The application of AI on customer experience in sustainable fashion

The application of AI on customer experience in sustainable fashion Sustainability in fashion buying Mingzhu Wang

1. Aim & objectives & methodology 2. Introduction of AI

CONTENTS

3. Application of AI in fashion 4. AI & Consumer experience 5. Sustainable and ethical practices 6. Risks and Conclusion 7. Appendix-Customer survey 8. References

AIM

In the light of the development of AI in the fashion industry, brands adapt and include this new technology in their buying cycle. The aim of this research is to evaluate how the development of AI in fashion industry can help in the improvement of customer experience by offering better merchandise choices and sustainable strategies alongside with exploring the store of the future .

Objectives

1.

Explore the implementation of AI in fashion industry, and find out its influence on the fashion buying cycle.

2.

Identify how AI has been implemented through customer experience services online and offline

3.

Critically analyse the challenge and risks of AI in general and propose ways to mitigate risks.

4.

Suggest a feasible business plan for fashion companies to imply AI sustainable purposes

5.

Analyse the practices of sustainable strategies in fashion industry alongside with identifying the impacts of AI on fashion retail and analysing its ethics and sustainability.

Primary research -

Customer survey with 44 responds aged between 20-35 to find out the acceptance of AI for shopping experience and

Methodology

personal data practice.

-

Observation on Stitch Fix made from 11th -18th November.

Secondary research -

Academic Journals from Sciencedirect

-

Report from Consultancy Companies: McKinsey, EY, Google

-

Literature

-

Reliable online resources from Drapers, WGSN, the Guardian, Forbes, Fashion Retail

2.Introduction ● What is AI ● The benefits of AI

INTRODUCTION

What is AI Computer systems that can learn for themselves Carry out tasks that were once the preserve of humans Innovation in the development of algorithms

3 types of AI models :

●

Deep learning

●

Machine learning

●

Neural networks (Venugopal, 2018)

(Arthur Zaczkiewicz et al., 2018)

Artificial intelligence (AI) is a broad category of software that uses algorithms to learn from experiences and interact with its environment (Shawn Ennis, 2019).

(SearchEnterpriseAI, n.d.)

INTRODUCTION

The benefits of AI To Business The Capgemini study estimates AI could offer as much as $340bn (ÂŁ262bn) in cost-saving opportunities for retail companies.-- through prediction (Drapers, Van Bos, B. 2018).

(McKinsey Global Institute analysis ,2018)

To Consumers

66%

62%

58%

More relevant

customized marketing Better

Profitability

Revenue growth

Higher customer conversion rates

merchandise selection Improved

(OXFORD ECONOMICS, 2018)

products and services

3.Applications of AI in fashion ● AI apply in fashion buying cycle ● AI applications in fashion

AI apply in fashion buying cycle Shaper decision making for fashion buyers Trend Forecasting

Customer Services

AI

Stock Management

(Clark, 2015)

Merchandising Pricing

Manufacture prediction: size, number

“AI is just a tool. It’s data we’re utilising to be sharper in our decision making” Arti Zeighami, head of advanced analytics and AI at H&M Group (Drapers, Van Bos, B. 2018)

AI application in fashion

01 Image recognition ● ● ● ●

Image to Image Text to image Speech recognition Acts as a personal stylist

03 Chat robots ● ● ● ●

02 Speech recognition ● ● ●

Voice user interface Speech to text Automatic speech recognition.

Gives personalized shopping experience.

Bot – bot interaction Context based decision making Live Chat

04 ● ●

Trend analysis

Scan social media and the web Analyse data of colour, size, style and pattern of clothes.

1. AI Image recognition Amazon Echo Looks

Mode.ai, Google search

● ● ●

Visual search engines Artificial Neural Networks (ANN) Image to image descriptions

LVMH, L’oreal, Neiman Marcus, Luxotica, Estee Lauder uses Memomi software technology.

● ● ● ● ●

Virtual assistance Machine learning algorithms Fashion specialist.

