A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF Free Download

Name: A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF
Author: William Jones

1 / 43

0 views•43 pages

A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF Free Download

A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF free Download. Think more deeply and widely.

A study of AI-powered tools in en-

hancing customer engagement within

the fashion industry

HOANG SON NGUYEN

BACHELOR’S THESIS

April 2025

BUSINESS ADMINISTRATION

International Business

ABSTRACT

Tampereen ammattikorkeakoulu

Tampere University of Applied Sciences

International Business

AUTHOR: HOANG SON NGUYEN

A study of AI-powered tools in enhancing customer engagement within the fash-

ion industry

Bachelor's thesis 43 pages, appendices 1 page

April 2025

A Study of AI-Powered Tools in Enhancing Customer Engagement Within

the Fashion Industry

The purpose of this thesis was to explore how AI-powered tools contribute to

customer engagement strategies in the fashion industry. The study aimed to

identify the benefits and challenges associated with AI-driven solutions while

addressing ethical considerations such as data privacy and customer trust.

The research utilized benchmarking analysis to compare AI applications in two

leading fashion companies, Zara and Marks & Spencer (M&S). Key evaluation

criteria included financial performance, supply chain efficiency, personalization

strategies, and ethical practices. The analysis was based on secondary data

sources such as annual reports, academic literature, and market studies.

The results illustrated that AI significantly enhances customer engagement by

enabling personalized recommendations, real-time interaction, and efficient in-

ventory management. Zara demonstrated advanced AI usage in predictive ana-

lytics and virtual fitting rooms, improving operational agility and engagement.

M&S prioritized ethical AI implementation, focusing on transparency, customer

trust, and compliance with data protection regulations.

The findings suggest that integrating technological innovation with ethical con-

siderations is the key to sustainable competitive advantage. This study intro-

duces the concept of “Ethical Technological Symbiosis” (ETS), emphasizing the

importance of balancing innovation with responsibility to maintain long-term cus-

tomer loyalty and trust.

Keywords: artificial intelligence, customer engagement, fashion industry, digital

marketing, ethical data use

CONTENTS

GLOSSARY or ABBREVIATIONS AND TERMS ....................................... 4

1 INTRODUCTION .................................................................................. 5

Thesis Background ........................................................................ 5

Thesis Objectives ........................................................................... 5

Intended Methods & Limitations ..................................................... 7

2 THEORETICAL FRAMEWORK ............................................................ 8

DIGITAL MARKETING ................................................................... 8

2.1.1 History of Digital Marketing ................................................... 8

2.1.2 Digital Marketing ................................................................... 8

2.1.3 Key Elements of Digital Marketing ........................................ 9

DIGITAL MARKETING IN FASHION INDUSTRY ........................ 10

2.2.1 How digital marketing has changed the fashion industry .... 10

2.2.2 How digital marketing has impacted consumer behavior in the

fashion industry .................................................................. 10

CUSTOMER ENGAGEMENT IN THE DIGITAL ERA .................. 13

2.3.1 Involvement and Participation in Customer Engagement ... 14

2.3.2 Emotional, Behavioral Impacts and Brand Visibility Influence

of Customer Engagement .................................................. 15

2.3.3 Economic Impact of Customer Engagement in the Fashion

Industry .............................................................................. 16

AI APPLICATIONS IN CUSTOMER ENGAGEMENT .................. 18

3 BENCHMARKING METHOD .............................................................. 20

Benchmarking .............................................................................. 20

The research process of this thesis .............................................. 21

4 ANALYSIS OF BENCHMARKING METHOD ...................................... 22

Financial Performance ................................................................. 22

Supply Chain and Inventory Management ................................... 23

Customer Engagement ................................................................ 26

Ethical Consideration ................................................................... 29

Summary of the Benchmarking of Zara and Marks & Spencer (M&S)

5. DISCUSSION ..................................................................................... 33

REFERENCES ........................................................................................ 35

APPENDICES .......................................................................................... 43

GLOSSARY or ABBREVIATIONS AND TERMS

TAMK Tampere University of Applied Sciences

AI Artificial Intelligence

MMS Multimedia Messaging Service

SMS Short Message Service

SNS Social Network Service

CE Customer Engagement

AOV Average Order Value

LTV Lifetime Value

AR Augmented Reality

PIMS Profit Impact of Market Strategy

GDPR General Data Protection Regulation

CCPA California Consumer Privacy Act

1 INTRODUCTION

Thesis Background

The topic of this thesis first came into my mind during my internship at a consult-

ing services company, where my major responsibilities focused on building and

maintaining customer engagement across various platforms. It was suggested

that I utilize smartly AI-powered tools to deliver optimal outcomes in order to cre-

ate a solid customer relationship and engagement. However, it's a double-edged

sword; I recognized that the overuse of AI could lead to significant issues, poten-

tially impacting customer engagement, customer relationships and further work-

ing efficiency. Therefore, exploring the use of AI-powered automated tools in en-

hancing customer engagement could be considered important research to control

and monitor the utilization of AI in a smart and effective way.

The thesis’s research will be conducted within the context of the fashion industry,

where AI adoption is increasingly dominant - 79% of fashion retailers plan to use

AI for customer services, and AI-powered chatbots could answer up to 80% of

customer inquiries (Worldmetrics n.d.) and currently, up to 30% of working tasks

in 60% of occupations across all industries could be able to automated by AI

(Luce 2018). The similarity between Fashion and Artificial Intelligence is vague

and clear at the same time. Both of them represent and express human creativity

and intelligence, furthermore, the technological advance of the human race in

general. Fashion has always been forward-looking, grabbing onto new technolo-

gies as they arise. Artificial intelligence is no exception, and it’s moving as quickly

as fashion does (Luce 2018). According to those significant figures, the fashion

industry is going through an era in which AI is dominating the customer relation-

ship and customer engagement, that is the reason why the topic is focusing on

this specific industry, or in other words, “a promised land”.

Thesis Objectives

From this perspective, the objective of this research is to explore the impact of

AI-powered automated engines and technologies in terms of enhancing customer

engagement within the fashion industry, while remaining focused on addressing

ethical considerations such as user data privacy and customer trust. To be more

particular, this thesis targets on:

(1) Exploring the implementations of AI-powered automation engines and

tools in customer engagement strategies, as well as potential benefits and

challenges in operating these tools and engines.

(2) Ascertaining effective practices for the effective application of these tools.

(3) Evaluating thoroughly the ethical implications of AI use, with a focus on

transparency and customer trust.

The thesis and related research are conducted and improved with the expectation

of various positive outcomes. To be more particular, the study is expected to bring

in an in-depth analysis and understanding of AI automated tools’ impact on form-

ing and monitoring customer interaction within the fashion industry.

Secondly, identifying the most effective practices for AI-based automated engine

implementation is outlined in this study, as mentioned above, AI is an effective

tool for addressing problems related to productivity and timesaving; however, the

users are required to carefully implement avoiding compromises in brand authen-

ticity. Considering the benefits and beneficiaries this thesis’s result supports, first

of all, fashion brands, particularly those aiming to optimize their digital marketing

strategies, would find the thesis results as a good reference for their develop-

ment. Not only the units that are operating in Fashion Digital Marketing but also

other individuals in slightly different fields would benefit by gathering insights from

this thesis’s results.

Another key expected outcome of this thesis is to delve into the concern of ethical

issues that are associated with data privacy and consumer trust in the context of

automated marketing. The research is going to explore and point out the im-

portance of navigating the complexities of data usage, also maintaining transpar-

ency and customer trust.

Intended Methods & Limitations

To address the research objectives, this study utilizes a benchmarking analysis

to compare current practices among leading brands. The purpose of this brief

methodological overview is to provide context for the research, with further details

on data collection and analysis to follow in subsequent chapters.

The research scope of this thesis is captured within the fashion industry, cus-

tomer engagement, and the impact of AI-powered tools or engines on these sec-

tors. The insights and suggestions drawn might not reflect the performance and

influence of AI in other industries where customer engagement practices differ

significantly. The fashion industry specializes in emotional connection, strong

brand identity, and personalization, which possibly differ from other industries;

thus, the results, suggestions, and insights generated from this research should

be considered with care. Other sectors and industries probably prioritize special

or different customer engagement strategies, and the potential challenges that

arise from each sector are dissimilar, so applying these findings to other contexts

requires thoughtful decisions.

The benchmarking approach offers a clear perspective on the effectiveness of AI

applications within marketing efforts, particularly regarding customer engage-

ment. However, this method alone might not capture the full scope of customer

engagement outcomes, as findings from benchmarking may not be generalizable

across industries with varying customer engagement strategies and practices.

2 THEORETICAL FRAMEWORK

DIGITAL MARKETING

2.1.1 History of Digital Marketing

The history of digital marketing started with the first message delivered by Ray

Tomlinson in 1971 and Ray’s software allowed people to transmit and receive

data by multiple machines in the provided environment. Afterwards, in 1991, the

appearance and development of the Archie browser highlighted the very first

milestone in the development of Digital Marketing. During this early stage, when

the cloud server was just an idea, the computer memory had been spaced with

tons of data which led to the overload of data processing. As a result, companies

began to search for more optimal methods in which data moves in a digital envi-

ronment, a server rather than a restricted list broker. These databases enable

businesses to monitor customer data more efficiently by improving the interaction

among buyers and sellers. (Puthussery 2020, 5)

The term Digital Marketing appeared initially in the 1990s in conjunction with the

explosion of desktop computers, and the dawn of server and client computing.

Consumer relationship management (CRM) systems have become a major part

of communications technology (Puthussery 2020, 6). Those things had placed

the very first step in the development journey of Digital Marketing, supported

largely by the introduction of the Web a few years later, the launch of iOS in 2001.

