ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 87
www.rsisinternational.org
Leveraging Data Analytics to Enhance Customer Experience in the
Food and Beverage (F&B) Industry: A Review of Practices and
Strategies
Z. Z. Zulpekri
1
, N. F. Razali
2*
, N. A. Abdul Aziz
3
, N. M. Ali
4
, M. N. Mohd Nizam
5
, N. S. Ahmad
6
1,2,3,4
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka
5
School of Technology, Asia Pacific University of Technology and Innovation
6
Fakulti Keusahawanan dan Technology, University Malaysia Kelantan
*Corresponding Author
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800010
Received: 06 November 2025; Accepted: 12 November 2025; Published: 18 December 2025
ABSTRACT
The rapid digital transformation in the food and beverage (F&B) industry requires businesses to continuously
adapt to evolving customer expectations, technology integration, and competitive pressures. This review
examines how data analytics enhances customer experience in F&B small and medium enterprises (SMEs),
particularly within the urban context of Kuala Lumpur. By analysing consumer purchasing behaviour, payment
methods, frequently ordered food categories, and customer feedback from delivery applications and social media
platforms, data analytics enable businesses to identify emerging patterns, personalise services, and design more
effective promotional strategies. Furthermore, data-driven insights improve operational efficiency, strengthen
customer relationships, and enhance profitability. This study also proposes a strategic framework for adopting
data analytics in the F&B sector, with emphasis on digital transformation, personalised services, and decision-
making efficiency. Ultimately, the findings highlight that data analytics is not only a competitive advantage but
also a necessity for achieving sustainable customer satisfaction in today’s digital economy.
Keywords: Customer Behaviour, Customer Experience, Data Analytics, Digital Transformation, Food and
Beverage Industry, Small and Medium Enterprises (SMEs), Personalised Services.
INTRODUCTION
The food and beverage (F&B) industry is undergoing rapid transformation in response to the digital revolution
and rising customer expectations. In today’s competitive environment, businesses are required to not only offer
quality products but also provide seamless and personalised customer experiences (Fang et al., 2023). To achieve
this goal, many are turning to data analytics as a strategic tool to gain deeper insights into customer behaviour
and improve service delivery.
Data analytics allows businesses to collect, analyse, and interpret large volumes of data using techniques such
as descriptive, diagnostic, predictive, and prescriptive analytics (Provost & Fawcett, 2013). When combined with
technologies such as artificial intelligence (AI), analytics can uncover purchasing trends and help businesses
make more effective decisions (Mahmud et al., 2021; Ali & Harrison, 2022). Despite widespread adoption in
sectors such as e-commerce and retail, the use of data analytics in the F&B industry is still growing.
In Malaysia, the rise of digital platforms like GrabFood and Foodpanda, along with the growing adoption of
point-of-sale (POS) systems and online reviews, has created new opportunities for F&B businesses to utilise data
more effectively (Hasan, Ibrahim & Koh, 2025). However, small and medium enterprises (SMEs) often struggle
to harness this data because of limited resources, expertise, and awareness of their strategic value.
This paper aims to examine how data analytics can be used to improve customer experience in the F&B industry,
especially among SMEs operating in urban areas. This study also provides insights into consumer patterns and
proposes a practical framework for data-driven decision-making in the F&B sector.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 88
www.rsisinternational.org
Background of Study
In the digital era, the food and beverage (F&B) industry is experiencing rapid growth due to technological
advancements and the increasing adoption of data-driven strategies (Fang et al., 2023). Businesses are not only
competing in product quality or price, but also in delivering meaningful and personalised experiences. With
changing customer expectations and the importance of customer satisfaction, F&B businesses are changing by
finding innovative ways to attract and retain loyal customers.
