RESEARCH PROJECT
ANASTASIA SOROKINA
MAKING FASHION BUSINESS “SMART” HOW ARTIFICIAL INTELLIGENCE IS CHANGING RETAIL
Nottingham Trent University School of Art & Design BA (H) Fashion Marketing & Branding Year 3 Semester 1 January 2018
CONTENTS
INTRODUCTION 1
LITERATURE REVIEW 2 METHODOLOGY 4
AI IN OUR LIVES TODAY 6 MOST AFFECTED INDUSTRIES 11 FASHION RETAIL TRANSFORMATION 12
CUSTOMER ATTITUDE TOWARDS AI 15
POSITIVE AND NEGATIVE IMPACTS OF AI IN RETAIL INDUSTRY 16 CONCLUSION & RECOMMENDATIONS 17
BIBLIOGRAPHY & LIST OF ILLUSTRATIONS
APPENDIX
INTRODUCTION
Artificial Intelligence has been a topic of different researchers’ interest for more than half a century already. However, the industry of AI has experienced significant growth of investments from either tech giants like Google, Facebook, Uber etc. or determined companies-adopters only last decade. Since 2013 the external investment growth has tripled (McKinsey Global Institute, 2017). This signifies that AI is no longer just a favorite subject of discussion for futurists and scientists. It is a business that gradually changes the game in many industries. The adoption of it by the companies remains relatively low comparing to the investment it gets, therefore it is a great moment for companies to become ahead of rivals that are still hesitating or don’t consider implementing AI tools at all. The question is how to do so and whether it worth to start now. Retail industry becomes one of the strongest adopters as finance and entertainment/media industries too. Fashion is a substantial part of world retail industry and this work focuses on how fashion companies are being affected by AI and what opportunities along with challenges they acquire using such tool. This is the first stage of the research aimed to highlight the latest updates in the indicated field in relevance to Artificial Intelligence and identify the objectives for a further exploration. There will be insights based on consumers perception and psychology towards AI in their shopping experience and daily routine, and following recommendations to the brands that aspire to become more data-centered, efficient and desirable for the consumer.Â
LITERATURE REVIEW Rapid growth of AI has been a consequence of digital connectivity and merging biology and science knowledge to the latest technology achievements which brought artificial neural networks to the world. Today AI becomes selfenhancing and self-educating which marks the beginning of Machine Learning (ML). The term Artificial Intelligence does not have one and only definition, in fact it is a collective term for the machine that on the one hand can think and act rationally and on the other, can do it humanly. In the book “Artificial Intelligence: A Modern Approach” (2010) Stuart Jonathan Russell and Peter Norvig break down the definition into four dimensions and discuss the meaning of each.
If behaving and thinking rationally based on data is intrinsic to computers, making logical and associative connections along with capability to read emotions that inherent to humans is a great and seemingly impossible task for a tool. The measurement of machine capability to act and think like a human was proposed by famous Alan Turing, the same person who was a main contributor to breaking the code of German cipher machine called Enigma during World War II. Turing designed a test which could evaluate “the intelligence” of the computer. If a person can not identify whether interrogated is a machine, then the test is passed.
However now, passing original Turing Test is not the main objective for developers of AI powered software. Philip Ball argues in his article for BBC “The Truth about the Turing Test” (2015) that a computer answering in reasonable manner can not prove that the machine is actually thinking. Different psychological human factors and the database behind can become very serious limitations. So, the human can be fooled that machine actually thinks through the conversation but it is only acting alike. Russell and Norvig put down the disciplines that compose most of AI - natural language processing (NLP), knowledge representation, automated reasoning, machine learning, computer vision and robotics. Each of the research areas had its ups and downs, some of them like robotics, computer vision and NLP has been the most popular AI directions for investments and development for a decade. Others are having their upturn only now such as machine learning. Machine Learning represents completely different approach to creating “smart” software. Erik Brynjolfsson and Andrew McAfee in Harvard Business Review article called “The Business of Artificial Intelligence” claim that ML is a beginning of the huge breakthrough - previously developers were focused on embedding human knowledge in machines but there was a huge limitation which they emphasize. They point out to the Polanyi’s Paradox - inability to share tacit knowledge like “how to recognise a friend’s face”. Therefore, ML is a crucial step towards smart software - it can learn through its own experience and trials, finding the most optimal solutions. Louis Columbus, Principal of IQMS Software, manufacturing enterprise resource planning (ERP) company highlighted by Gartner as FrontRunners Quadrant, talks about huge investments in ML in Forbes article “McKinsey's State Of Machine Learning And AI, 2017”. Relying on the latest McKinsey Global Institute Analysis, he claims that ML software startups are more favoured now than robotics counterparts due to the high cost of the second.
