This study aims to explore and identify the effective strategies for
enhancing customer engagement through the use of artificial intelligence
(AI) technologies in online marketplaces. A qualitative research approach
was adopted, focusing on semi-structured interviews with managers and
experts in the fields of information technology and online marketing.
Participants were selected based on their direct experience with AI
technologies for customer engagement. The data were analyzed using
content analysis to identify key themes and patterns. Four main themes
were identified: Personalization Strategies, Interactive Strategies,
Optimization Strategies, and Loyalty Strategies. Each theme comprised
various categories and concepts, highlighting the diverse ways AI
technologies can be utilized to improve customer interaction.
Personalization
strategies
emphasized
customized
product
recommendations and behavior-based offers. Interactive strategies focused
on enhancing social media engagement and virtual technologies.
Optimization strategies dealt with streamlining the shopping journey and
site performance. Loyalty strategies underlined the importance of data
driven loyalty programs and post-purchase personalized experiences. Thus,
by leveraging AI for personalization, interaction, optimization, and loyalty,
businesses can significantly enhance the online customer experience,
thereby fostering deeper relationships and increased customer loyalty.
Keywords: Artificial Intelligence, Customer Engagement, Online Markets,
Personalization, Optimization, Loyalty, Interactive Communication.
Introduction
In recent decades, technology, especially artificial intelligence (AI), has had an unprecedented
impact on all aspects of human life (Ameen et al., 2022; Parsakia, 2023; Yildiz Durak, 2023). This impact
is particularly evident in the world of marketing and online commerce, where AI-based technologies are
transforming customer-business relationships (Ai-zhong & Zhang, 2022; Ameen et al., 2022). Among
these, the creation of AI-based touchpoints in the customer journey has emerged as a key area for research
and development (Ai-zhong & Zhang, 2022). These technologies can personalize online shopping
experiences and make interactions more efficient and effective. Additionally, the challenges and
opportunities posed by the COVID-19 crisis for online businesses have drawn more attention to the role
of AI in optimizing marketing strategies and strengthening brand communications with new customers
(Ding et al., 2022). In this period, brand love and repurchase intention have been influenced by the
effective use of smart technologies, highlighting the strategic importance of AI in the current economic
conditions. However, the use of AI is not only about deploying advanced technologies; understanding and
adhering to ethical principles in the use of these technologies is also crucial (Dolganova, 2021). Adhering
to these principles can enhance the customer experience and create a positive image of the business in
customers’ minds. Moreover, precision in digital marketing communications using smart data analysis can
lead to more targeted messaging and higher levels of customer engagement and satisfaction (Li, 2022). In
marketing optimization, the application of AI in precision marketing allows businesses to customize their
offerings to match the unique needs of each customer, thereby creating more meaningful relationships
between brands and consumers (Yang et al., 2021). This not only helps increase customer loyalty but also
acts as a competitive advantage, making businesses stronger in today’s turbulent markets.
Therefore, it can be asserted that in the current digital age, AI technologies play a central role in
shaping customer-centric approaches in online marketing. Steinhoff et al. (2018) emphasize that the
integration of AI technologies with customer-centric approaches has transformed the online marketing
landscape, facilitating personalized, seamless, and efficient interactions (Steinhoff et al., 2018).
Simultaneously, businesses striving to optimize customer experiences are moving towards using AI as a
powerful tool to better understand consumer behavior, preferences, and needs (Ai-zhong & Zhang, 2022).
The use of AI in online markets goes beyond simple automation; it delves into relationship
marketing, where AI facilitates personalized and multichannel experiences that connect with customers
on a deeper level (Steinhoff et al., 2018). By leveraging AI-based technologies, businesses can not only
optimize their operations but also build stronger connections with their customers, thereby fostering
loyalty and trust. Additionally, ethical considerations in implementing AI play a critical role in shaping
customer perceptions and trust towards AI systems (Dolganova, 2021).
As the digital landscape rapidly evolves, the role of AI in marketing decision-making has become
more prominent. By harnessing the power of AI, businesses can gain valuable insights into consumer
behavior, preferences, and trends, enabling them to make informed decisions that engage their target
audiences (Zaman, 2022). Moreover, the rise of AI-based chatbots has transformed customer service
interactions, offering a blend of efficiency and personalization that enhances the overall customer
experience (Chen et al., 2021).
In online shopping, the infusion of AI technologies has played a key role in reducing risks,
improving brand perception, and optimizing marketing strategies (Ding et al., 2022). Using AI for
precision marketing, businesses can tailor their offerings to meet the unique needs of each customer,
thereby creating stronger relationships between brands and consumers (Yang et al., 2021). Additionally,
integrating AI into digital marketing communications has facilitated more accurate and targeted
messaging, leading to higher levels of customer engagement and satisfaction (Li, 2022).
This study examines strategies for enhancing customer engagement using AI technologies in
online markets. Given the increasing competition in the digital space and the rapid changes in customer
behavior, understanding how to use AI to create positive and lasting customer experiences is of utmost
importance. Therefore, this study aims to identify the role of AI technologies in interacting with customers
and their impact on brand-customer relationships.