Data Analytics
Data analytics refers to the systematic process of collecting, processing, and interpreting large datasets through
statistical techniques, machine learning algorithms, and data visualisation (Provost & Fawcett, 2013). It aims to
extract valuable information to support organisations in making more accurate and strategic decisions. Among
the types of data analytics commonly used are descriptive analysis (what is happening), diagnostic analysis (why
it is happening), predictive analysis (what might happen), and prescriptive analysis (what appropriate action
should be taken). According to Mahmud et al. (2021), the use of data analytics in the retail industry allows
businesses to offer more personalised services, retain customers and improve operational efficiency (Mahmud
et al., 2021). Meanwhile, Ali and Harrison (2022) emphasised that the combination of Big Data and artificial
intelligence (AI) allows companies to identify purchasing patterns, customer needs and make strategic decisions
more effectively (Ali & Harrison, 2022). Although the context of this study focuses on e-commerce, the approach
used is relevant and can be applied in the F&B industry to optimise the customer experience.
In the F&B industry, data obtained through food delivery applications, online ordering systems, point-of-sale
(POS) systems, customer feedback, as well as Internet of Things (IoT) devices, such as temperature sensors and
inventory monitoring, are important sources for analysis. These data analytics help businesses identify peak
purchasing times, best-selling products, location-based customer needs, and consumer behaviour in more detail.
Furthermore, this supports the overall digital transformation of F&B by saving costs, time, and increasing
competitive advantage (Fosso Wamba et al., 2015).
Enhancing Customer
Customer experience encompasses all touchpoints between customers and businesses, from awareness, purchase
process, product usage, to after-sales support (Shaw & Ivens, 2005). In the context of F&B, this experience
includes the taste of food, speed of preparation, ease of ordering, digital interaction, and accuracy and efficiency
of delivery.
According to Lukita et al. (2023), implementing a digital e-menu system can speed up the ordering process and
reduce food delivery errors, thus increasing customer satisfaction (Lukita et al., 2023). The use of AI to analyse
customer reviews and social media data also allows businesses to understand customer perceptions in real time,
as well as identify issues or needs that require immediate action.
With the help of data analytics, businesses can identify customer preferences, organise targeted promotions based
on location or purchase history, and introduce intelligent recommendation systems that suggest menus based on
past orders. All of this results in a more personalised and seamless experience, thus fostering customer loyalty
and increasing long-term value to the business (Fosso Wamba et al., 2015).
F&B Industry
The food and beverage (F&B) industry in Malaysia is one of the most active and dynamic sectors, supported by
consumer demand for convenience and digital interaction. The development of food delivery applications such
as GrabFood and Foodpanda, as well as the emergence of virtual restaurants and smart ordering systems, have
revolutionised the way food is offered and delivered to consumers (Hasan, Ibrahim & Koh, 2025).
Today's F&B businesses are not just selling food but also selling experiences. Therefore, systems such as
interactive digital menus, online table reservations, cashless payments, and data-driven promotions have become
essential elements of daily operations. All these systems generate data that can be used to understand customer
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 89
www.rsisinternational.org
behaviour, predict demand, monitor stock, and devise more effective marketing strategies.
Schymanietz, Jonas & Moslein (2022) stated that successful businesses in the digital age are those that not only
produce products but also develop a data-driven service ecosystem (Schymanietz, Jonas & Moslein, 2022).
Therefore, understanding and applying data analytics is now an important aspect in ensuring the survival and
growth of F&B businesses, especially for small and medium enterprises (SMEs) that need to compete in an
increasingly challenging and competitive landscape.
Problem Statement
In this fast-paced digital era, the food & beverage (F&B) industry is rapidly evolving in line with the growing
demand for convenience, speed, and enhanced customer experiences. Today’s consumers no longer only evaluate
food quality but also pay attention to aspects of the overall experience, such as the ordering process, customer
service, and the suitability of offers to personal needs. Therefore, providing a personalised customer experience
has become a necessity, not an option (Ali & Harrison, 2022).
One effective approach to meeting this need is using data analytics, especially by analysing customer purchasing
patterns. Data such as order times, favourite products, and payment methods can reveal consumer behaviour
patterns that can be used to personalise services and enhance customer satisfaction (Afriyeni et al., 2024).