Customer searching preferences have transferred to digital decision-making, ra-

ther than contacting a salesman, thenceforth, until the current era, customers in

everyday life are heavily dependent on digital systems (Puthussery 2020, 6).

2.1.2 Digital Marketing

Digital Marketing is marketing as well as promotion of goods or services by utiliz-

ing digital technologies, especially through the Web, cell phones, visual advertis-

ing as well as any other electronic media. Digital marketing platforms are Internet-

based and offline systems which can build, promote, and distribute brand quality

across digital channels to an end user (Puthussery 2020, 4). According to Hanlon

(2022, 3), Digital marketing is satisfying customers’ needs and wants using digital

means in other terms, digital marketing ‘can be defined as using any digital tech-

nology to facilitate the marketing process, with the end goal of customer interac-

tion, engagement and measurement’ (Zahay 2021,125). Digital Marketing refers

to the implementation of marketing in which technologies and advanced tools are

utilized in a controlled way in order to deliver the optimal experience to the end

users or consumers. However, the application of digital marketing also includes

other platforms that operate offline such as SMS or MMS, visual media. This ex-

pansion of offline platforms supports distinguishing digital marketing towards

online marketing.

2.1.3 Key Elements of Digital Marketing

Firstly, one important element when considering digital marketing is its accessi-

bility. The main goal of digital marketing is to reach consumers and encourage

them to connect with the product via technology distribution (Puthussery 2020,

7). During customer experience in digital marketing, the desired data is exposed

through electronic communications. That supports significantly the data ex-

change in the effort of promoting the services or products. Precisely, this provides

a multi-communication channel whereby data can be easily exchanged by any-

one anywhere in the globe, irrespective of who they are (Puthussery 2020, 7).

Besides, digital marketing eliminates cultural differences in terms of perceiving

the information, due to the advantages of contactless communication or lack of

common knowledge to a small community (Puthussery 2020, 7).

Competitive edge is the second element of digital marketing in which businesses

utilize Web technologies, and social networks as a strategic means to establish

data streamlining as well as acquire the highest potential of online marketing.

Good use of online marketing can contribute comparatively to a low cost com-

pared with traditional marketing methods such as reducing external service costs,

operating expenses, promotion costs, processing fees, software development

costs and control costs (Puthussery 2020, 8).

Digital Marketing has been proven to offer a more efficient method in terms of

Brand Recognition, especially in countries that are strong in insecurity resistance,

also instability minimization (Puthussery 2020, 8). Using this form of advertising

smartly and in a controlled way has shown great opportunities to increase product

or service awareness and minimize ambiguity. Indeed, it is possible that some

people with strong confusion and aversion would especially appreciate the higher

degree of social media engagement with a humanoid brand (Puthussery 2020,

8).

DIGITAL MARKETING IN FASHION INDUSTRY

2.2.1 How digital marketing has changed the fashion industry

Digital Marketing has significantly transformed traditional marketing in the fashion

industry by offering direct and real-time interaction with customers through plat-

forms such as social media, e-commerce or emails whilst traditional marketing

focuses mainly on physical stores and print media. In order to reach a wider au-

dience, enhance brand visibility and facilitate personalized communication, a rev-

olution of marketing strategies in the fashion industry is required, those traditional

marketing methods have shifted from time to time to online strategies with the

involvement of influencers, data-driven ads targeting and interactive content.

According to Rathnayaka (2018, 1), Fashion is an industry which has a very short

product life cycle and totally depends on changing trends. From that point of view,

marketers should be aware of the newest and upcoming fashion trends by ana-

lysing and if possible, forecasting the data before the customers switch to other

competitors. Marketers with a sense of trends and analytic minds will become

trendsetters and digital channels become the best platform to acquire and convert

customers (Rathnayaka 2018, 1).

2.2.2 How digital marketing has impacted consumer behavior in the fash-

ion industry

Consumer Behaviour is such a sophisticated concept or in other words, phenom-

enon and getting a clear view of its facilitated factors, particularly in the digital

marketing environment has been prioritized among contemporary research pa-

pers (Kochhar 2020, 72). Kochhar (2020) also stated that the major reason for

social media’s developing scene is based on the fact that social media not only

shifts the way consumer communicate with others but also their behaviour in

searching, evaluating, selecting and purchasing goods or services. Digital Mar-

keting has altered significantly consumer behaviour in the context of the fashion

industry by moving the purchasing process to digital platforms, leveraging social

media, and enhancing personalization. Precisely, the role of social media in facil-

itating consumer engagement, where platforms such as Instagram and Facebook

act as virtual storefronts, provides visual inspiration and direct purchasing oppor-

tunities. In the current stage of the digital era, the customer is hardly influenced

by electronic word-of-mouth (e-WOM) and recommendations or verification from

influencers rather than traditional advertising (Bandara 2021). Moreover, mar-

keting trends are heavily influenced by personalized digital marketing strategies,

highlighted by a transformation from mass marketing to individual engagement

including advertising and tailored email campaigns; these strategies showcase

effectiveness in fostering stronger brand loyalty and driving high conversion rates

(Rathnayka 2018). The digital transformation has reshaped the expectations

around convenience, immediacy, and the availability of fashion goods; further-

more, it enhances the accessibility of customers to global fashion brands, em-

phasizing the importance of digital marketing in the modern market.

In the discussion of digital factors that shift and modify consumer behaviour in

everyday life, Kochhar (2021) suggests that there are three major categories of

influencing factors that account for the motivation of customers. First and fore-

most, which is personal factors, Park et al. (2011) emphasized the impact of user

lifestyle on the use of social networking services (SNS) as well as its impact on

high-end brand loyalty. The result said that users’ lifestyle is deeply influenced by

the way they perceive different SNS characteristics, which affect their level of

brand loyalty. Consumer’s social media activities also are impacted by their per-

sonality traits such as agreeableness, extraversion, and openness to experience.

Thus, digital marketing of the brands might need to segment the consumers

based on their personal characteristics (Salem & Alanadoly 2020). Since person-

ality traits could be approached and seeded by utilizing different digital marketing

channels, fashion brands are developing targeted social media activities that

serve the needs of consumers' decisions, lifestyles and traits. Secondly, another

major that has a huge impact on consumer behaviour is the psychological factors,

in which social media and digital channels play an important role. Nelson, D.,

Moore, M. and Swanson, K., (2019) discussed that social media platform moni-

tors the variousness of millennial fashion consumers in terms of buying motiva-

tions. Digital channels provide a wide range of motivation for young people to

search or to decide which brands would suit them best. For example, Instagram

and Pinterest are utilized to inspire their fashion sense or seek fashion advice,

while Twitter or Instagram act as a connecting channel for brands and celebrities

to spread their fashion tastes (Nelson et al. 2019). In the context of luxury fash-

ion, the association between beliefs and attitudes is affected profoundly by digital

advertising (Chu, S., Kamal, S. and Kim, Y., 2013). The empirical findings illus-

trate that millennials’ beliefs about social media ads (product information, fal-

sity/no sense, and value corruption) influence their attitudes towards digital mar-

keting, which eventually affect their behavioural responses (Kochhar 2021).

The rise of digital marketing has established a solid foundation for another brand-

new concept of influencer marketing; precisely, the rise of social media platforms

such as Instagram, and TikTok has created new avenues for brands to reach and

engage with their target audience (Jain 2023). Influencer marketing in the digital

era provides trust and authenticity which impact directly consumers' view of the

brand. Influencers are mostly seen as trustworthy sources of information and rec-

ommendations by their followers (Jain 2023); therefore, fashion brands are

equipped with the capacity to explore the hidden mechanisms to build trust and

authenticity through genuine relationships with influencers, in which their market-

ing efforts are reduced largely, but their authenticity and trust are gained signifi-

cantly (Jain 2023 ).

Digital Marketing has reshaped the fashion industry by influencing consumer be-

haviour via SNS (Social Networking Services), influencer collaborations, and per-

sonalized campaigns. From that perspective, digital marketing has brought more

opportunities to expand global reach, enhance brand visibility, and foster innova-

tion. The rise of digital platforms provides advantages for brands to connect with

wider audiences, building credibility and driving consumer trust. Advanced tech-

nologies offer a streamlined customer experience, turning digital marketing into

a cornerstone of modern fashion strategies.

CUSTOMER ENGAGEMENT IN THE DIGITAL ERA

First of all, customer engagement could be defined as the emotional, behavioural

and cognitive investment that consumers make in their interactions with a brand

or company (Brodie, Hollebeck, Jurić & Ilić 2011). According to Van Doorn, J.

Lemon, K. N., Mittal, V. et al (2010), customer engagement is a behavioural man-

ifestation towards a brand or firm, beyond transactional activities, driven by cus-

tomer satisfaction, brand loyalty and perceived value. The interactions between

consumers and brands also include participation in social online platforms, shar-

ing content and brand advocacy. Customer engagement participates deeply in

associating with the brand and creating brand value, in other words, customer

engagement builds an impression of importance to the brand (Brodie et al. 2011).

From the view of practitioners and organizations, CE (Customer Engagement)

acts as activities to facilitate “repeated interactions that strengthen the emotional,

psychological or physical investment a customer has in a brand” (Sedly 2010, 7).

In the field of information systems, Customer Engagement (CE) could be de-

scribed as the extent of customer involvement with both representatives of an

organization and other customers in a collaborative knowledge exchange pro-

cess (Wagner & Majchrzak 2007,20).