In 2016 ML-oriented companies received 5 to 7 billion US dollars comparing to Computer Vision ($2.5-3.5 b) or Smart Robotics ($0.3 - 0.5 b). Coming back to the HBR article, the authors make a clear statement that ML is driving changes in business at three levels: tasks and occupations, business processes, and business models. However, they don’t believe that such changes can lead to the complete replacing of human sources. It is a tool that can complement human work with some automated tasks/processes/models. Human decided what limitations to put to such tool and where it is relevant to implement. Just like Jim Stern says in Chapter 8 of his book “Artificial Intelligence for Marketing” that hammer is not a carpenter - AI won’t replace decision-making in near future and, also, while people control it. No doubts, that tech progress always disrupt workforce and some work positions disappear due to lack of cost-effectiveness. However, new specialists are needed. In New York Times the author of “Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent“ Cade Metz talks about huge demand and shortage of AI talents in job market. Almost every big tech company has AI project and because there are not much professionals in that area, the salaries are spiraling. Professor Dame Wendy Hall and Jérôme Pesenti give different recommendation on improving access to data, improving supply of skills, maximising UK AI research and supporting uptake of AI in October independent report for UK government called “Growing the artificial intelligence industry in the UK”. The development of AI and seeing it as an important part of business strategy is inevitable today. It seems to be one of the most perspective directions to work on when the world is already so digitally connected.
METHODOLOGY
Research Design The purpose of this study is to understand what AI market can offer to the fashion retail industry, how good is ROI in companies who deploy AI tools and how consumers feel about it. For example, using chatbots in ecommerce business - how effective it is and what opinion customer has on that. To fully explore the area, research is also directed towards comprehension of AI business as a whole and what other industries aside from retail are being affected the most.
Research Approach In this research mixed methods were used - both qualitative and quantitative. Such approach was chosen to explore not only AI performance today but to understand what consumers and professionals think and how they see the changes from their perspective. Primary research consisted of online survey done through Typeform, in-depth interviews conducted in mobile messengers with industry insiders and face to face interviews with millennials. Explicit secondary research included different sources such as books about AI and consumer behaviour, recent publications of professionals on the latest updates in the industry, articles from respectable newspapers and online portals and reports from reputable research companies.
Sample and Methods The study did the accent on mostly millennial consumer as they become the the biggest spenders now. However, the survey did not limit any age group aside from those under 18 - they had to resign. To get a global perspective, geography was not limited as well including respondents mostly from UK, USA, Singapore, Russia and France. Sampling size for the survey was one hundred people. Sample size of face to face interviews with millennial consumers was 5 people. Also, there were two big insights from the industry that contributed to the research development. The first one was provided by Shawn Tan, a store manager at MNC, “Lumine” in Singapore. Lumine is a Japanese fashion mall which opened the first international destination in Singapore recently in 2017. Before that Shawn was a manager in multi label concept store “Manifesto” that worked with mostly Scandinavian, French and Singapore brands. The store had a partnerships with Farfetch and some of the biggest Singapore fashion department stores. Being a specialist in customer service, merchandising, buying and operating a store - he became a great candidate for an interview. The second one was the insight from AI business called “nuTonomy”. nuTonomy is the company based in Boston, MIT spinoff that develops automated vehicles. The company had self-driving test launches in collaboration with Singapore car-sharing and taxi app in Singapore as well as the launches in Boston. Philipp Robbel, is the Head of Safety and Validation at nuTonomy, he holds a Ph.D. from MIT and a Masters Degree in Artificial Intelligence from the University of Edinburgh.
Fig. 5
In 2017 AI is everywhere, even if it does not seem to be obvious, it became a big part of our lives mainly because it is in our pocket. Mobile phones are a portal to the world of Artificial Intelligence. (Intel, 2016)
Siri, Google Now, and Cortana are all intelligent digital personal assistants on various platforms (iOS, Android, and Windows Mobile). They do not have the capability to learn, therefore the pattern of answers is very predictable and programmed by developers. However, speech recognition has improved dramatically over recent years and such tools help to save time and use the phone while you can’t hold it (for example, riding a bicycle).