However, most small and medium-sized enterprises (SMEs) in the F&B industry have yet to fully leverage this
potential due to constraints such as a lack of skills, use of complex analytical tools, and low awareness of the
strategic value of customer data (Hasan, Ibrahim & Koh, 2025).
Furthermore, most previous studies related to the use of data analytics have focused more on the e-commerce
and retail industries (Ali & Harrison, 2022), while this study specifically examines how purchasing pattern
analysis can improve customer experience in the local F&B industry, which is still very limited. The lack of a
suitable local framework makes it difficult for SMEs to convert data into impactful strategies.
Without clear guidance, F&B SMEs are at risk of being left behind in digital competition. Therefore, this study
was conducted to identify how purchasing data can be analysed to improve customer experience in the F&B
industry in Malaysia, as well as propose an analytical framework that is practical and relevant to the current
needs of the industry.
Research Question
As a result of the statement of the research problem above, three research questions were formulated, namely:
1. What are the purchasing patterns in enhancing customer experience in the F&B industry?
2. How can the F&B industry leverage data analytics to improve customer experience in purchasing patterns?
3. What framework can be developed to enhance customer experience through data analytics in the F&B
industry based on purchasing patterns?
Research Objective
The main aim of this research was to present enhancing customer experience in the F&B Industry by using data
analytics. Hence, the related research objectives are as follows:
1. Identify the customer purchasing patterns in enhancing customer experience in the F&B industry
2. Evaluate the role of data analytics in enhancing customer experience and decision-making based on
purchasing patterns.
3. Propose a data analytics framework for enhancing customer experience in the F&B industry by using
purchasing patterns.
LITERATURE REVIEW
In the rapidly evolving digital age, the landscape of the food and beverage industry has changed drastically.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 90
www.rsisinternational.org
Businesses in this sector now face competition that is not only based on price and product quality alone but also
depends on the level of customer experience they can offer. Customer experience is now a key indicator in
determining the success of an F&B business, particularly in urban areas like Kuala Lumpur, where digitally
engaged customers expect a seamless, personalised experience from the first order to post-purchase interactions
(Lemon & Verhoef, 2016).
The rise of tech-savvy consumers has pushed F&B businesses to adopt digital platforms like Point-of-Sale (POS)
systems, e-menu, delivery apps such as Foodpanda and Grabfood, and social media to stay competitive. These
platforms generate a wealth of transactional and behavioural data, including ordering time, payment method,
frequently ordered items, and customer feedback. Analysing these purchasing patterns through data analytics
allows businesses to understand consumer behaviour and strategically enhance customer satisfaction (Fikry et
al., 2024).
Therefore, this study focuses on the digital F&B environment in Kuala Lumpur, using secondary data to explore
how purchasing patterns can be analysed to improve customer experience. Businesses that can effectively utilise
this data are more likely to remain relevant, responsive, and competitive in Malaysia’s digital economy
Data Analytics Concepts
Data analytics refers to the overall process of collecting, cleaning, interpreting and using data to support more
accurate and informed decision-making. In today’s data-driven business world, data analytics has become a key
component in an organisation’s digital transformation. According to Mahmud et al. (2021), there are four main
categories in data analytics, namely descriptive analytics, which provides a picture of what has happened. The
second category is diagnostic analytics, which looks for the reasons behind an event. Next, predictive analytics,
which makes predictions about what might happen. Finally, prescriptive analytics suggests the best course of
action based on the analysis of the data (Mahmud et al., 2021).
In the retail business industry, the implementation of comprehensive data analytics allows organisations to
predict customer demand, adjust inventory and form more effective marketing strategies (Mahmud et al., 2021).
Furthermore, in the context of e-commerce, the integration of Big Data and artificial intelligence (AI) enables
companies to identify purchasing patterns, determine consumer preferences, and generate automated and
personalised product recommendations (Oktaviani et al., 2024). Although the focus of the study is e-commerce,
the principles and approaches used are highly relevant in the F&B industry, especially in efforts to improve
customer experience based on solid data.