Methods and Materials
This study employs a qualitative research method aimed at identifying strategies for improving
customer engagement in online markets through AI technologies. To this end, semi-structured interviews
were used as the primary data collection tool. Participants in this study included managers and experts
active in the fields of information technology and online marketing who have direct experience using AI
technologies to improve customer engagement. Participants were purposefully selected, and efforts were
made to use diverse experiences and perspectives.
In this study, interviews were designed based on a semi-structured interview guide that included
questions about the participants’ experiences, views, and evaluations of using AI technologies in customer
interactions.
Data analysis from the interviews was conducted using content analysis. In this method, the textual
data from the interviews were first carefully studied and then coded to identify patterns, themes, and main
categories. This process was repeated to ensure that all significant aspects of the data were covered.
Findings
In this study, a total of 23 participants were involved, who had diverse demographic characteristics.
Of these, 14 (60%) were male, and 9 (40%) were female. The age of the participants ranged from 25 to
55 years, with the highest frequency in the 30-40 year age group, comprising 10 participants (43%). In
terms of education, 8 participants (35%) had a bachelor’s degree, 10 (43%) had a master’s degree, and 5
(22%) had a doctoral degree. Participants also had varying levels of work experience in information
technology and online marketing, ranging from 3 to more than 20 years.
Table 1
Final Results of Qualitative Data Analysis
Main Theme
Sub-theme
Concepts
Personalization
Strategies
Interactive
Strategies
Personalized
experience
Customer
shopping
behavior
analysis
AI-based
communications
Deep data analysis
Improving social media
engagement
Product recommendations, offering suggestions based on past behavior,
displaying relevant ads, designing personalized pages, content settings based on
geographic location
Purchase data analysis, web browsing behavior tracking, identifying purchase
patterns, sentiment analysis, predicting future preferences
Chatbots, personalized emails, automated customer support, instant responses to
inquiries, sending personalized messages at specific times
Complex data modeling, identifying hidden trends, optimizing marketing
strategies, customer segmentation analysis
Collaborative content creation, interactive campaigns, using influencers,
organizing contests and challenges, creating interactive video content
E-ISSN: 3041-8585
51
interaction
Virtual
technologies
Crowdsourcing ideas and
feedback
Custom
strategies
Enhancing
experience
technology
Optimization
Shopping
optimization
Site
optimization
Customer
prediction
content
user
with
path
performance
behavior
Enhancing security and
privacy
Loyalty Strategies
Data-based
programs
loyalty
Customer education and
support
Personalized
post
purchase experiences
Gamification
Augmented reality, virtual reality, virtual fitting rooms, virtual product tours,
displaying products in augmented reality environments
Customer feedback platforms, online surveys, analyzing customer reviews,
encouraging customer participation in product development, creating online
discussion forums
Targeted content creation for specific platforms, using analytical data to create
engaging content, providing exclusive content to active users
Improving the user interface with AI, behavioral analysis for UX optimization,
creating dynamic interactive environments
Sales funnel analysis, landing page optimization, reducing cart abandonment
rates, simplifying the payment process, increasing page loading speed
Faster page loading, improved UX/UI, mobile compatibility, site search
optimization, using big data to predict user behavior
Purchase prediction models, demand analysis, identifying best-selling products,
using machine learning algorithms to predict customer preferences, dynamic
pricing optimization based on behavioral patterns
Strengthening customer data security, advanced encryption, access management
and authentication, ensuring compliance with data protection regulations
Personalized rewards and incentives, loyalty behavior analysis, behavior-based
discounts, developing mobile apps to increase loyalty, multi-tiered reward
systems
Online webinars and training, 24/7 live support, AI-based product guides,
creating a customer training center, using video and interactive content for
education
Personalized thank-you emails, post-purchase surveys, suggestions based on
previous purchases, planning events for loyal customers, creating a space for
customer feedback in product improvement
Integrating game elements into apps and websites, scoring and ranking systems,
customer missions and challenges to increase engagement
This section examines strategies for improving customer engagement using AI technologies in
online markets through four main themes and multiple sub-themes. Data analysis and selected quotes from
the interviews indicate the diversity of strategies and their effectiveness in engaging with customers.
Personalization Strategies: A personalized shopping experience is one of the most important
tools for attracting and retaining customers. This includes product recommendations, offering suggestions
based on past behavior, and displaying relevant ads. One participant stated, “By using customers’ historical
data, we can offer suggestions that really align with their interests.” Deep data analysis was also
highlighted as a key tool for identifying trends and optimizing marketing strategies.
Interactive Strategies: Effective communication with customers through social media and virtual
interaction technologies was highlighted as a key factor in increasing engagement. Interviewees
emphasized the importance of collaborative content creation and the use of influencers: “Interactive
campaigns on social media play an important role in creating an emotional connection with customers.”