Customer engagement is possibly grouped based on the active level of their ac-

tivities towards the brands: consumption (least active), contribution (moderately

active), and creation (most active) (Tan & Goh & Zainal 2024, 172). In the context

of luxury fashion brands, the first level of customer engagement (passive con-

sumption) represents a minimum level of engagement in which basic activities

are included such as viewing brands’ posts and following the brand’s social me-

dia. In the next level of engagement, interaction with the brands on multiple plat-

forms is involved by activities that represent their attractiveness towards the

brands. Creating value, indirectly or directly contributes to the value chain of the

brands such as: reviewing products, and publishing content related to the brands

are the obvious signs of the third level (creation level), this engagement goes

beyond receiving information, establishing a substantial engagement (Tan et. al

2024, 173).

2.3.1 Involvement and Participation in Customer Engagement

According to Vivek, Beatty and Morgan (2012, 131), their research delved pro-

foundly into the managerial perspective of CE, resulting in effective suggestions

for producing successful customer engagement. It is suggested that investing

value in communicating with the customer is one of the essential criteria; further-

more, engagement tends to be more successful when the accustomed initiatives

are more related, requiring the client’s understanding of the brand. Moreover, in

the managerial perspective of CE, the existing customer is not only the potential

outcome but also the potential or future customer. From that point of view, the

brands are recommended to research and observe what kind of behaviours and

emotional responses they are seeking from the customer in order to well convert

those to value added to the bottom line (Vivek et al. 2012, 131).

In the research, Vivek also mentioned of theoretical framework of CE with an

illustration of the relation between CE and other marketing constructs. The re-

search stated that there are several important elements of CE that incorporate

the customer’s feelings which are affective and cognitive, while the other two el-

ements including the social element and behavioural element, account for cap-

turing the participation by current and future customers (Vivek et al. 2012, 133).

Customer engagement is strongly established through company-generated ac-

tivities and offers that uniquely build the experience-based relationship of the cus-

tomers (Vivek et al. 2012, 133). Regarding the CE concept, customer participa-

tion is necessary to discuss since it refers to the degree to levels the customer is

involved in producing or delivering the service (Dabholkar 1990). Precisely, cus-

tomer participation forms an interactive series of activities that contain the com-

mon interest of the firm as well as the customer. Besides, involvement is another

constructive element to CE; involvement acts as a contributor to raising cus-

tomer’s state of mind (Smith and Godbey 1991) in terms of cognition, affection,

and motivation (Vivek et al. 2012, 134) or perceived personal relevance, but not

acts as a behaviour itself (Celsi and Olson 1988). There is a point showing that

involvement is one of the important concepts of CE in which its heightened level

of interest and caring creates an absolute forerunner to engagement (Vivek et al.

2012, 134). For instance, in the cosmetic industry, Sephora and Clinique have

provided consulting sessions for customers or anyone who walks in based in the

form of free beauty workshops (Vivek et al. 2012, 134). The attributes supplied to

CE by involvement are remarkable, including producing greater external search

(Beatty & Smith 1987), greater depth of processing (Burnkrant & Sawyer 1983),

more elaboration (Petty & Cacioppo 1986), and pushing demands in product trials

(Krugman 1965; Robertson 1976).

2.3.2 Emotional, Behavioral Impacts and Brand Visibility Influence of

Customer Engagement

Recent research has shown that positive brand performance is contributed enor-

mously by enhancing customer engagement, in which customers are allowed to

invest time, energy or effort in interacting with the brand through social media

platforms (Marjerison et al. 2019; Marjerison & Gan 2020; Sawhney et al. 2005).

The impact of customer engagement has been shown in connecting brands with

customers in terms of building relationships and supporting brands with a better

prediction of future markets as well as changes in the current market (Lin et al.

2019). According to Bolton et al. (2018), involving customers in a spectrum or

series of activities would accelerate and facilitate the customer engagement pro-

cess, with the participation of experiential marketing provided by marketers.

Customer Engagement has become fundamental to success in the fashion in-

dustry, especially when digital transformation is exploding in various fields includ-

ing the fashion industry. Customer Engagement in the digital era enables brands

to build strong emotional connections, strengthen customer loyalty and foremost,

drive innovation in the industry. Customer Engagement creates an environment

where the core is customer-centric value, customer interaction during this stage

is transformed into journeys of mutual value, aligning products and services with

individual preferences (Ling, Kiong & Ahmad 2024). Researches show that per-

sonalized engagement strategies, such as tailored recommendations and inter-

active campaigns, significantly increase customer satisfaction and retention rates

in the fashion industry (Chaffey & Smith 2022). The rapid development of tech-

nologies in a digital world has reshaped the mechanism of customer engagement,

allowing brands to interact directly with customers in real-time and shape their

offerings to fit with customer’s personal preferences. There are examples of so-

cial media that utilize virtual storefronts to provide inspiration and opportunities

for customers to engage with brands through interaction with brands’ online con-

tent such as Pinterest or Instagram (Dwivedi et al. 2021). Brands and customers

interact in ways that go beyond simple product showcasing - these communica-

tions reveal customers’ desires or demands, supporting shaping smarter market-

ing strategies. In the context of the fashion industry, collaborating with influencers

and featuring content from customers in real-time prove especially potent, given

that purchasing behaviours in this sector are fundamentally shaped by visual el-

ements and social endorsement (Rathore et al. 2020).

Furthermore, customer engagement broadens the brands’ ecosystem, fostering

trust, loyalty and emotional connection, which are crucial in a highly competitive

market. Engaged customers are more likely to act as brand advocates, spreading

positive word-of-mouth and contributing to the brand’s social capital. This dy-

namic is specifically evident in the digital age, where customer reviews, social

media content and interaction have changed the public’s perception (Hollebeek,

Glynn & Brodie 2014). Chaffey and Smith (2022) further stated that the ability to

engage customers effectively creates a virtuous cycle that repeating purchases,

strengthened loyalty and enhancing brand equity are obtained by increased sat-

isfaction. Personalization is a key element in enhancing customer engagement.

Customized recommendations and offers, and tailored campaigns trigger the

customer feelings of their value, thereby the emotional connection with the brands

is strengthened (Hollebeek et al. 2014)

2.3.3 Economic Impact of Customer Engagement in the Fashion Industry

Aside from its marketing impact and branding influence, customer engagement

acts as a critical driver of economic performance in the fashion industry, directly

impacting profitability, revenue growth, and cost efficiency. Engaged customers

are more presumably to repeat their purchases, increasing their lifetime value

(LTV) and reducing the dependence on costly customer acquisition strategies

(Hollebeek et al. 2014). As an illustration, Chaffy & Smith’s research (2022) indi-

cated that retaining an existing customer costs five times less than acquiring a

new one, turning engagement strategies essential for enhancing profitability. In

such a competitive industry where trends change rapidly, retaining customers

through effective campaigns and strategies with efficiently saving inputs ensures

a stable revenue base, even during periods of market instability.

Moreover, customer engagement is capable of increasing the average order

value (AOV). As mentioned above, personalization in customer engagement pro-

duces various valuable advantages to the brands as well as their economic con-

cerns. Personalization strategies encourage the customer to purchase additional

items based on their preferences and purchase habits or purchase history. Pro-

nounced examples could be seen in the luxury sector in the fashion industry,

where brands that engage customers with exclusive offers and expertly selected

collections have shown a higher number of sales per transaction compared to

non-engage customers (Dwivedi et al. 2021). Furthermore, engaged customers

have the tendency to purchase more frequently, ensuring a consistent revenue

stream for the brands. Customer engagement also reduces churn rates, which is

extremely valuable in the fashion industry, where customers have numerous al-

ternatives. Engaged customers are less likely to switch to competitors, as their

loyalty and trust are built towards the brand. Therefore, the brand could be able

to minimize lost revenue as well as solidify the brand’s market position. According

to Rathore, Ilavarasan & Dwivedi (2020), the study pointed out that brands with

high engagement metrics on social media platforms encountered 23% lower

churn rates compared to brands with minimal engagement efforts.

Additionally, customer engagement generates indirect economic benefits through

advocacy and organic marketing. Engaged customers play an important role in

minimising marketing expenses through sharing their experience on various plat-

forms. From a broader perspective, customer engagement contributes largely to

the sustainability targets of fashion brands by facilitating the process of custom-

ers responding to the market more effectively. Engaged customer support brands

adjust, improve and refine their offerings and optimize inventory management

through valuable feedback, thus reducing waste and overproduction. Customer

feedback would create a spectrum that enhances operational efficiency, aligning

with the increasing customer demand for sustainable practices within the fashion

industry (Chaffey & Smith 2022). As the fashion industry has the potential to

evolve, prioritizing engagement strategies is the key to remaining a critical deter-

minant of financial success and competitive advantage.

AI APPLICATIONS IN CUSTOMER ENGAGEMENT

Artificial Intelligence (AI) has emerged in recent years in every aspect of daily

routine in human society, especially in the apparel industry. AI has been applied

to enable personalized experiences, predictive analysis, interactive tools, and

sustainable practices. The power of AI underlies its algorithm which could ana-

lyse vast amounts of customer data, such as browsing behaviour, purchase habit

patterns, and preferences; from those valuable data, the brands are able to de-

liver tailored, customized recommendations aligning with personal tastes. As an

example, Stich Fix, an online personal styling service in the United States, has

utilized the hybrid approach by combining AI algorithms with human stylists to

create personalized wardrobes based on customer input, fostering loyalty

through individualized attention (Siddhu & Mohibi 2024).

Furthermore, the digital shopping experience has been revolutionized by virtual

fitting rooms and AR (Augmented Reality), allowing customers to preview clothes,

Zara has applied this technology effectively, with AR features that transform cus-

tomer personal devices into virtual mirrors. These innovations have notably

boosted buyer confidence while reducing largely the return rates (Goti,

Querejeta-Lomas, Almeida, Puerta & López-de-Ipiña 2023). Particularly, predic-

tive analytics support brands in terms of forecasting fashion trends turning into a

competitive advantage and managing inventory levels. By researching historical

data and social media trends, brands are able to predict customers’ desires be-

forehand their demands upon or forecast the upcoming trends to prepare adap-

tive strategies (Song & Bonanni 2024).