Customer Purchasing Patterns in the F&B Industry
Customer experience in the food and beverage (F&B) industry encompasses various aspects such as food
preparation, cleanliness of the premises, ease of ordering, and digital interaction through applications or websites.
All these aspects have a direct impact on customer satisfaction levels and their tendency to use the service again
or recommend it to others.
According to Lukita et al. (2023), the use of digital menus (e-menus) can speed up the ordering process and
reduce errors in delivery, thus improving the smoothness of the customer experience. This technological
advancement supports not only operational efficiency but also improves the dynamic between businesses and
customers (Lukita et al., 2023). This study also shows that customers tend to place orders faster when using more
visual and interactive digital displays. Furthermore, these e-menu systems are often accompanied by automatic
recommendation features such as ‘add-ons’ or ‘recommended items’, which indirectly influence customer
purchasing patterns towards increasing order value.
Meanwhile, research by Oktaviani et al. (2024) emphasises the role of artificial intelligence (AI) in analysing
customer purchasing behaviours through online reviews and social media feedback. Such analysis enables
businesses to identify service issues more quickly and respond proactively, thereby reinforcing customer trust
and satisfaction. More importantly, AI helps detect specific purchasing patterns, including peak hours, frequent
orders, and repeat ordering behaviour. These patterns allow businesses to personalise customer experiences
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 91
www.rsisinternational.org
through systems like Customer Relationship Management (CRM), which can offer time-based promotions or
loyalty rewards based on an individual’s order history by making interactions more relevant and meaningful
(Oktaviani et al., 2024).
Overall, these two studies show that investing in digital technologies and behavioural data analysis is no longer
optional but essential. Customer purchasing patterns act as a critical foundation for enhancing F&B customer
experience, enabling businesses to personalise service delivery, optimise operations, and continuously adapt to
evolving customer needs. It is no longer an option, but a necessity in the increasingly competitive F&B
landscape.
Peak Hours for Purchasing
Understanding peak buying times is one of the most important components of analysing customer purchasing
patterns in the F&B industry. These patterns, such as frequent lunch orders between 12 pm and 2 pm or high
weekend demand, can reveal when customers are most active and allow businesses to optimise both service
delivery and resource management. According to Oktaviani et al. (2024), the use of artificial intelligence (AI)
allows businesses to detect peak demand periods based on historical transaction data, facilitating more effective
planning for staff and inventory (Oktaviani et al., 2024).
In addition, when this data is integrated with a customer relationship management (CRM) system, businesses
can implement time-sensitive and personalised offers. For example, lunch promotions can be automatically
triggered for loyal customers during peak lunch hours, increasing their satisfaction by providing timely and
relevant offers. This not only improves business performance but also reinforces the perceived value of the
service provided.
Lukita et al. (2023) further emphasise that interactive digital menus help speed up the ordering process during
peak periods. By providing automated recommendations and visually appealing displays, customers can make
faster decisions, reducing wait times and queue congestion, two key factors that affect the quality of customer
experience during peak hours (Lukita et al., 2023).
In short, peak-hour purchasing patterns, when analysed and effectively utilised through technologies such as AI,
CRM and e-menus, enable F&B businesses to deliver faster, more relevant and more satisfying customer
experiences. These tools transform what can be stressful and congested time into a seamless interaction tailored
to the needs of customers.
Order Frequency
Customer order frequency is a key purchasing pattern that reflects customer loyalty and engagement in the F&B
industry. According to Oktaviani et al. (2024), artificial intelligence (AI) enables businesses to detect repeat
purchasing behaviour from historical transaction data. By identifying customers who order weekly, monthly, or
seasonally, businesses gain deeper insights into individual commitment levels, which in turn inform strategies
to improve the overall service experience (Oktaviani et al., 2024).
When coupled with customer relationship management (CRM) tools, these insights support segmenting
customers into groups such as loyal, occasional, or inactive users. Businesses can then personalise
communications and rewards that offer exclusive offers to loyal customers or re-engagement promotions to those
with declining activity (Oktaviani et al., 2024). This targeted engagement not only encourages repeat purchases
but also increases perceptions of brand care and relevance, contributing positively to customer satisfaction.