(Luce, 2019)

●

Smart Mirror - full stack of technology Hardware with depth sensing Software equipped with advanced computer vision algorithms.

2. AI speech recognition Uses Natural language processing and Neural network technology.

Voice command to Image to Virtually integrated with the brand’s catalogue.

High street brand H&M , Zara launch this innovation.

(Luce 2019; Kapfunde, 2018; G.I.A, 2018)

3. AI ChatBots -- Mode.ai AI fashion chatbot with picture match Mode.ai-Natural language processing

● ● ●

Interpret human natural language Relevant response Inputs that are not exactly predefined.

Louis Vuitton launched a chatbot on their Facebook page.

● ●

●

Acts like a Virtual stylist Intuitive, visual-centric aipowered bot for conversational shopping Fashion shopping-optimized NLP capabilities (Luce, 2019; Arthur, 2017; Appenics, n.d)

4. AI Fashion trend forecasting--Heuritech & Tencent Youtu Lab Help create more merchandise choice Its ability to scan social media and the web, and analyse data such as the colour, size, style and pattern of clothes.

Heuritech Uses AI To Predict Fashion Trends From Millions Of Images:

Christian Dior and Louis Vuitton use Heuritech AIpowered mood boards for product development and merchandise planning.

(Su,2018, Heuritech.)

Youtu Lab Perfectly capable to identify the preference of a certain age group:

Chinese designer Chi Zhang (Graduate in IM ) use the tech to design his AW18 (Van Bos, 2018) collection.

4. AI & consumer experiences ● Survey Survey based on AI and in-store service ● Online shopping experience ● Offline smart stores

Why consumer experience is important to the business ?

“89% of companies compete primarily on the basis of customer experience” Hyken, S, Forbes, 2018

(EY-Shifting-from-consumer-to-experience, 2014)

● ● ● ● ●

Builds trust and relationships between the brand and customers Stands out from competition Boosts customer advocacy Creates Loyalty Improves customer satisfaction

Own research of personalized experience acceptance based on AI 44 response from 20-35 age group

75%

(Oxford Economics, 2018)

75% Capture customers’ data 57% Predict customer behaviour 53% Personalized customer service

customer Like personalized shopping experience

39%

customer Very interested in AI based shopping experience

32 % above average

AI Online shopping experience AI chatbot

Intelligent stylist Personalised products recommendation based on previous purchase

AI Styling Assistant

Developed an algorithm for personal styling service.

AI Measurement

Identify body size Recommend the best size

Online: AI Styling Assistant Based on combinations of consumers’ favorite colors, patterns, and textiles to help the customer to match their need.

How it works?

(Mckinsey, 2018)

(Stitch Fix,, 2019)

(Stitch Fix, 2019)

According to a study by McKinsey & Co., curation services with 55 % of total subscriptions, are so far the most popular, suggesting a strong desire for personalized services. Vue.ai(2018).

Online: AI Measurement Gives consumers the confidence to purchase the things they fall in love with on your site.

Top

Second

( Invesp, Khalid Saleh)

(Shopify Plus, 2019)

True Fit: More than 30 Retail Clients, including:

( True Fit, 2019)

(Time, ND)

AI offline shopping experience “Around 90 % of purchase still occur inside the walls of a physical retail store”

--Shoplifter, Embrey Medina, A

Over 50% of people researched would be more loyal to stores that offered a “meaningful in-

store experience”.

-- Drapers Guides (2019)

AI Smart stores: offline

AI : computing technology Analyse customer preference & Previous purchase data Personalised recommendations

Beacons Send personalised promotions and products details to shopper's phones. iSign Solutions,2019

Facial recognition Identify customer Track the customers' habits

Geofencing GPS,RFID In-store App Catch potential customers within a radius (Marian, WGSN, 2017)

AI powered Visual Search & Argos model AI platform FindMine Adidas store

(Berthiaume, 2019)

● ● ● ● ●

Core: training a generator Trained collocation discriminator Attribute discriminator (Liu et al., 2019)

● ● ●

AI Visual Search Personal recommendations based on previous purchase Click and Collect Speed and Convenience Delivery the Same day for orders placed before 6pm

In-store app In-store retail apps merging of the online and physical shopping experiences.