Automated systems among the brands have evolved along with other sectors. AI

chatbots are in demand due to their rapid response capability and individualiza-

tion for specific customer assistance. These AI-supported tools are built to serve

the demand 24/7, making them invaluable for handling customer inquiries, offer-

ing personalized recommendations, and suggesting users through the purchas-

ing process. A giant in apparel – H&M has utilized chatbots to assist customers

in real-time whilst gathering their preference data during the assisting process

(Mishra 2024). These responsive interactions in a short time ensure a seamless

shopping experience, and establish a solid foundation of mutual trust; eventually

strengthening the connection between customers and the brand. Another remark-

able application of Artificial Intelligence in the apparel industry is its ability to gen-

erate predictive analysis which is absolutely important in understanding customer

perceptions and preferences. The ability to provide real-time insights into cus-

tomer attitudes towards products and brands is turning predictive analysis into a

powerful technique for companies. These insights offer various valuable infor-

mation regarding identifying upcoming trends, responding to emerging issues

and adjusting brands’ offerings to suit the consumers’ demands. Mohammadi &

Kalhor (2021) suggested that brands could be able to remain aligned with cus-

tomers’ expectations as well as identify popular items, addressing dissatisfaction.

Despite of environmentally controversial impact of Artificial Intelligence, sustain-

ability practices in recent years have contributed by AI practically, aligning with

environmentally conscious customers. The significant contribution of AI in im-

proving sustainable practices occurs in the optimization of supply chains as well

as the reduction of production waste and general waste in the apparel industry.

AI supports the brands to target multiple goals at once, achieving sustainable

goals and enhancing customer loyalty. Precisely, predictive analysis - an AI ap-

plication, supports brands in forecasting the appropriate amount of demands in

the market, curtailing overproduction, and lessening unsold inventory that would

otherwise involve environmental degradation. In addition to these advantages, AI

offers a transparency environment including sourcing and manufacturing, allow-

ing brands to disclose ethical practices that associate firmly with customer’s

value. Alwyn and Richard (2024) argued that sustainable fashion is supported

remarkably by AI tools through optimizing material usage and cutting down car-

bon footprint linked with production and logistics. This connection between AI and

sustainability not only strengthens customer engagement but also allocates the

brands as frontiers in ethical and environmentally responsible practices. Relying

on the capability of segmenting audiences and delivering campaigns with desired

targets, AI ensures marketing messages strike the right chord with each cus-

tomer. Nike exemplifies this approach through their mobile apps, in which cus-

tomer’s data is effectively utilized to tailor exclusive offers, customer’s wishes

contents and suggestions guided by purchase history and shopping habits.

These personal touches have sparked higher app usage, lifted sales volume and

concreted brand allegiance (Manoharan, Ashtikar, Nivedha & Dutta 2024).

3 BENCHMARKING METHOD

Benchmarking

Benchmarking is applied by systematically collecting and analysing publicly avail-

able performance data from industry leaders. The utilization of the benchmarking

method focuses on comparative metrics such as conversion rates, customer sat-

isfaction, and operation efficiency to identify best practices and actionable rec-

ommendations. The benchmarking method is selected in this study based on its

ability to provide a structured way to evaluate and compare customer engage-

ment across leading fashion companies. This method particularly benefits this

study by systematically identifying best practices and performance gaps, offering

actionable insights for brands to develop effective implementation of advanced

marketing tools. By analysing the practices of industry leaders including H&M and

Zara, this study captured the process of leveraging strategies and tools to achieve

objectives such as personalization, operational efficiency, and improved engage-

ment metrics. These companies are selected based on their reputation for effec-

tively implementing AI-automated tools to enhance customer satisfaction and loy-

alty, which matches the core mission of this thesis: understanding the impact of

AI in the successful execution of marketing strategies.

The benchmarking analysis evaluates firms across four key dimensions: financial

performance, supply chain and inventory management, customer engagement,

and ethical considerations, aligning closely with the detailed analysis that follows

in this thesis. Financial performance is assessed to understand how effectively

AI-driven tools impact company profitability, revenue growth, and overall effi-

ciency. Metrics such as Return on Investment (ROI), Return on Sales (ROS),

gross profit margins, and the specific contributions of predictive analytics and dy-

namic pricing models are analysed. Supply chain and inventory management are

reviewed to explore how AI technologies enhance operational responsiveness,

inventory accuracy, and cost efficiency. This includes evaluating inventory turno-

ver rates, lead-time effectiveness, accuracy in stock management, and the re-

duction of waste and overproduction achieved through predictive analytics and

real-time data. Customer engagement is a central focus of the analysis, examin-

ing the effectiveness of AI-powered personalization strategies including recom-

mendation engines, virtual fitting rooms, augmented reality (AR) solutions, and

voice commerce. Indicators such as customer retention rates, satisfaction levels,

conversion rates, and brand loyalty improvements are crucial elements in this

evaluation. Ethical considerations examine how companies manage critical as-

pects such as customer trust, data privacy, transparency in AI-driven decisions,

and compliance with privacy regulations including GDPR and CCPA. The analy-

sis emphasizes each company’s approach to maintaining transparency and eth-

ical practices in handling customer data and AI-driven marketing activities. This

benchmarking analysis relies heavily on secondary data sources, including finan-

cial and annual reports, academic research, and credible market analyses.

Through a structured comparison of best practices, this study aims to identify

practical and actionable insights for fashion brands looking to enhance customer

engagement through responsible and effective use of AI technologies.

The research process of this thesis

The research process of this thesis was designed to evaluate AI-driven customer

engagement tools and strategies in the fashion industry through a comprehensive

analysis of benchmarking method. The selection of benchmarking companies fo-

cuses on identifying companies with well-documented AI applications. Therefore,

Zara and M&S (Marks & Spencer) were chosen, regarding the selection of Zara,

the company is famous for its industry-leading JIT (Just-In-Time) inventory sys-

tem, predictive analysis and AI-powered engines, which enable flexibility when

the market changes and provide efficient in stock management (Meritshot, n.d.).

On the other hand, M&S was put into consideration due to its strengths in moni-

toring AI-driven personalization recommendations, chatbots, and virtual assis-

tants, which has been proven lately with a 20% increase in online sales (The

Guardian, 2024). Those two firms are suitable puzzles for the picture of under-

standing AI-powered engines in the fashion industry with a comprehensive view

of AI-powered operational efficiency (Zara) and AI-driven customer personaliza-

tion, turning them into lucrative sources for comparative benchmarking. The data

collection was conducted in 2 periods. The benchmarking analysis took place

from January to February 2025, using publicly available sources, case studies,

information and corporate reports to assess Zara and M&S by various key per-

formance metrics.

4 ANALYSIS OF BENCHMARKING METHOD

This benchmarking analysis compares Zara and Marks & Spencer (M&S), two

major fashion retailers with distinct business models and operational strategies.

Zara, owned by Inditex, is known for its agile supply chain and trend-driven fast

fashion approach, whereas M&S focuses on quality, sustainability, and a strong

digital transformation strategy. Evaluating both companies’ financial perfor-

mance, supply chain efficiency, customer engagement strategies, technological

adoption, and sustainability initiatives, this analysis identifies key competitive ad-

vantages and areas for improvement.

Financial Performance

Financial performance is a fundamental indicator of business success, allowing

firms to identify their position in the market and profitability. The PIMS (Profit Im-

pact of Market Strategy) study, a comprehensive empirical analysis of numerous

businesses, provides strong evidence that financial metrics, such as Return On

Investment (ROI) and Return on Sales (ROS), are associated closely with strate-

gic market decisions (Buzzell & Gale 1987). Particularly, Relative Market Share

is a strong predictor of profitability, with firms holding a higher market share

achieving better financial performance (Buzzell, Gale & Sultan, 1975). Addition-

ally, product quality has been found to have a positive correlation with profitability,

improving the idea that financial success reflects a firm’s competitive strength

(Gale 1994). These findings showcase that financial performance is not only a

measure of business success but also an essential tool for evaluating marketing

positioning and strategic decision-making (Buzzell & Gale 1987).

According to Inditex Group’s Annual Report (2023), Zara and Zara Home had a

10,3% increase in net sales (Figure 1), reaching 26 billion euros in 2023, with

Zara fashion being its dominant brand. With regards to the mother company, In-

ditex maintains a high gross profit margin of 57.8% (Figure 2), reflecting its effi-

cient cost management and high product turnover. Zara’s revenue growth is

driven by its ability to rapidly introduce new collections and respond to shifting

consumer trends. AI-automated tools, such as predictive analytics and real-time

demand forecasting, enable Zara to cut down superabundant products and opti-

mize pricing strategies, leading to higher margins and profitability (Inditex Annual

Report 2023). On the contrary, M&S’s latest financial report emphasizes a 9.3%

increase in total revenue, reaching 13 billion pounds in 2024 (Figure 3). While

profitability has improved, M&S operates at a lower gross margin of around 40%,

mainly due to higher sourcing costs and a slower stock turnover compared to

Zara. Nonetheless, M&S’s investment in digital transformation and AI-driven dy-

namic pricing models has supported increased sales conversions and improved

customer targeting in e-commerce channels (M&S Annual Report 2024).

From those gathered figures and data from both firms, it is shown that AI-auto-

mated tools have contributed to elaborate Zara’s efficient pricing and inventory

turnovers, meanwhile, M&S has adopted the integration of AI in order to enhance

digital marketing and pricing optimization to improve profitability.