Furthermore, as noted by Lukita et al. (2023), user-friendly digital interfaces such as e-menus streamline the
ordering process, making it faster and more intuitive. When customers consistently experience convenience and
efficiency, they are more likely to return, thus increasing their purchase frequency. Over time, this builds a more
consistent and loyal customer base that associates the brand with reliability and convenience (Lukita et al., 2023).
Favorite Menu Types
Identifying the types of food or beverages that customers like the most is an important purchasing pattern in the
F&B industry. A study by Oktaviani et al. (2024) shows that the use of artificial intelligence (AI) technology
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 92
www.rsisinternational.org
allows businesses to analyse past order data to identify the most frequently ordered menus (Oktaviani et al.,
2024). With this information, management can increase the stock of popular items or create new variations, thus
increasing customer satisfaction because their needs are met more accurately.
Knowledge of favourite menus also allows for more targeted marketing strategies. For example, if data shows
that coffee-based beverages are most ordered in the morning, promotions such as “Morning Coffee Deals” can
be offered at that time. This approach, supported by a CRM system, makes offers more relevant and effective
and encourages repeat purchases (Oktaviani et al., 2024).
In addition, according to Lukita et al. (2023), the visual presentation on menus also influences customer selection.
Attractive and interactive image displays can highlight certain items and lead to the formation of customer
preference trends (Lukita et al., 2023). This combination of data analysis and digital presentation contributes to
a more engaging and satisfying customer experience.
Interactive Visuals and E-Menus
Interactive visuals in e-menu systems play a key role in shaping customer purchasing patterns and enhancing the
overall experience. According to Lukita et al. (2023), displaying menus with high-resolution images, animations,
and additional information such as ingredients or side dish suggestions can increase customer confidence when
making a choice. These features not only speed up the ordering process but can also trigger impulsive purchases,
especially among mobile app users (Lukita et al., 2023).
In addition to convenience, interactive e-menus also support inclusivity and personalisation. Clear labels such as
“gluten-free” or nutritional information displays allow customers with special dietary needs to feel more
appreciated. This helps create a more trustworthy and relevant dining experience for individuals (Lukita et al.,
2023).
Another major advantage is the ability of e-menus to be updated in real time. As stated by Lukita et al. (2023),
this functionality allows businesses to inform customers of stock availability or the latest promotions without
having to reprint physical menus. This capability is particularly useful during peak hours as it reduces confusion,
speeds up ordering, and contributes to a smoother and more efficient customer experience (Lukita et al., 2023).
Recommendations Based on Purchase History
In today’s digital F&B environment, personalised recommendations based on customer purchase history have
become an important element in enhancing customer experience. According to Lukita et al. (2023), e-menu
systems equipped with functions such as “most frequently ordered” or “other customers also bought” use past
order data to automatically suggest complementary items. This facilitates customer decision-making and adds
value to the ordering process (Lukita et al., 2023).
Oktaviani et al. (2024) also stated that when AI is combined with a customer relationship management (CRM)
system, businesses can offer highly personalised promotions and menu suggestions. By analysing customer
purchase patterns and tendencies, the system can suggest items that are compatible with individual tastes and
purchasing habits (Oktaviani et al., 2024). This approach makes the customer’s experience easier, more relevant
and enjoyable.
Furthermore, such personalised recommendations contribute to increased customer loyalty. When customers feel
that the system understands their needs and provides accurate recommendations, they are more likely to return
to using the service. As stated by Oktaviani et al. (2024), this data-based strategy not only increases satisfaction
but also builds strong long-term relationships between customers and businesses (Oktaviani et al., 2024).
The Role of purchasing patterns in enhancing customer experience through data analytics
Understanding customer purchasing patterns, such as order frequency, loyalty to a particular menu, and price
sensitivity, provides important insights to improve customer experience in the F&B industry. According to Mittal
et al. (2023), analysis of this behavioural data allows businesses to better understand customer expectations and
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 93
www.rsisinternational.org
tailor services to their needs. This includes more personalised pricing strategies, loyalty programs, and menu
offerings, thereby making the dining experience more satisfying and relevant (Mittal et al., 2023).
