fashion retailers in Asia are tapping AI and data analytics to enhance their customer experience and fuel their market expansion
- Emerging tech is becoming a memorable part of the fashion retail industry
- For companies to convert likes to dollars, the X-factor now is trending towards composing a highly individualistic customer experience powered by AI
AI is the new black in Asia. Why? Because replying with “just pick anything” to the question “what should I wear?” is not going to cut it.
For the style-conscious, a lot more thought is put into selecting a piece of garment. What’s the occasion? Is it indoor or outdoor? Is there a particular dressing etiquette to observe? Will it look flattering and suit the skin tone? Will the outfit match the handbag or the scarf or the shoes? The shoes!
That’s just a glimpse of the mini decisions being made before settling on an ensemble. The kind of processing warrants shopping with a friend or the current digital-savvy alternative, artificial intelligence (AI).
Tapping into AI and data analytics in Asia
Fashion retailers are tapping AI and data analytics to enhance their customer experience and fuel their market expansion. The technology is increasingly being adopted within the industry for many use cases, from spotting trends to helping customers find the right fit from size to style.
Singapore-based womenswear brand Love, Bonito uses its data pool on customer preferences accumulated over the years to track what works and what doesn’t to better design and curate their clothes offerings.
It is also looking to develop an AI-based subscription service in Asia to deliver specific items according to its customers’ profiles and the season.
“Do we add, sort of, this length of the sleeve? Do we change up the hem of this skirt, for example? By doing all these changes, what is the probability, right, of improving sell-through, of improving performance? So that’s the first step upstream,” Dione Song, CEO of Love, Bonito, told Nikkei Asia recently.
The company, already established in neighbouring Malaysian and Indonesian markets, is looking to penetrate Hong Kong, Japan, the Philippines, and the US following its $50 million Series C funding round.
Deploying AI for real-time decisions
Meanwhile, Pantaloons, one of India’s largest value fashion retail brands under ABFRL (Aditya Birla Fashion and Retail Ltd), is deploying AI for real-time decisions and stitching up hyper-personalized omnichannel shopping experiences for its customers.
It is partnering with Algonomy, the only algorithmic decisioning platform for retail, to leverage insights derived from analyzing shopper behavior and other data to create a more individually-relevant service to its customers seamlessly across all its consumer channels – website, app, email, in-store, and other commerce journey touchpoints.
The customers can receive recommendations for products based on top regional sellers or complement their past purchases and access to a style finder and complete-the-look features.
Pantaloons is part of ABFRL’s network of 3,264 stores across approximately 26,841 multi-brand outlets, with more than 7,094 points in department stores across India.
Emerging tech is becoming a memorable part
“For us, it’s important to understand each customer, their intent and context in real-time and personalize their experience at a 1:1 level. Manual merchandising and segmentation cannot handle the scale, and as a result, e-commerce suffers from low conversions. AI-driven, real-time tailoring of our webstore is here and now – and with Algonomy, we get an integrated suite that meets our future needs as well,” said Varun Rajwade, AVP – Product & Digital CX at ABFRL.
Emerging tech is becoming a memorable part of the fashion retail industry. The clothes don’t only need to fit well and look good; they need to sell. For companies to convert likes to dollars, the X-factor now is trending towards composing a highly individualistic customer experience powered by AI. And that’s the tea. Get in the game or be gone, honey.
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