Supply Chain and Inventory Management

In the context of this research, which delves deeply into the impact of AI-auto-

mated engines in enhancing customer engagement within the fashion industry,

the supply chain is a crucial factor. To verify this point, supply chain efficiency is

a significant factor that contributes to enhancing customer engagement, espe-

cially in the fashion industry, where customer expectations for product availability

and delivery speed are high. AI-driven supply chain automation supports brands

to minimize stockouts, reduce delivery times, and personalize the shopping ex-

perience, all of those criteria are significant factors that control the level of cus-

tomer satisfaction (Rathore 2019, 1). Predictive analytics and real-time demand

forecasting allow companies such as Zara and M&S to optimize inventory, ensur-

ing that customers are able to search for desired products in stock while minimiz-

ing overproduction and waste (Arora, Anant, Chaudhary 2023). Furthermore, AI-

powered logistics and warehouse automation facilitate order fulfilment, enabling

faster deliveries creating rapid deliveries and enhancing omnichannel shopping

experiences (Sodiya, Umoga, Amoo, Atadoga 2024).

The efficiency of supply chain and inventory management is crucial in determin-

ing a firm’s ability to respond to market trends, maintain cost efficiency, and en-

sure customer satisfaction. Both Zara and M&S deploy unique approaches to

monitor their own supply chains, influenced by the firm’s business models and

strategic goals. Zara’s vertically integrated model allows for high responsiveness,

reducing lead times and ensuring rapid inventory turnover. Meanwhile, M&S em-

ploys a more traditional structure of supply chain, operated by collaborating with

third-party suppliers. This method focuses on quality control and long-term sup-

plier relationships, despite its limitation on providing agility and challenges in flex-

ibility. As mentioned previously, both companies have adopted progressively AI-

driven technologies to enhance their logistics, improve inventory accuracy, and

reduce waste. One of Zara’s most significant competitive advantages is its agile

Just-In-Time (JIT) inventory model, enabling rapid adaptation to consumer de-

mand.

Based on empirical research, Zara maintains a vertically integrated supply chain,

producing approximately 50% of its merchandise in-house. This strategy enables

rapid design-to-retail cycles, allowing Zara to introduce new collections within just

two to four weeks compared to the industry average of several months. This effi-

ciency is obtained through close coordination between design, production, and

logistics teams, allowing the company to react quickly to emerging trends in the

market and consumer demand (Kumar 2024). To optimize inventory manage-

ment and minimize waste, Zara utilizes advanced predictive analytics that ana-

lyzes real-time sales data, social media trends, and market demand indicators.

These systems use machine learning algorithms to assess which products are

selling in high demand and predict future needs, assuring that popular items re-

main stocked while low-demand merchants are rapidly phased out. This data-

driven approach benefits Zara’s reduction in overproduction by approximately 15

to 20% annually, significantly lowering markdown rates and improving profit mar-

gins (Schoenmaker 2024). Additionally, SCM Globe reports that Zara sells 85%

of its items at full price, compared to the industry average of 60%, and has only

10% unsold inventory annually, meanwhile, the industry inventory average fluc-

tuates around 17% and 20%. Besides, the investment of Zara in robotics and

warehouse automation has a heavy impact on streamlining its logistics opera-

tions. Zara also accelerated the movement of goods through automated picking

and sorting systems in Zara’s distribution centers, which reduces order pro-

cessing times by up to 40% compared to traditional warehouse management.

These automated warehouses are significantly effective in high-density storage

environments, utilizing AI-powered conveyor belts and robotic arms to facilitate

faster restocking and order fulfilment. Zara’s seamless omnichannel experience,

supported by AI-powered logistics and personalized recommendations, improves

brand loyalty and encourages repeat purchases. These elements collectively po-

sition Zara as a leader and a pioneer in customer-centric retailers, where respon-

siveness and innovation drive higher engagement and satisfaction.

As regards M&S, Mark & Spencer (M&S) has slightly different supply chain oper-

ations, the company operates a global supply chain, sourcing products from over

1,000 supplier factories across more than 55 countries. According to the figures

in September 2024, the distribution of clothing and home supplier factories in-

cludes 205 in China, 88 in India, and 72 in Bangladesh. This results in longer

production and replenishment cycles, typically six to eight weeks, turning M&S

into a less agile firm in terms of responding to rapid fashion shifts. Therefore, after

recognizing its limitations, M&S has deeply invested in logistics and warehouse

automation. In 2023, the company allocated £36.8 million to supply chain mod-

ernization, marking a 28.6% increase from the previous year. A major component

of this strategy was the acquisition of logistics firm Gist, which enhanced M&S’s

ability to streamline distribution and improve inventory accuracy (McLoughlin

2023). Differing from Zara’s operation which is a fast production model, M&S has

integrated a Supplier Code of Conduct that focuses on ethical labor practices,

environmental responsibility, and fair trade policies. The company works closely

with SEDEX (Supplier Ethical Data Exchange) to audit factories and ensure com-

pliance with global labor standards. These measures contribute to brand trust

and long-term sustainability goals, which are a priority for M&S consumers (FRC

2023).

From those analyses, the impact on customer engagement is affected signifi-

cantly by the strategies of utilizing AI to fulfil the companies’ needs. While Zara’s

vertically integrated model prioritizes fast fashion and frequent new arrivals, M&S

focuses on high-quality, ethically sourced fashion with a strong omnichannel

presence. Despite the less reactiveness to fashion trends, M&S’s model compen-

sates through superior product quality and sustainability initiatives. Getting along

with the quality of the product, M&S has implemented warehouse automation and

AI-powered inventory tracking which reduces inefficiency and improves product

availability, leading to better customer experience.

Customer Engagement

Customer Engagement is a crucial part of this benchmarking due to its im-

portance in showcasing the main idea of AI influence in the context of customer

engagement across the fashion industry. First and foremost, customer engage-

ment is a critical factor in retail success, impacting sales performance, brand loy-

alty, and customer lifetime value. Both companies Zara and M&S have adopted

AI-influenced strategies offering the customers personalized experiences, opti-

mizing communication channels, and integrating AI solutions across multiple cus-

tomer touchpoints.

Zara has employed their unique approach to AI-driven customer engagement

which focuses on speed, responsiveness, and seamless omnichannel interac-

tions. The brand integrates AI-powered solutions across multiple customer touch-

points, enhancing both digital and in-store experiences. Zara’s strategy is deeply

rooted in rapid response to changing fashion trends and real-time customer feed-

back, enabled by advanced digital solutions.

One of the most impactful AI-driven is its personalized recommendation engine,

which enhances the shopping experience by analyzing customer browsing pat-

terns, purchase history, and behavioral data. Differing from traditional recommen-

dation systems that rely on static algorithms, Zara’s AI solution is applied through

deep learning models to dynamically adjust homepage layouts, product sugges-

tions, and promotional content based on real-time user interactions. Research

suggests that AI-driven recommendation engines are able to increase conversion

rates by 10-30% due to their ability to deliver hyper-personalized experiences

(Goti et al. 2023). Furthermore, the enhancement of this application is showcased

effectively in the integration with Zara’s mobile app and e-commerce platforms,

where AI customizes product suggestions based on weather conditions, location-

based fashion trends, and social media influence. This means Zara has deep-

ened engagement, boosted higher purchase frequency and increased customer

engagement through hyper-personalization. Eventually, Zara has carefully en-

sured that the gap between physical and digital retail is filled and bridged, turning

the AI-powered recommendations into intuitive, relevant ones and more im-

portant aligned with customer’s real-time fashion needs.

Significantly, there is one of the emerging AI-powered technologies revolutioniz-

ing customer engagement in retail is the integration of AI-driven virtual assistants

and voice commerce systems which are utilized effectively by Zara. Voice com-

merce has become an essential part of its e-commerce ecosystem, allowing cus-

tomers to search for products, receive styling recommendations, and even place

orders using AI-driven voice recognition tools. Zara has integrated voice-based

AI assistants within its mobile platforms and website. This would actually support

the customer significantly in terms of navigating product categories, checking

availability, and tracking orders hands-free. According to Mnyakin (2020), and

Zumstein & Kotowski (2020), AI-driven voice commerce could significantly im-

prove the conversion rate by reducing friction in the purchase journey and en-

hancing customer experience. Studies analysing global e-commerce platforms

have suggested that retailers implementing voice-enabled shopping interfaces

experience a remarkable increase in transaction completion rates, especially in

mobile commerce, where voice search simplifies product discovery and check-

out. Even though the improvements vary across industries and fields, AI-powered

voice commerce leads to measurable gains in online engagement and purchase

frequency.

In recent years, the integration of Augmented Reality (AR) virtual fitting rooms

has shifted the fashion retail industry to a more customer-centric focus by en-

hancing purchase confidence, reducing return rates, and increasing customer en-

gagement. Zara has embraced Augmented Reality technology to enhance both

its in-store and online shopping experiences. The company introduces virtual fit-

ting rooms and AR-powered mirrors in select flagship stores, allowing customers

to preview clothing digitally without trying it on physically. These features have

helped improve customer engagement by evolving the shopping experience to

be more interactive and providing a clearer observation of garments when they

are worn (Reddy 2024).

Additionally, Zara has also integrated virtual try-on options into its mobile plat-

forms. The integration grants the customers the selection to scan their products

by using their personal devices and imagine how the different clothing items

would look on them before purchase. Studies suggested that this feature has

been well-received, as it supports deeply the customer to be more confident in

purchase decisions and reduces the uncertainty associated with online shopping

(Anderson 2025). This application also solves the major challenge in fashion re-

tail which is high return rates. High return rate results from inaccurate sizing or

unmet expectations regarding fabric and fit. Virtual fitting rooms address it per-

fectly by providing a realistic representation of garments, eliminating the fear of

purchase decisions and the dissatisfaction of post-purchase (Lee & Xu & Porter-

field 2021). In physical stores, Interactive AR mirrors add another layer of en-

gagement by allowing customers to experiment with different styles digitally.