The use of data analytics tools such as customer segmentation also strengthens the decision-making process.
Calvo-Porral & Levy-Margin (2017) stated that by grouping customers according to behavioural patterns,
businesses can deliver more personalised communications and adjust promotions to be more targeted (Calvo-
Porral & Levy-Margin, 2017). This approach makes customers feel more valued, as the offers and services they
receive are in line with their purchasing habits.
In addition, machine learning techniques such as Support Vector Regression are used to predict demand based
on purchase history and location. According to Ismail & Hooy (2023), this predictive model helps businesses
make smarter decisions in inventory management, employee scheduling, and production planning (Ismail &
Hooy, 2023). Ismail & Hooy (2023) also show that AI-based systems can optimise delivery times and order
accuracy. These data-driven decisions not only improve operational efficiency but also ensure that customers
receive faster, more accurate, and more consistent service, especially during peak hours (Ismail & Hooy, 2023).
Targeted Promotions
Targeted promotions tailored to customer purchasing patterns are among the most effective strategies for
improving customer experience in the F&B industry. According to Mittal et al. (2023), segmenting customers
based on behaviours such as ordering frequency, menu preferences, or price sensitivity allows businesses to offer
more relevant and meaningful promotions. Customers who receive offers that align with their interests are more
likely to respond positively and make repeat purchases (Mittal et al., 2023).
Technologies such as artificial intelligence (AI) and customer relationship management (CRM) systems allow
customer data to be automatically analysed to identify potential segments. For example, customers who
frequently order in the morning can be offered breakfast promotions, while customers who frequently order in
large quantities can receive bulk discounts. A study by Calvo-Porral & Levy-Margin (2017) supports this
approach by stating that marketing personalisation based on behavioural data has been shown to increase
campaign effectiveness and promotion redemption rates (Calvo-Porral & Levy-Margin, 2017).
Additionally, the use of automated systems to deliver promotions via mobile apps or customer emails implements
targeted promotions more consistently and efficiently. Calvo-Porral & Levy-Margin (2017) also asserted that
automation in marketing based on behavioural segmentation not only increases campaign effectiveness but also
reduces costs by avoiding sending promotions to irrelevant segments. As a result, customers receive more
relevant offers and a more personalised and seamless interaction experience (Calvo-Porral & Levy-Margin,
2017).
Menu and Pricing Optimisation
Customer purchasing pattern analysis plays a key role in menu optimisation and pricing strategies in the F&B
industry. Through historical order data, businesses can identify best-selling items, less popular menus, and price
points that are sensitive to customers. According to Mittal et al. (2023), understanding customer price
sensitivities allows managers to adjust prices without affecting demand, thus maintaining customer satisfaction
while silently making strategic adjustments (Mittal et al., 2023).
A study by Ismail & Hooy (2023) shows that the use of machine learning models such as Support Vector
Regression can predict sales performance and support more accurate pricing decisions. This model not only
considers sales history, but also location factors, allowing prices to be adjusted according to branch or operating
area. As a result, businesses can adjust prices more strategically based on the context of the customer’s
environment, thus increasing relevance and satisfaction with the value offered (Ismail & Hooy, 2023).
In addition, purchasing pattern data can also be used to restructure the menu by emphasising popular items and
reducing or replacing less popular items. This approach not only reduces raw material waste and operating costs
but also helps customers make faster and more satisfying choices. A study by Ismail & Hooy (2023) confirmed
that data-based sales analysis helps management make strategic decisions such as menu adjustments and more
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 94
www.rsisinternational.org
effective production capacity management, all of which contribute to a better and more efficient customer
experience (Ismail & Hooy, 2023).