AI driven fashion app

(Van Bos, 2018)

(Store.marketline.com, 2018)

● ●

Shops are geofenced Help people navigate to products they are most likely to purchase.

● ● ●

(EY, 2014)

Combine the shopping experience with online Use same account when shopping in store Offer personalized recommendations based on previous shopping behaviour online

5. Sustainable and ethical practices ● How can applications of AI impact on sustainability ● Circular fashion ● Slow fashion ● Fashion as a service

Applications of AI in fashion Trend forecasting Customization

Impacts on Sustainability ● -

No overproduction PLANET, PEOPLE, PROFIT

●

Better match

between customers and products

Personal styling Image recognition

-

PLANET, PEOPLE, PROFIT ●

Chatbot Voice recognition

More efficient communication

Increase revenue - PROFIT (metropolitanintelligence, 2018)

Less return - PLANET & PEOPLE

Circular Fashion Designs the waste out of the system ●

AI can accelerate the transition to a circular economy

●

Improve design, operating business models

●

Optimise infrastructure and processes (Google , 2019)

Slow Fashion What is slow fashion? ● Not just the opposite of fast fashion ● Complete break from growth-based fashion. ● Allows for different forms of production and retail.

(Walker & Giard, 2013)

Fashion as a Service

Rent The Runway

9 million members, 5 stores in US Company’s total valuation to $1 billion Machine learning Create highly personalized interactions

(Segura, 2019)

6. Risks and conclusion â—? AI risks & risks management â—? Conclusion

AI challenge - Observation of Stitch Fix Tester: Ying-Chen, Lai Order date: 11.11.2019 Receive date: 18.11.2019 Charge:ÂŁ10 styling fee (Free deliver) Overall, the experience is not good.

Improvement 1.Value (Quality x Price point) 2.Style 3.Seasonal

AI challenge to individual

1. Retailers build trust with their consumers by offering better merchandise choice

Data Privacy Social Manipulation

2. Sharing a little more data brings a better user and retail experience (Draper,2019)

Job threats Safety

3. Secure anti-fraud system(Cordoba, 2014).

Unreliability

4. Ensure the efficacy of in- house cybersecurity measures and indemnity clauses

(Marr, 2018; Cheatham, et al, 2019)

85 % Losing large amounts of personal information

57 % AI machines become more intelligent than human

Recommendations:

63 %

Own research from 44 responds

AI machines being unreliable (Pages.arm.com, 2019)

AI challenge to business

Recommendations: ●

Recruit more talents in data Science and decision makers (Google, big data London)

●

Invest in effective data management strategies (WGSN,Poile, 2019)

●

Organizations should be more

ORGANIZATION More data is required Regular data cleaning is required Lacks human qualities High cost Models misbehaving

transparent to the public.

(Marr, 2018; Cheatham, et al, 2019)

(OXFORD ECONOMICS, 2018)

AI challenge to society

Recommendations: ●

Implement more data management regulations such as GDPR and CCPA

●

Technology firms should be forced to share their

SOCIETY National security Infrastructure integrity

data with the rest of society, so as not to impede

Political stability

social progress(Guardian,Morozov, 2018)

Economic stability (Marr, 2018; Cheatham, et al, 2019)

●

Incorporating human participation into a complex socio-technical system for safe and equitable development of AI.

Conclusion AI-Bring effectiveness into the fashion

buying cycle with more merchandise choice through better decision making AI-Enhance personalised customer service AI- Smart retail in the future emerge online and offline shopping experience AI- Realise sustainability more efficiently

(Bhardwaj, BusinessInsider, 2018)

Appendix- customer survey

Appendix- customer survey

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