Optimization Strategies: Optimizing the shopping path and site performance are strategies that
can improve user experience and increase conversion rates. One respondent remarked, “Page loading
speed and ease of the payment process are key factors in increasing online purchase rates.”
Loyalty Strategies: Data-based loyalty programs and customer education and support were
identified as important tools for creating long-term loyalty. Personalized post-purchase experiences also
emphasized that “taking care of customers after purchase is as important as initial attraction.”
Discussion and Conclusion
This study examined the role of AI technologies in improving customer interactions in online
markets. The results showed that using AI can significantly enhance the customer shopping experience
through personalization and optimization of communications and interactions. In particular, AI-based
chatbots, personalized touchpoints in the customer journey, and the use of smart data for digital marketing
communications were identified as key factors in increasing customer satisfaction and loyalty.
Four main themes along with their related categories and concepts were identified in this study.
The main themes were: personalization strategies, interactive strategies, optimization strategies, and
loyalty strategies. Each of these themes had its own categories and concepts, specifically illustrating the
various methods and approaches for using AI technologies to enhance customer interactions in online
markets.
Personalization Strategies: This theme included three categories: personalized shopping
experience, customer behavior analysis, and AI-based communications. Concepts related to the
personalized shopping experience included product recommendations and suggestions based on past
behavior. The customer behavior analysis category highlighted purchase data analysis and web browsing
behavior tracking as key concepts. AI-based communications involved the use of chatbots and
personalized emails.
Interactive Strategies: This theme included improving social media engagement, virtual
interaction technologies, and crowdsourcing ideas and feedback. Key concepts for improving social media
engagement were collaborative content creation and the use of influencers. Virtual interaction
technologies encompassed concepts such as augmented reality and virtual reality. Crowdsourcing ideas
and feedback referred to platforms for customer feedback and online surveys.
Optimization Strategies: The third theme included shopping path optimization, site performance
optimization, and customer behavior prediction. Shopping path optimization was associated with sales
funnel analysis and landing page optimization. Site performance optimization involved faster page loading
and improved UX/UI. Customer behavior prediction was characterized by purchase prediction models and
demand analysis.
Loyalty Strategies: The fourth theme, loyalty strategies, included data-based loyalty programs,
customer education and support, and personalized post-purchase experiences. Important concepts
included personalized rewards and incentives, online webinars and training, and personalized thank-you
emails.
The results of this study indicated that using AI technologies at customer touchpoints throughout
the online shopping journey can significantly improve the customer experience and personalize and
optimize interactions (Ai-zhong & Zhang, 2022). Among the key findings was a significant increase in
customer satisfaction and loyalty following the use of AI-based chatbots, aligning with the findings of
Chen et al. (2021). This study emphasizes that AI chatbots, due to their quick and accurate responses, have
the potential to enhance the online shopping experience for customers (Chen et al., 2021). Additionally,
the results indicated the positive impact of AI technologies on fostering brand love and reinforcing
repurchase intention among new customers during the COVID-19 era, consistent with the findings of Ding
et al. (2022). This demonstrates that strategic use of AI can be a valuable resource for businesses in
challenging market conditions (Ding et al., 2022). Moreover, the importance of integrating ethical
principles in the use of AI, which can enhance customer experience and build trust in AI systems, is
E-ISSN: 3041-8585
53
affirmed (Dolganova, 2021). This aspect is particularly important in creating a positive business image
and maintaining a reliable relationship with customers. Furthermore, the findings of this study showed
that using smart data analysis in digital marketing communications can lead to more precise and targeted
messaging, consistent with Li’s (2022) results (Li, 2022). This underscores the importance of employing
AI in data analysis to optimize marketing strategies. Finally, the findings emphasize that the application
of AI in precision marketing can lead to meaningful relationships between brands and consumers (Yang
et al., 2021). These results align with previous studies indicating that AI technologies can not only improve
individual customer experiences but also help strengthen long-term customer sustainability and loyalty.
Overall, these findings confirm that the use of AI technologies in online marketing can be a
significant factor in improving customer relations and enhancing business performance.
Thus, it can be concluded that AI technologies play a crucial role in revolutionizing customer
interactions and enhancing online experiences. Strategic use of AI can assist businesses in identifying and
responding to customer needs and preferences in innovative ways, thereby helping to create meaningful
and lasting relationships with customers.
This study faced limitations, including the limited number and diversity of participants in the
interviews, which may affect the generalizability of the findings. Additionally, focusing on specific AI
technologies might have overlooked perspectives on other emerging technologies.
Future research could focus on exploring the impact of other AI technologies not covered in this
study. Moreover, conducting larger-scale studies with greater geographic and industry diversity could
provide a deeper understanding of how AI is used and its impact on customer interactions. Businesses
should prioritize integrating AI technologies into their marketing and customer service strategies. This
includes investing in advanced chatbots, developing personalized touchpoints in the customer journey,
and using smart data analytics for more precise marketing communications. Additionally, businesses must
pay attention to ethical principles in using AI to maintain customer trust and confidence.