Personalisation and Customer Loyalty
loyalty, and its implementation becomes more effective with the help of collected purchase data. According to
Mittal et al. (2023), customer behavioural data such as purchase history and price sensitivity play a key role in
developing a truly meaningful loyalty program. For example, rewards can be given based on frequently
purchased items or at consistent purchase times and make customers feel valued and understood (Mittal et al.,
2023).
In a regional context, a study by Liem et al. (2023) showed that AI-based ordering systems used in Southeast
Asian countries have helped personalise services. Through these systems, customers receive recommendations
and rewards based on their purchasing habits. This approach not only makes the customer experience more
relevant but also strengthens the long-term relationship between the customer and the brand (Liem et al., 2023).
Consistent personalisation also contributes to increased satisfaction and repeat purchases. When customers
realise that the system is constantly adapting the experience based on their individual needs, they are more likely
to remain loyal and not switch to competitors. Mittal et al. (2023) stated that personalisation based on purchase
data increases perceived brand value and drives customer loyalty (Mittal et al., 2023). Liem et al. (2023) also
confirmed that AI systems that provide personalised purchase recommendations directly contribute to increased
satisfaction and repeat purchases in Southeast Asia (Liem et al., 2023).
Increasing Average Transaction Value
One of the important strategies in the F&B business is to increase the average transaction value per customer.
Understanding customer purchasing patterns, promotions and product recommendations can be designed to
encourage customers to buy more than was planned. A study by Mittal et al. (2023) showed that implementing
upselling and cross-selling strategies based on purchasing behaviour has proven effective in increasing customer
order volume (Mittal et al. 2023).
For example, an AI system that suggests “additional drinks” or “combo packages” during the ordering process
can indirectly increase purchase volume. Liem et al. (2023) proved that an automated ordering system in the
Southeast Asian region successfully implemented this strategy, through product suggestions displayed when
customers place an order in the application. This approach not only makes it easier for customers but also triggers
impulsive purchases that enhance the digital shopping experience (Liem et al., 2023).
Furthermore, this strategy is supported by interactive visuals in the e-menu that are attractive and convincing.
Engaging visual displays help customers make decisions more easily, as well as provide relevant and valuable
recommendations. According to Liem et al. (2023), AI-based ordering systems that contain product
recommendation functions and interactive visuals have helped drive additional purchases more efficiently,
thereby contributing to increased transaction value and overall customer satisfaction (Liem et al., 2023).
Customer Retention Strategies
Attracting new customers is important, but retaining existing customers is more cost-effective and profitable in
the long run. Through customer purchase data, F&B businesses can design more targeted and high-impact
retention strategies. A study by Calvo-Porral & Levy-Margin (2017) shows that customer segmentation based
on behavioural data helps to shape more personalised communications, thus strengthening the emotional
connection between customers and brands (CalvoPorral & Levy-Margin, 2017).
In addition, Ismail & Hooy (2023) emphasise that business performance can be predicted in advance through
data analysis, including changes in purchase frequency or order value reduction (Ismail & Hooy, 2023). This
information allows proactive actions to be taken, such as providing coupons, special promotions, or
automatically reactivating customers who have not interacted for a long time. This makes the service more agile
and responsive to changes in customer behaviour.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 95
www.rsisinternational.org
Finally, data-driven loyalty programs provide more meaningful rewards and are aligned with customers’
purchasing habits. When rewards are tailored to customers’ real needs, they are more likely to stay with the
brand. This combination of data-driven strategies and personalised communications provides a solid foundation
for building sustainable and ongoing customer loyalty.
The Role of Data Analytics in Enhancing Customer Experience
The use of data analytics allows businesses to understand customer needs and wants more deeply. Through
various channels such as online booking systems, mobile applications, customer reviews and social media sites,
companies can collect valuable data to form the basis for more focused and impactful strategic decisions.
A study by Abell, Biswas, & Arroyo Mera (2024) shows that digital ordering systems not only make things
easier for customers, but also produce detailed data such as ordering patterns, peak times and customer
preferences. This data can be used to reorganise staff work schedules, plan special promotions at certain times,
and run tailored offers based on customer order history (Abell, Biswas, & Arroyo Mera, 2024).