Games like Far Cry and Call of Duty also make significant use of AI - enemies can analyze their environments to find objects or actions that might be beneficial to their survival; they will investigate sounds, use special maneuvers and communicate with other AIs to increase their chances of victory. In such games AI is powered by  Finite State Machine (FSM) algorithm in which its designer generalizes all possible situations that an AI could face and then programs the reactions to each one (Lou, 2017).
Driverless cars are not the part of our routines yet every year we see trials and improvements. Philipp Robbel from the company developing safe autonomous cars nuTonomy says in the interview (Appendix 2) that for restricted, lowspeed environments, autonomous vehicles will officially roll out over the next 2 years or by 2021 at the latest. For fully autonomous highway driving, it should be a similar timeline. Many companies are targeting 2020/21 but it's still an open question whether the industry can achieve this without having safety drivers in the front seats monitoring the system.
Accuracy in purchase prediction can lead to the elimination of shrinkage and prompt satisfaction of the customer. Millions of dollars can be saved along with sources used for transportation. Not only tech giants like Amazon use AI software to predict the demand but companies like Otto from Germany. In April 2017 e-commerce firm Otto used the algorithm that inhaled data on nearly three billion past transactions and used 200 different variables to predict what customers would order—before they bought it. (The Economist, 2017). The company achieved 90% accuracy in predictions what consumers will buy.
Chatbots do not surprise in 2017. However, 39% of people that have done the research survey (Appendix 1) and many of them claim it still feels like talking to robot and conversion is not meaningful enough. Through face to face interviews it was noted that participants are quite indifferent to chatbots (Appendix 3). Nevertheless, there are plenty b2b AI companies focusing on online customer support. DigitalGenius is one of them but it uses a deep neural network, therefore claims to empower customer service excellence. Lots of respectable companies are the clients of it - Unilever, Royal Dutch Airlines, Eurostar, HSBC, Panasonic, BMW and others.
A lot of news readers might have no idea that some of the writing is done by AIpowered software. One of the successful examples is Wordsmith natural language generation platform generates human-sounding narratives from data. Hundreds of customers like Microsoft, The Associated Press, Cisco, Yahoo, and PwC use Wordsmith to generate more than 1.5 billion pieces of content per year.
By monitoring the choices on Spotify or Netflix people make and inserting them into a learning algorithm, AI gives the recommendations. On the scale from 0 to 5 the average Netfix recommendations accuracy of 2.89 was rated by survey participants. Nevertheless, it is not a bad result and for entertainment and retail industry “smart” recommendations leverage the engagement and sales.
Many smart home devices now include the ability to learn a person’s behavior patterns and help to save money, for example, by adjusting the settings on your thermostat or other appliances in an effort to increase convenience and save energy. Lighting is another place where people might see basic artificial intelligence. By setting the preferences, the lights around the house might adjust based on where you are and what you’re doing. Internet of Things is at its early stage but in not very far future it will a norm to connect devices from your alarm clock to your car.
Today computers learn how to detect similar objects on the images and we can see the results via performing image Google Search or Pinterest search. It is not 100% accurate but even a human being is not. Image recognition improves each year and shows great results. For example, Snapchat and Instagram use it for face filters and Facebook for automated tagging.