These features not only make shopping more convenient but also contribute to a

more enjoyable and personalized experience. Shoppers using AR mirrors report

greater confidence in their selections, which in turn leads to improved customer

satisfaction and a stronger connection with the brand (Sayed, 2019).

In addition to the utilization of AR in Zara’s ecosystem, Marks & Spencer has also

incorporated Augmented Reality (AR) technology into its retail strategy to im-

prove both in-store and online shopping experiences. The customer now is able

to visualize clothing before purchasing with a more convenient and engaging

shopping process through M&S’s implementation of virtual fitting solutions and

interactive displays. These innovations go well with M&S’s broader efforts to mod-

ernize the firm’s retail operations and improve customer satisfaction. As men-

tioned above, recent studies have proven that AR-based virtual fitting rooms sup-

port largely customer’s decision making turning it into a more enjoyable experi-

ence (Alexander & Kent 2021). M&S has also implemented AI-powered chatbots

and virtual assistants to provide real-time customer support. These AI-driven

tools actually made a huge impact in reducing response times and improving ser-

vice efficiency by addressing common inquiries, offering product suggestions,

and assisting with order tracking. Verifying this point, a study conducted by Blü-

mel & Jha in 2023 has shown that retailers using AI chatbots see higher customer

retention rates, as automated responses ensure faster and more consistent in-

teractions.

Beyond customer-facing AI, predictive analysis contributes effectively to the sys-

tem of M&S, giving M&S the ability to forecast demand and manage inventory

efficiently. M&S’s AI-powered analysis ensures the stock level is optimized as

well as controls the availability of the product when it comes to the customer’s

demand. This minimizes stock shortages and overstock situations, maintaining a

smooth shopping experience for customers and improving supply chain efficiency

(Cao 2021). Looking ahead, M&S intends to invest in AI-powered solutions to

further enhance customer interactions. The company is exploring the use of con-

versational AI for more sophisticated virtual shopping assistants, AI-driven mar-

keting strategies for targeted campaigns, and automated checkout experiences

to improve convenience. With the rising AI in this modern life, as well as the

evolvement of technology-supporting engines across various industries, retailers

that adopt intelligence automation and personalized engagement tools would

gain an absolute competitive advantage, offering customers a more seamless

and satisfying shopping experience (Wilson, Johnson & Brown 2024).

Ethical Consideration

Ethical data privacy is essential for consumer trust, regulatory compliance, and

responsible business practices in retail. As companies’ thirst for customer data

for personalized shopping and operations, ensuring transparent and fair data use

is critical. Mishandling data potentially leads to customer distrust, reputation dam-

age, and legal penalties, particularly under laws such as GDPR and CCPA. Stud-

ies stated that brands which prioritize ethical data governance and customer con-

sent establish stronger loyalty and higher consumer confidence (Alom, Zakaria,

Rahmat 2024). The risk of legal consequences and loss of consumer trust ap-

pears to firms that fail to protect customer data. Another research has highlighted

that customers tend to engage with brands that ensure secure and transparent

data handling, avoiding problems related to data breaches and unfair profiling

(Selvarajan 2021). Additionally, ethical data management not only is a legal re-

quirement but also a competitive advantage, guiding the firm to improve brand

reputation, customer relationships and long-term success (Arjoon & Rambocas

2011). Marks & Spencer (M&S) and Zara both operate in the retail sector, but

each firm has their unique approaches to data privacy and ethical considerations

that differ significantly. M&S has positioned itself as a pioneer in responsible data

management, ensuring that customer information is handled with transparency

and strict adherence to privacy regulations such as GDPR. M&S prioritizes re-

ducing data breaches and unauthorized tracking by employing data minimization

techniques and anonymization. It is suggested that firms could be able to foster

higher consumer trust and compliance with evolving legal standards since they

focus on implementing strong data governance frameworks (Perry & Wood

2019). Marks & Spencer’s ethical approach to data handling and consumer pro-

tection has been a core part of its corporate responsibility strategy, emphasizing

transparency in AI-driven personalization and digital marketing (Ahmad 2012).

On the contrary, Zara employs a more data-driven business model, heavily rely-

ing on customer analytics to optimize supply chain management and personalize

marketing efforts. However, reports indicate that Zara has faced greater scrutiny

regarding data privacy concerns, particularly in automated customer profiling and

targeted promotions. Unlike M&S, which provides clear opt-in choices for data

usage, Zara’s AI-driven customer engagement tools often default to more aggres-

sive tracking settings, raising ethical concerns about the extent of data collection

and customer consent (Matic & Vabal 2015). A study by Perry, Fernie, and Wood

(2014) found that fast-fashion retailers like Zara prioritize data analytics to drive

purchasing decisions, but often lack clear frameworks for consumer data trans-

parency and informed consent (Perry et al. 2014). Another major difference be-

tween M&S and Zara in terms of corporate responsibility and ethical AI imple-

mentation. M&S integrated AI in an ethical way that allows users to have greater

control over their personalized shopping experiences by ethically ensuring algo-

rithmic transparency as well as avoiding bias in product recommendations. This

statement is aligned closely with the abovementioned research that mentioned

that transparency in AI decision-making enhances consumer confidence and

long-term engagement (Perry et al. 2014). On the other hand, Zara prefers to

utilize real-time data analytics to drive sales and optimize pricing strategies but

has been less transparent about how customer data informs these automated

decisions. While a data-driven approach allows for more efficient inventory con-

trol and dynamic pricing, it also increases ethical concerns around data collection

and algorithmic fairness (Perry & Wood 2019). Both companies recognize the

importance of ethical data use and compliance, but M&S has taken a more pro-

active approach by implementing customer-centric privacy policies, while Zara

focuses on data-driven efficiency at the potential cost of customer transparency.

As privacy regulations evolve, retailers that integrate ethical data practices and

transparent AI governance will maintain a competitive edge while safeguarding

consumer trust.

Summary of the Benchmarking of Zara and Marks & Spencer (M&S)

In summary, the benchmarking analysis compared Zara and Marks & Spencer

(M&S) on key dimensions related to the thesis’s objectives which is evaluating

AI-powered automation’s impact on customer engagement, effective practices,

and ethical implications within the fashion industry. The findings from Zara and

M&S have showcased the effective use of AI in enhancing customer engagement

whilst pointing out areas that require attention, particularly in transparency and

ethical data processing. The key findings as abovementioned include Zara's abil-

ity to facilitate an agile, data-driven supply chain, superior operational efficiency,

and high customer engagement through innovative AI-driven personalization and

AR technologies. On the other hand, M&S prioritizes sustainability, quality and

customer trust through transparent AI applications, clear ethical practices, and

strong GDPR compliance, while effectively leveraging AI for improved customer

experiences.

Figure 3. Comparison of both companies based on benchmarking dimen-

sions

5. DISCUSSION

In conclusion, this thesis provides a comprehensive exploration of how technolo-

gies, specifically automation tools, are changing customer engagement within the

fashion industry. The research highlights a noticeable transformation from tradi-

tional marketing techniques towards more customized and interactive customer

experiences. By analysing thoroughly and carefully benchmarking leading fash-

ion brands Zara and Marks & Spencer (M&S), this research contributes largely to

attempt to provide meaningful insights into how digital innovation significantly im-

proves customer interaction, satisfaction, and loyalty.

Regarding the research’s result related to Zara, the company effectively demon-

strates how modern digital tools, including predictive analysis, augmented reality

(AR), and voice-activated shopping, enhance the customer’s shopping experi-

ence. These technologies support Zara in creating highly personalized interac-

tions, and quick responses to fashion trends which have rapidly changing pat-

terns, also notably improve their operational efficiency. By strategically using con-

sumer data to understand and forecast customer preferences, Zara maintains a

customer-centric approach that strengthens emotional connections and solidifies

brand loyalty.

Marks & Spencer (M&S), on the other hand, employs a complementary yet dis-

tinct strategy that integrates modern digital tools within a strong framework fo-

cused on data ethical utilization and sustainability. By using digital customer ser-

vice engines and personalized support systems, M&S significantly enhances the

consumer experience, fostering transparency, trust, and lasting customer rela-

tionships. The company's strong commitment to ethical standards, strict compli-

ance with privacy regulations such as GDPR and CCPA, and dedicated commit-

ment to sustainable sourcing position M&S as a responsible leader within the

competitive fashion industry.

From these performance analyses and insights, this thesis recommends the con-

cept of “Ethical Technological Symbiosis” (ETS). This concept evolved directly

from the research by studying how top fashion brands successfully combine tech-

nological innovation with ethical responsibility. During the research, including the

study of Zara and Marks & Spencer (M&S), there is a clear pattern showing that

effective customer engagement increasingly requires not only innovative person-

alization and operational effectiveness but also transparency, responsible prac-

tices, and consumer trust. Therefore, ETS suggests that long-term competitive

advantage and meaningful customer relationships rely on carefully balancing

technological innovation with strong ethical standards.

This new theoretical perspective acts as a practical guide or a considerable point

for implementing effective digital strategies and contributes to broader academic

discussions about technology management and ethical marketing practices.

Fashion brands adopting the ETS could be capable of excelling in maintaining

customer engagement, sustainable growth, and industry leadership in today’s

digital and ethical-aware marketplace.

