Unless you’ve been living in a tropical forest with no internet for the past few years you’ve probably heard of Artificial Intelligence.
AI is the simulation of human intelligence processes by machine, usually computer systems. It includes learning, reasoning, using data to elaborate conclusions, self-corrections and improvements. We are talking about technologies that can make connections and reach meanings without relying on pre-defined algorithms, that can improve and getting smarter on past iterations, enhancing its capabilities and knowledge.
Sounds scary?! And we haven’t mentioned that they have been shown able to elaborate their own language, communicate with one another and make the conversation impossible to understand to humans. Seems like the sci-fi apocalypses are moving from screens to reality.
Stephen Hawking in 2014 declared that AI “would take off on its own and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.”
Thankfully, this is not the case today, and machines have not taken over, not yet. But they are definitely filtering into all aspects of our lives, affecting the way we live, work, shop and entertain ourselves.
Being Retaily.org about retail(y) we are going to focus on the way AI affects the way we shop, which technologies are already part of our lives and the future implications for retailers.
Artificial intelligence technologies are integrated everyday more into bricks and mortar stores, mainly to customize the offer of products and services. Brands are investing accordingly: retail spend on AI is expected to grow from 2 billion in 2018 to 7.3 billion by 2022, according to a Juniper Research. The research found that “retailers will heavily invest in AI tools that allow them to differentiate and improve the services they offer. These range from automated marketing platforms that generate tailored, timely offers, to chatbots that provide instant customer service.”
A global study last year from the Capgemini Research Institute found that in 2018 28% of retailers deployed AI, up from 17% in 2017 and just 4% in 2016.
Sentiment analytics and customer service are the sectors where AI is more commonly applied to understand customer reactions to the products purchased and the service received. They record today the strongest spending from retailers, but brands are already applying AI technologies in different parts of the supply chain and shopping experience, with different purposes:
1. Inventory planification and management
Having the right stock is vital in business. In order to achieve that, retailers should be able to predict the future demand, both in quantity, quality and time. Machine learning algorithms use historical data to make predictions and choices, helping retailers reducing forecasting errors up to 50%, while reducing inventory by 20-50%.
The technology can manage inventory both across e-commerce channels and in store, getting products to customers more efficiently.
Jeans retailer Levi’s is using Detego, an inventory management software, to track all inventory and create planograms that recommends sizes and placement of the inventory within a store. The algorithm also learns if and when a specific size is in demand in a store, and it is able to adjust future inventory allocation. It also groups items together based on which products are most likely to be sold together.
By using this tool sales usually go up to 10%, declares Luke Sinclair, marketing executive at Detego.
2. Connect with customers
Several companies are already using AI to communicate with customers. Ever noticed a little chat bubble popping up while browsing on a website? These are the so-called chatbots, or smart assistants, and they represent a way in which brands can save money on customer service while building customer loyalty.
3. Tailor recommendations and reducing returns rate
Returns are a pain for both shoppers and retailers, but in the fashion industry is a critically big problem, with clothing returns taking approx. three times longer to inspect and brands are losing about $642.6 billion every year from returns.
AI can intervene in preventing returns by helping the consumer to make a more informed purchase decision: online fashion retailer ASOS, uses AI to recommend clothing sizes based on what shoppers have previously bought – and kept.
Stitch Fix is a fashion subscription start-up which uses AI tools to analyse the customer profile and then picking, among millions of combinations of clothing, items that fit the customer’s budget and taste. Stitch Fix delivers the selection of clothes to customers once a month, customers can keep what they like and return the rest. The company collects feedback and data to provide a more tailored recommendation the next month. (https://algorithms-tour.stitchfix.com)
The North Face employs a similar mechanism with the help of IBM’S Watson, using voice input technology. They ask to customers when and where they plan to wear the jacket they are looking for, then Watson scans through over 350 products to find the ones that fits best to the customers’ need.
Meanwhile, Net-a-Porter is testing technology that scans customer data for information on people’s upcoming trips and events, and uses that to tailor its recommendations.
A step beyond is taken by Echo Look, Amazon’s virtual fashion assistant, that not only gives recommendations items to buy, but analyse the outfit the users are wearing.
4. Improve products discovery.
The simplest and most well-known example is the suggestions section offered by Amazon. The “Inspired by your shopping trend” or “Because you bought…” units are today very common in several e-commerce platforms. These tools are critical to offer a personalized experience to the customers and, meanwhile, pushing sales.
But AI can help the customers the other way around: how many times have you found something you like online but you do not have the right words to describe it to Google search? Visual search makes this easier, with the possibility to upload a photo of the product you want and have it identified by AI across multiple sites.
Other than visual search, companies (such as North Face or H&M) are experimenting also with voice search, following the diffusion of voice-activated digital assistants as Amazon Alexa and Google Assistant.
H&M offers voice search through H&M Home Stylist tool, providing styiling advice by asking what kind of theme or style users are interested in. Last December they improved the function creating H&M Home Gift Guide, which enables users with a nearby H&M Home store to look through the catalogue and tell Google Assistant what they want. They can also link their credit card to their Google Account in order to buy H&M Home products via voice command.
Recommendation engine is the most used solution among the retailers, accounting for more than 35% share in the revenue. The growth of the recommendation engine market is credited to the rising demand for the personalized shopping experience among the customers. Furthermore, the demand for the visual search solutions is estimated to grow at a CAGR of more than 45% during 2018-2024.
5. Fashion and trends forecasting
In order to keep costs low, brands need to better predict customer preferences by gathering and analyzing purchase data. Using this data alongside AI and machine learning allows fashion retailers to create clothes that customers want to buy when they want to buy them. Getting the right products in front of customers as quickly as possible requires analysis of product attributes, such as size, shape and colour.AI has accelerated the business of fashion forecasting and trend spotting with its ability to scan social media and the web, and analyse data such as the colour, size, style and pattern of clothes.
Yoox Net-a-Porter Group created for its own-label brand, 8 by Yoox, clothes that are designed with input from in-house AI tools. After analyzing social media and websites, it picked up the most buzzed-about trends and stylesto create the collection.
6. Changing the role of staff.
One of the most controversial consequences of AI technologies is represented by the “stealing” jobs and functions previously covered by humans.
“Today, any task that requires a basic analysis of data is going to fall into the capabilities of AI” says Doug Stephens, founder of industry forecaster Retail Prophet. “A lot of the rudimentary product knowledge that retail associates have been responsible for and a lot of data-driven tasks, such as inventory management that floor employees are responsible for are going away.”
In 2018 Zalando cut around 250 marketing and communication roles, replacing them with algorithms and AI; whereas Uniqlo replaced 90% of the staff in one of its warehouse in Tokyo with an automated system formed by robots technologies that do everything from identifying and scanning products to move items around the place.
Artificial intelligence is infiltrating physical retail, and it has incredible potential to simplify and improve many aspects of the consumer’s experience. Although it is advanced enough to help in various aspects of the shopping process, today it still has some limitations, mainly around the quality of data and information given. Nevertheless, it is quite noticeable that their presence is going to increase, providing further game-changing options for retailers. In the future brands working with AI technologies willing to integrate them into omnichannel strategies, will be able to see trends before time rather than responding to them; and knowing their customers’ needs and desires better than consumers themselves.
https://qz.com/1419418/uniqlo-cut-90-of-staff-at-one-warehouse-by-replacing-them-with-robots/
https://algorithms-tour.stitchfix.com
http://guides.drapersonline.com/6012.guide
https://www.voguebusiness.com/technology/artificial-intelligence-physical-stores-kering-nike-alibaba
Blonde Problems 