Fig. 6 Computer get confused whether it is a puppy or muffin
THE MOST AFFECTED INDUSTRIES No doubts that in long term AI will change in some ways all industries in the world. However, not all of them are ready for a rapid change and sometimes AI achievements are not that perfect to start embedding it into the business models worth of billions. Financial companies like banks and investment firms are among the earliest adopters of deep learning. Many are already using it to augment investment research, improve investment performance, and strengthen fraud detection, whereas others are in the process of implementing AI. Some of the examples from finance industry are Paypal which has a deep learning system that filters out suspicious merchants and cracks down on sales of illegal products (Forbes, 2017). AI became the huge part of travel industry. Making data-driven recommendations, providing dynamic pricing which depends from variety of factors from weather to user booking pattern and assisting customers through AI-powered software for travel industry companies already changed travellers behaviour. Before AI tools they had to go to the agencies to get the best option, now it is easy to do by yourself  and sometimes less time-consuming than asking for help of a travel company. Another industry that seeing benefits from AI already is retail. Artificial intelligence in retail is being applied in lots of different ways. In manufacture General Electric’s Brilliant Manufacturing software was designed to make the entire manufacturing process more efficient. Making accent on the industrial Internet of Things the tool solves different operational challenges. Procter & Gamble is already using it. Other aspects as payment services, logistics, customer service and analytics are also very important; however, retail companies do not hurry to try it all. Step by step they do trials and try to understand what works for the specific company the most because only huge corporations can invest in every AI tool, for others it is a rough and quite risky decisionÂ
FASHION RETAIL TRANSFORMATION
Even though the online sales are growing, store experience is considered to remain influential. All of the face to face interviews’ participants mentioned that they like to shop in store to feel the product and catch the mood of the brand, however shopping online is easy as never. People come to the stores or ecommerce websites, instantly check prices in other places and leave if they find a cheaper option. The issue is to sustain the right pricing, constantly analysing the market and getting a loyal customer. In this case “smart” retail analytics is the way to achieve it.
Retail Analytics One of the companies that does a great job in retail analytics is called Edited. It is used by numerous famous companies like Debenhams, Farfetch, Tommy Hilfiger, Topshop and others. It helps to optimize assortments, maximize margins, identifying effective promotional strategies and eliminate blind spots in the market. Both of interviewees from the industry emphasised the importance of AI analytics and said that most of the companies already use it not only for ecommerce but for brick-n-mortar stores too.
Customer Service and Experience IBM claims that by 2020 85% of all customer interactions will be handled without a human agent (IBM, 2017). It is hard to believe right now, however we can see how chatbots are being upgraded at online shop destinations and androids like Pepper acting as shop assistants. Through different research methods it was straightforward that people don’t perceive such androids seriously. Humans need human approach especially when shopping for fashion. Also, robots are a big investment and if there is no return, companies lose interest and faith in it. Today all the bets are on software which can personalise the shopping experience as much as possible. North Face, for instance, has adopted IBM Watson’s cognitive computing technology to help consumers determine what jacket is best for them, based on things like location and gender preference. Published 2015 pilot results, based on data collected from 55,000 users, resulted in a 60% click-through rate and 75% total sales conversions (Faggela, 2017). Another thing which is the part of customer journey too is payment service. Amazon is trying their latest innovation such as Amazon Go, where you only scan your phone when enter a store, take what you need and go because of the sensors and thousands of cameras spotting that the product was taken. The idea of check-out-free services is fascinating to the consumers and if fashion retail could employ it too, the cashier lines could just disappear.
Supply Chain Optimization Getting the right stock, at the right place, on the right time and the lowest cost has been the major problem of the supply chain in any retail business. With rising demand for new trends and styles optimizing it would enormously help to satisfy the customer before he asks. One of AI companies who does it is “Vekia� based in London. Vekia enhances the supply chain of hundreds retailers improving sales by 50-90% and reducing the inventory by 30%. It does store and allocation replenishment, sales forecast and demand managing, stock optimization and supply chain management overall. In fact, making logistics more productive and supply chain organised could save a lot of workforce, money and natural resources. Kiva robotics (now Amazon Robotics) is known for its tremendous success in being deployed to Amazon supply chain. The company makes robots that automate picking and packaging products at large warehouses. As e-commerce grows, delivery is one of the main priorities to look at and improve. Again, Amazon started moving towards fast and cheap delivery and decided to use drones for that. Amazon Prime Air is an innovative delivery aimed to perform in 30 min.The method is very green, however, the corporation keeps testing it and looking for improvements.
CUSTOMER ATTITUDE TOWARDS AI
Throughout this research study one of the main questions was How consumers feel giving out their data? Do they realize how much data is being collected for analytical and security reasons? 49% of respondents chose in the survey that they would feel uncomfortable if the stores start collecting data through the face recognition tool (Appendix 1) and opinions of people that were interviewed divided on giving out the data by logging in to get a free wi-fi. Some did not care, others use 4G and enable the mode “do not track”.
Consumers are excited about innovations but believe that AI can not replace human in customer service in fashion retail as they need human interaction in store. They are omni-channel shoppers and go to the store for a experience not necessarily for buying a particular product. They do like a movement towards complete personalization but believe that their data must be secured and T&C of collecting it should be regulated by the government.