REFERENCES

Ahmad, H. 2012. Responsiveness and Collaboration in the Fashion Supply

Chain. The degree of MPhil in Responsiveness and Collaboration in the Fash-

ion Supply Chain. The University of Manchester. Read on: 22.12.2024.

https://pure.manchester.ac.uk/ws/portalfiles/portal/54527323/FULL_TEXT.PDF

Alexander, B., & Kent, A. 2021. Tracking technology diffusion in-store: a fashion

retail perspective. International Journal of Retail & Distribution Management,

ahead-of-print(ahead-of-print). Read on: 2.2.2025

https://doi.org/10.1108/ijrdm-05-2020-0191

Ali, Y. 2024. Emotional Branding in the Fashion Industry: Enhancing Customer

Engagement. Mohammad Ali Jinnah University. Academia. Read on: 24.1.2025

https://www.academia.edu/124688511/Emotional_Branding_in_the_Fashion_In-

dustry_Enhancing_Customer_Engagement

Alom, N. B., Zakaria, A. F., Rahmat, S. B., Nurhuda, S., & Razak, A. B. 2024.

An Examination of Customer Behavior Analytics, Privacy Issues, and Data Pro-

tection Laws in the Age of Big Data and Machine Learning. ResearchGate.

https://www.researchgate.net/publication/386507641_An_Examination_of_Cus-

tomer_Behavior_Analytics_Privacy_Issues_and_Data_Protec-

tion_Laws_in_the_Age_of_Big_Data_and_Machine_Learning

Anderson, J. 2025. Innovative Visual Merchandising Strategies in the Digital

Era. ResearchGate.

https://www.researchgate.net/publication/389272534_Innovative_Visual_Mer-

chandising_Strategies_in_the_Digital_Era

Arjoon, S., & Rambocas, M. 2011. Ethics and Customer Loyalty: Some Insights

into Online Retailing Services. Ethics and Customer Loyalty: Some Insights into

Online Retailing Services, 2.

https://www.researchgate.net/publication/228534237_Ethics_and_Cus-

tomer_Loyalty_Some_Insights_into_Online_Retailing_Services

Arora, N., Anant, B. C., & Chaudhary, K. 2023. Optimizing Supply Chains in the

Fashion & Textile Industry through AI and the Fashion Industry. ResearchGate.

unknown.

https://www.researchgate.net/publication/381109666_Optimizing_Sup-

ply_Chains_in_the_Fashion_Textile_Industry_through_AI_AI_and_the_Fash-

ion_Industry

Rajvanshi. A. 2024. How AI Could Transform Fast Fashion for the Better—and

Worse. TIME.

https://time.com/7022660/shein-ai-fast-fashion/

Blümel, J., & Jha, G. 2023. Designing a Conversational AI Agent: Framework

Combining Customer Experience Management, Personalization, and AI in Ser-

vice Techniques. Hawaii International Conference on System Sciences, 2023

(HICSS-56).

https://aisel.aisnet.org/hicss-56/da/service_science/2/

Boudet, J., Gregg, B., & Vollhardt, K. 2019. The future of personalization--and

how to get ready for it | McKinsey. Www.mckinsey.com.

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-in-

sights/the-future-of-personalization-and-how-to-get-ready-for-it

Brodie, R. J., Hollebeek, L. D., Jurić, B., & Ilić, A. 2011. Customer Engagement:

Conceptual Domain, Fundamental Propositions, and Implications for Research.

Journal of Service Research, 14(3), 252–271.

https://doi.org/10.1177/1094670511411703

Butler, S. 2024. M&S using AI as personal style guru in effort to boost online

sales. The Guardian. Read on: 21.10.2024.

https://www.theguardian.com/business/article/2024/sep/05/m-and-s-using-ai-to-

advise-shoppers-body-shape-style-preferences

Buzzell, R. D., & Gale, B. T. 1987. The PIMS principles: linking strategy to per-

formance. New York: Free Press.

Camp, R. C. 2006. Benchmarking: The search for industry best practices that

lead to superior performance. New York: Productivity Press.

Cao, L. 2021. Artificial Intelligence in retail: Applications and Value Creation

Logics. International Journal of Retail & Distribution Management 49(7), 958–

976.

Chaffey, D. 2023. How do you compare? 2019 email marketing statistics compi-

lation. Smart Insights.

https://www.smartinsights.com/email-marketing/email-communications-strat-

egy/statistics-sources-for-email-marketing/

Chaffey, D., & Smith, P. 2022. Digital Marketing Excellence. Digital Marketing

Excellence Planning, Optimizing and Integrating Online Marketing. London:

Routledge

Chowdhury, S. N., Faruque, M. O., Sharmin, S., Talukder, T., Mahmud, A.,

Dastagir, G., & Akter, S. 2024. The Impact of Social Media Marketing on Con-

sumer behavior: A Study of the Fashion Retail Industry. Open Journal of Busi-

ness and Management 12(03), 1666–1699.

https://doi.org/10.4236/ojbm.2024.123090

Marks & Spencer. 2023. DATA PROTECTION AND PRIVACY POLICY.

https://corporate.marksandspencer.com/sites/marksandspencer/files/2023-

02/Data%20Protection%20and%20Privacy%20Policy.pdf

Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user ac-

ceptance of information technology. Journal of Risk and Uncertainty 18(3), 321–

325. JSTOR.

https://doi.org/10.1023/a:1011156710779

Day, F. J. 2021. Zara Does Data Privacy Better than The Tech Giants. Medium.

https://faitheday.medium.com/zara-does-data-privacy-better-than-the-tech-gi-

ants-68a566d85c55

DigitalDefynd. 2024. 5 Ways Zara is Using AI. DigitalDefynd.

https://digitaldefynd.com/IQ/ways-zara-using-ai/

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., & Carlson, J. 2021. Setting the fu-

ture of digital and social media marketing research: Perspectives and research

propositions. International Journal of Information Management 59(1), 1–37.

Hoang, D., Kousi, S., & Martinez, L. F. 2023. Online customer engagement in

the post-pandemic scenario: a hybrid thematic analysis of the luxury fashion in-

dustry. Electronic Commerce Research, 23(3), 1401–1428.

https://doi.org/10.1007/s10660-022-09635-8

Fashion Revolution. 2019. Fashion Transparency Index. Modasosteniblebcn.

https://www.modasosteniblebcn.org/wp-content/uploads/2020/05/Fashion-

Transparency-Index-2019.pdf

Gale, B. T. 1994. Managing customer value: creating quality and service that

customers can see. New York: Free Press.

Galletta, A. 2013. Mastering the semi-structured Interview and beyond: from

Research Design to Analysis and Publication. New York: New York University

Press.

IntersoftConsulting. 2018. General Data Protection Regulation (GDPR).

https://gdpr-info.eu

Goti, A., Querejeta-Lomas, L., Almeida, A., de la Puerta, J. G., & López-de-

Ipiña, D. 2023. Artificial Intelligence in Business-to-Customer Fashion Retail: A

Literature Review. Mathematics, 11(13), 2943.

https://doi.org/10.3390/math11132943

Haas, G., & Morschett, D. 2023. Personalisation in Retail Marketing: Effects and

Examples.

https://www.unifr.ch/intman/en/assets/public/intman/files/news/2023-07-

13_HaasGuillaume_Personalisation_in_Retail_Marketing_(Master%20The-

sis%20Uni%20Fribourg).pdf

Harreis, H., Koullias, T., Roberts, R., & Te, K. 2023. Generative AI in Fashion |

McKinsey. McKinsey; McKinsey & Company.

https://www.mckinsey.com/industries/retail/our-insights/generative-ai-unlocking-

the-future-of-fashion

Hennink, M., Hutter, I., & Bailey, A. 2020. Qualitative Research Methods. 2nd

edition. London: Sage Publications.

Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. 2014. Consumer Brand Engage-

ment in Social Media: Conceptualization, Scale Development and Validation.

Journal of Interactive Marketing, 28(2), 149–165.

Iacovcich, S. 2024. M&S expands AI data-driven capabilities across clothing

and home. Retail Systems.

https://retail-systems.com/rs/Ms_expands_ai_data_driven_capabili-

ties_across_clothing_and_home.php

Ivanov, D., Tsipoulanidis, A., & Schönberger, J. 2018. Operations and Supply

Chain Strategy. Springer Texts in Business and Economics, 81–110.

https://doi.org/10.1007/978-3-319-94313-8_4

Iyanuoluwa, D., Ndubuisi, L., Franca, O., Owolabi, R., Sunday, T., & Adura, R.

2024. AI-DRIVEN PREDICTIVE ANALYTICS IN RETAIL: A REVIEW OF

EMERGING TRENDS AND CUSTOMER ENGAGEMENT STRATEGIES. Inter-

national Journal of Management & Entrepreneurship Research, 6(2), 307–321.

Jain, T. 2023. The Impact of Influencer Marketing on Consumer Behavior in the

https://www.ijnrd.org/papers/IJNRD2306246.pdf

Jarboe, G., Bailey, M., & Stebbins, M. 2023. Digital Marketing Fundamentals.

John Wiley & Sons.

Kaur, J., Singh, R., & Singh, S. 2021. AI and Customer Experience in the Fash-

ion Industry. ResearchGate; unknown.

https://www.researchgate.net/publication/355203924_AI_and_Customer_Expe-

rience_in_the_Fashion_Industry

Kochhar, N. 2020. Social Media Marketing in the Fashion Industry: A System-

atic Literature Review and Research Agenda.

https://pure.manchester.ac.uk/ws/portalfiles/portal/194687249/FULL_TEXT.PDF

Kotler, P., & Keller, K. L. 2015. Marketing management. 15th edition. London:

Pearson Education.

