shiwaneeg

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

Full Bio 
Follow on

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

Why is there so much buzz around Predictive Analytics?
467 days ago

Changing Scenario of Automation over the years
468 days ago

Top 7 trending technologies in 2018
469 days ago

A Beginner's Manual to Data Science & Data Analytics
469 days ago

Artificial Intelligence: A big boon for recruitment?
470 days ago

Top 5 chatbot platforms in India
36270 views

Artificial Intelligence: Real-World Applications
23076 views

Levels of Big Data Maturity
14088 views

Challenges of building intelligent chat bots
13521 views

Chatbots' role in customer retention
13044 views

Tech revolution in fashion industry

By shiwaneeg |Email | Apr 11, 2018 | 9054 Views

The industry has reached a point where machines can leverage enormous amounts of online user data from E-commerce, social media, and smartphones to understand and foresee real-life trends, and in return, refresh their products and customize the shopper's experience.

Fashion industry have always been a platform for innovation, right from the invention of the sewing machine to the rise of e-commerce. This industry is forever forward-looking, proactive and cyclical. It is considered to be one of the largest industries in the global economy.

Technology is transforming fashion at a faster pace than ever. Right from robots that sew and cut fabric, to AI algorithms that predict style trends, to VR mirrors in dressing rooms, technology is automating, personalizing, and speeding up the development of fashion and the fashion industry.

Fashion brands are using technology extensively to understand customers and provide a better customer experience. Artificial intelligence has the ability to reshape brands' approach to product design and development, with a focus on predicting what customers will want to wear next. And, there is a requirement of more R&D to create and iterate their designs more quickly. With an ever-increasing amount of data about sales and consumer preferences available, AI is able to transform the overall retail experience in a way that appeals to the today's customers. 
1. The fastest way to find your look:

AI-driven visual search helps shoppers to promptly find their desired fashions. Pinterest recently launched a visual search tool called Lens, which uses machine vision to detect items on the web or in the Pinterest library and suggest related items, like a Shazam for products. Neiman Marcus launched the AI-driven Snap. Find. Shop. mobile app; customers use their smartphone cameras to take pictures of an item they like and the app displays similar items from the store's inventory. Retail brands using visual search for a superior online shopping experience include Asos, John Lewis, Shoes.com, Nordstrom, Hook (an aggregator of all brands) and Urban Outfitters.

2. Hyper-personal product recommendations:
For individualized service, IBM Watson partnered with The North Face to ask shoppers questions about their gender, time of year and technical product details, to tailor product recommendations. Blending AI and a human touch, style service Thread asks customers to complete a questionnaire and upload images of themselves. A stylist reviews the information to understand each client's needs and uses the company's AI algorithm to sort through thousands of products for personalized style suggestions.
3. Unprecedented omnichannel service:

Farfetch, the world's top luxury online marketplace, uses AI to improve supply chain visibility. AI helps Farfetch's partners, including 1,500 boutiques and over 200 brands, link their online inventory with inventory in their physical stores, and deliver services like click-and-collect and in-store returns. Also, AI-driven bots help retail companies engage consumers across channels, like Facebook, Slack or a retailer website. Burberry launched a Facebook Messenger bot during London Fashion Week to offer exclusive glimpses of the new collection before the runway debut, plus live customer service so users could buy the clothes immediately.

4. A savvy social sales assistant:

Pinterest also launched Shop the Look, a machine learning tool that identifies pinned items shoppers can buy, including fashions from major retailers. The insightful data analytics from Shop the Look can also tell retailers whether a sponsored post on the social media platform results in a sale.

5. Shop like a stylist:
To give tech-savvy, fashion-conscious millennials and Gen Zs a mobile personal stylist, the Hook team at Intelligence Node created the first AI-generated fashion feed for consumers. Hook learns, in real time, what a shopper likes just by analyzing which product images the individual has liked or added their online wishlisted, so shoppers find items they love, fast. The app sends shoppers real-time price drop alerts on their wishlisted or liked products. Hook helps consumers shop like a sophisticated stylist by gaining unprecedented search capability to find a specific item they want with a single view of the item across brands so they can compare prices and get the best deal.
6. The ultimate trendspotter:

To give retail companies certainty on up-to-the-minute fashion trends, AI can crawl e-commerce sites to pinpoint exactly which products are most visible. AI can also crawl social media sites to identify trends, helping brands be first to market with popular styles. Intelligence Node built the world's most comprehensive retail crawling framework like Google' for fashion. The system crawls more than 1000 websites and maps one billion products from 130,000 brands in 1400 categories in real time, using AI and machine learning for image recognition to rapidly process and make sense of the abundant retail market data.

7. The best way to ensure the price is right:

Big data tools like Incompetitor,  helps retailers maintain a competitive pricing strategy, combining AI and machine learning. Retailers integrate this API into a brand's data, so there's a rules-based engine that keeps prices optimized, depending on real-time external factors like inventory, competitors' out-of-stock situations and discounts. This integration allows retailers to optimize their price and, for instance, ensure they always sell a specific SKU 10% cheaper than Amazon.


This article was written through research and analysis from various sources.

Source: HOB