POSITIVE IMPACTS
POSITIVE & NEGATIVE IMPACTS OF AI ON FASHION RETAIL
NEGATIVE IMPACTS
OPPORTUNITIES OF USING AI IN FASHION INDUSTRY
Store experience is one of the greatest opportunities to apply AI tools. Farfetch “Store of the Future� can give a customer exciting and futuristic feeling with instant automatic customer recognition at the entrance, digital mirrors with options to choose sizes, colours and directly check out, RFID-enabled clothing racks. Using such tools will definitely drive the traffic to the store, especially if a customer can order the delivery within the same day after trying in store - at such case shopping will no longer mean carrying heavy bags around. Another fascinating opportunity is to make a meaningful change in the industry like minimizing the waste and water usage, forcing AI to find a golden mean of demand and supply when neither of them exceeds.
CONCLUSION & RECOMMENDATIONS Today AI industry is definitely experiencing huge buzz. After breakthrough in machine deep learning when Google DeepMind programme AlphaGo beat world champion at Go people started seeing Artificial Intelligence in a different light. Now, machines can learn. They just require some time to do it and people that guide in areas where it can be the most beneficial. Nobody knows how transition to AIpowered systems in every industry will happen. In retail, AI is not a surprise - it has been in POS systems and analytical software like Shopify for a while. However, specifically ML is not implemented anywhere yet but before doing so, fashion businesses should consider following recommendations.
REFERENCES & APPENDIX
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List of Illustrations Fig.1. (2018). [image] Available at: http://i3.wp.com/bestmuscle.pw/wp-content/uploads/hanging-clothes-racks-Beautiful-doublecanvas-wardrobe-rail-clothes-storage-cupboard-Wooden-Clothing-Rack-awesome-vonhaus-double-canvas-wardrobe-clothescupboard-hanging-rail-storage_horrifying-double.jpg?resize=890,700&strip=all [Accessed 21 Jan. 2018]. Fig.2 The table from “Artificial Intelligence: A Modern Approach� (2010), p.2 Fig.3. (2018). [image] Available at: http://aggiemazzucco.com/wp-content/uploads/2017/07/workplace-stressfree-ottawa300x200.jpg [Accessed 21 Jan. 2018]. Fig.4 Screenshot of the survey (2018) Fig.5 (2018). [image] Available at: http://www.telegraph.co.uk/content/dam/technology/2017/08/11/iPhone-4-whitehandheld_trans_NvBQzQNjv4BqqVzuuqpFlyLIwiB6NTmJwfSVWeZ_vEN7c6bHu2jJnT8.png [Accessed 19 Jan. 2018]. Fig.6, (2018). [image] Available at: https://static.boredpanda.com/blog/wp-content/uploads/2016/03/dog-food-comparison-bagelmuffin-lookalike-teenybiscuit-karen-zack-fb__700-png.jpg [Accessed 21 Jan. 2018]. Fig.7 (2018). [image] Available at: https://edited.com/static/img/seo/EDITED-home.jpg [Accessed 21 Jan. 2018]. Fig. 8 (2018). [image] Available at: https://www.ald.softbankrobotics.com/sites/aldebaran/files/styles/638x314/public/pepperb2b.png?itok=QlwC9HK2 [Accessed 21 Jan. 2018]. Fig.9 (2018). [image] Available at: http://static2.businessinsider.com/image/562963b9bd86ef1d5d8b8f29/amazon-is-now-using-awhole-lot-more-of-the-robots-from-the-company-it-bought-for-775-million.jpg [Accessed 21 Jan. 2018]. Fig.10 (2018). [image] Available at: https://www.droneflit.com/wp-content/uploads/2016/07/Amazon-Prime-Air.jpg [Accessed 22 Jan. 2018]
Appendix 1 Typeform Survey Dec 2017-Jan 2018
Survey Results
Survey Results For more detailed version, please, visit: https://docs.google.com/spreadsheets/d/17fmK2qz5J1KjCDnYVVx8XGKQR7JD4nI9dbViEwulwy8/edit?usp=sharing
Appendix 2 Online Interviews
Participant 1
Participant 2
Appendix 3 Face to face Interviews Sample: 5 students from UK, Singapore and Russia
Participant 1
Participant 2
Participant 3
Participant 4
Participant 5
Appendix 4
Appendix 4
Strategic & Creative Solutions