Kulikowski, K. 2020. The model of evidence-based benchmarking: a more ro-

bust approach to benchmarking. Benchmarking: An International Journal, 28(2),

721–736.

https://doi.org/10.1108/bij-04-2020-0175

Lee, H., Xu, Y., & Porterfield, A. 2020. Consumers’ adoption of AR-based virtual

fitting rooms: From the perspective of theory of interactive media effects. Jour-

nal of Fashion Marketing and Management: An International Journal.

https://doi.org/10.1108/jfmm-05-2019-0092

Lim, W. M., Rasul, T., Kumar, S., & Ala, M. 2021. Past, present, and future of

customer engagement. Journal of Business Research, 140(1), 439–458.

https://www.sciencedirect.com/science/article/pii/S0148296321008213

Lu, S., Marjerison, R., & Seufert, J. 2023. Experiential Marketing, Customer En-

gagement, and Brand Loyalty in the Luxury Fashion Industry: Empirical Evi-

dence from China. Review of Integrative Business and Economics Research,

12(2), 58.

https://sibresearch.org/uploads/3/4/0/9/34097180/riber_12-2_03_k22-052_58-

79.pdf

Luce, L. 2018. Artificial intelligence for fashion: How AI is revolutionizing the

fashion industry. New York: Apress Media.

M&S launches trials of virtual clothing try-on service - DecisionMarketing. (2023,

January 23). DecisionMarketing.

https://www.decisionmarketing.co.uk/news/ms-launches-trials-of-virtual-cloth-

ing-try-on-service

Madsen, D. Ø., Slåtten, K., & Johanson, D. (2017). The emergence and evolu-

tion of benchmarking: a management fashion perspective. Benchmarking: An

International Journal, 24(3), 775–805.

https://doi.org/10.1108/bij-05-2016-0077

Manoharan, G., Ashtikar, S. P., Nivedha, M., & Dutta, P. K. (2024). Tailoring the

Future—The Ascendancy of Artificial Intelligence in the Fashion Industry. 503–

520.

https://doi.org/10.1007/978-3-031-71052-0_20

Marks and Spencer Group plc. (2024). Annual Report & Financial Statements

2024.

Mason, T., & Jarvis, S. (2023). Omnichannel Retail. Kogan Page Publishers.

Meritshot. 2024. Zara Case Study. Meritshot.

https://www.meritshot.com/zara-case-study/

Mnyakin, M. 2020. Investigating the Impacts of AR, AI, and Website Optimiza-

tion on Ecommerce Sales Growth. ResearchBerg Review of Science and Tech-

nology, 3(1), 2020.

https://core.ac.uk/download/pdf/560380663.pdf

Oflazoglu, S. 2017. Qualitative versus Quantitative Research Edited by Sonyel

Oflazoglu.

https://mts.intechopen.com/storage/books/5859/authors_book/authors_book.pdf

O'keefe, J. 2018. Beyond Due Diligence: Enhancing Business Respect of Hu-

man Rights through Engagement with the UN Sustainable Development Goals.

https://repository.gchumanrights.org/server/api/core/bitstreams/a555e470-a8ef-

4181-9027-56adaf274635/content

Ozkara, B. O. 2018. An investigation into the preferences of distance learning

students for constructivist learning. Journal of Higher Education and Science,

8(2), 378.

https://doi.org/10.5961/jhes.2018.279

Pei Ling Tan, Poh Kiong Tee, & Ahmad, R. B. 2024. The Impact of Digital Con-

tent Marketing on Customer Engagement in an Online Fashion Store. Interna-

tional Journal of Advanced Business Studies, 3(Special Issue 1), 107–123.

https://doi.org/10.59857/ijabs.2581

Perry, P., Fernie, J., & Wood, S. 2014. The International Fashion Supply Chain

and Corporate Social Responsibility. ResearchGate.

https://www.researchgate.net/publication/274951956_The_International_Fash-

ion_Supply_Chain_and_Corporate_Social_Responsibility

Purwar, S. 2019. Digital Marketing: An Effective Tool of Fashion Marketing. Pa-

pers.ssrn.com.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3318992

Puthussery, A. 2020. Digital marketing: An overview. India: Notion Press.

Rathnayaka, U. 2018. Role of Digital Marketing in Retail Fashion Industry: A

Synthesis of the Theory and the Practice. Journal of Accounting & Marketing,

07(02). ResearchGate.

https://doi.org/10.4172/2168-9601.1000279

Rathore, B. 2019. Artificial Intelligence in Sustainable Fashion Marketing:

Transforming the Supply Chain Landscape. Eduzone: International Peer Re-

viewed/Refereed Multidisciplinary Journal, 8(2), 25–38.

https://www.eduzonejournal.com/index.php/eiprmj/article/view/363

Rathore, B. 2019. View of Artificial Intelligence in Sustainable Fashion Market-

ing: Transforming the Supply Chain Landscape. Eduzonejournal.com.

https://www.eduzonejournal.com/index.php/eiprmj/article/view/363/316

Renascence. 2024. How Zara Leads in Customer Experience (CX). Renas-

cence.io.

https://www.renascence.io/journal/how-zara-leads-in-customer-experience-cx

Saldaña, J. 2011. Fundamentals of qualitative research. Oxford University

Press.

Selvarajan, G. P. 2021. Harnessing AI-Driven Data Mining for Predictive In-

sights: A Framework for Enhancing Decision-Making in Dynamic Data Environ-

ments. INTERNATIONAL JOURNAL of CREATIVE RESEARCH THOUGHTS,

9(2), 5476.

https://www.researchgate.net/publication/385557912_Harnessing_AI-

Driven_Data_Mining_for_Predictive_Insights_A_Framework_for_Enhanc-

ing_Decision-_Making_in_Dynamic_Data_Environments

Shatz, S., & Chylik, S. E. 2019. The California Consumer Privacy Act of 2018: A

Sea Change in the Protection of California Consumers’ Personal Information.

Business Lawyer, 75, 1917.

https://heinonline.org/HOL/LandingPage?handle=hein.jour-

nals/busl75&div=37&id=&page=

Sheridan, N. 2025. Marks & Spencer Marketing Mix 2025: A Case Study – Lat-

terly.org. Latterly.org.

https://www.latterly.org/marks-spencer-marketing-mix/

Sheridan, N. 2025. Zara Business Model | How Zara Makes Money – Lat-

terly.org. Latterly.org.

https://www.latterly.org/zara-business-model/

Shewaramani, M. 2024. Ensuring Data Security and Compliance in Fashion Re-

tail with PIM. Credencys Solutions Inc.

https://www.credencys.com/blog/ensuring-data-security-and-compliance-in-

fashion-retail/

Siddhu, M., & Mohibi, S. 2024. AI Application and Fashion Industry: A Case

Study of Emerging Economies. Information Systems Engineering and Manage-

ment, 537–552.

https://doi.org/10.1007/978-3-031-71052-0_22

Song, X., & Bonanni, C. 2024. AI-Driven Business Model: How AI-Powered Try-

On Technology Is Refining the Luxury Shopping Experience and Customer Sat-

isfaction. Journal of Theoretical and Applied Electronic Commerce Research,

19(4), 3067–3087.

https://doi.org/10.3390/jtaer19040148

Takyar, A. 2023. AI for fashion brands: Use cases, benefits and future trends in

the fashion landscape. LeewayHertz - Software Development Company.

https://www.leewayhertz.com/ai-use-cases-in-fashion/

Tan, A.-P., Goh, Y.-N., & Zainal, N. N. 2024. Factors Influence Online Custom-

ers’ Purchase Intention on Luxury Fashion through Customer Engagement. In-

ternational Journal of Academic Research in Business and Social Sciences,

14(11).

https://doi.org/10.6007/ijarbss/v14-i11/23466

Van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef,

P. C. 2010. Customer engagement behavior: Theoretical foundations and re-

search directions. Journal of Service Research, 13(3), 253–266.

https://doi.org/10.1177/1094670510375599

Vena Solutions. 2024. Industry Benchmarks of Gross, Net and Operating Profit

Margins - Vena. Venasolutions.com; Vena Solutions.

https://www.venasolutions.com/blog/average-profit-margin-by-industry

Vivek, S. D., Beatty, S. E., & Morgan, R. M. 2012. Customer Engagement: Ex-

ploring Customer Relationships beyond Purchase. Journal of Marketing Theory

and Practice, 20(2), 122–146.

https://doi.org/10.2753/MTP1069-6679200201

WBR Insights. 2024. Zara’s Augmented Reality App Brings Virtual Models to

Life in Stores. Future Stores Los Angeles.

https://futurestores.wbresearch.com/blog/zara-augmented-reality-app-virtual-

model-strategy

Wilson, E. 2018. What is qualitative research? SAGE Publications Ltd EBooks,

1–18.

https://doi.org/10.4135/9781529622737.n1

Wilson, G., Johnson, O., & Brown, W. 2024. Exploring the Integration of Artifi-

cial Intelligence in Retail Operations. Preprints.org.

https://doi.org/10.20944/preprints202408.0012.v1

Zaytsev, A. 2023. Case Study: Zara’s Comprehensive Approach to AI and Sup-

ply Chain Management. AIX.

https://aiexpert.network/case-study-zaras-comprehensive-approach-to-ai-and-

supply-chain-management/

Zaytsev, A. 2023. Case Study: Zara’s Comprehensive Approach to AI and Sup-

ply Chain Management - AIX | AI Expert Network. AIX | AI Expert Network.

https://aiexpert.network/case-study-zaras-comprehensive-approach-to-ai-and-

supply-chain-management/

Zumstein, D., & Kotowski, W. 2020. SUCCESS FACTORS OF E-COMMERCE

– DRIVERS OF THE CONVERSION RATE AND BASKET VALUE. Proceed-

ings of the 18th International Conference on E-Society (ES 2020).

https://doi.org/10.33965/es2020_202005l006

APPENDICES

Figure 1. Zara Net Sales (Inditex’s Annual Report 2023)

Figure 2. Inditex Gross Profit Margin (Inditex’s Annual Report 2023)

0 views·43 pages

A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF Free Download

A study of AI-powered tools in enhancing customer engagement within the fashion industry PDF free Download. Think more deeply and widely.

Uploaded by William Jones on 4/13/2026

/43

100%