Meanwhile, Jain et al. (2023) asserted that monitoring customer sentiment through social media allows
businesses to detect issues early. Through proactive actions, the company’s reputation can be maintained, and
customer loyalty levels can be increased (Jain et al., 2023). A study by Oktaviani et al. (2024) also shows that
using AI to segment customers and predict behaviours such as customer churn risk allows businesses to
implement special offers or loyalty campaigns targeting high-risk groups. The ability to interpret data quickly
and accurately is a key advantage for modern F&B businesses in building more effective, consistent and
personalised customer experiences (Oktaviani et al., 2024).
Challenges in The Use of Data Analytics
While the use of data analytics in the F&B sector provides many benefits, its implementation also faces several
complex challenges. One of the main issues is cost. Investment in technologies such as advanced analytics
software, data integration systems, and hiring a highly skilled workforce requires significant financial resources,
something that may be difficult for small and medium-sized businesses (SMEs) to achieve (Abell, Biswas, &
Arroyo Mera, 2024).
In addition, there are challenges in terms of the quality and completeness of the data collected. Inconsistent or
incomplete data can affect analytical capabilities and produce inaccurate results. The issue of silo systems, where
data is stored separately on different platforms, can also prevent comprehensive information integration and
affect the efficiency of strategic decision-making.
From an ethical and legal perspective, the use of customer data needs to comply with personal data protection
guidelines such as GDPR or PDPA. The use of AI and algorithms can also raise issues of bias if not handled
transparently and fairly. According to Oktaviani et al. (2024), bias in analytical models can cause some customers
to receive disproportionate services or offers, thus eroding trust in the brand. Therefore, implementing a strong
data management system, continuous staff training, and adherence to data ethics principles are important steps
to ensure that data analytics provides long-term benefits to customers and businesses (Oktaviani et al., 2024).
CONCEPTUAL FRAMEWORK
The conceptual framework of this research is built by combining and adapting key elements customer purchasing
patterns to enhance the F&B industries, role of customer purchasing patterns to enhance the F&B industry’s
profitability, the role of data analytics in enhancing customer experience, challenges in the use of data analytics
and the following three major studies relevant to the topic, namely the research by Sukwadi (2015), Latino &
Menegoli (2022), and Abu Khalifeh & Mat Som (2012). All three models offer different but complementary
approaches to understanding how analytical data can be leveraged to improve customer experience in the food
and beverage (F&B) industry.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 96
www.rsisinternational.org
Business Analytics for Data-Driven Decisions
The first model referenced in this research is from Sukwadi (2015), which emphasises the important role of
business analytics in helping organisations make better decisions and improve service quality based on customer
data (Sukwadi, 2015). In today’s digital era, customer data can be obtained from various touchpoints such as
purchase records, customer feedback, and user behaviour on websites and mobile applications.
Through the use of techniques such as descriptive analytics and predictive analytics, organisations can identify
customer purchasing patterns and their preferences (Sukwadi, 2015). This information is then used to tailor
products and services to better align with customer needs. For example, if data shows that customers often buy
plant-based foods on weekends, management can offer special promotions during those times.
This data-driven approach not only helps in improving operational efficiency, such as inventory management
and resource allocation, but also contributes to a more satisfying overall customer experience. This is because
customers feel valued when their needs and preferences are understood and met accurately.
Figure 1. Customer Experience Management (CEM) (Sukwadi, 2015)
Cybersecurity and Customer Trust
The second model refers to research by Latino & Menegoli (2022), which emphasises the importance of
cybersecurity in maintaining customer trust in a brand or digital platform. In an increasingly sophisticated digital
environment, customers are now increasingly concerned about the security of their personal information, such
as payment data, email addresses, and online behaviour (Latino & Menegoli, 2022).
This research shows that secure digital systems can increase consumer confidence in brands while also having a
positive impact on the user experience. This is because when customers are confident that their information is
well protected, they will be more open to interacting, making repeat purchases, and sharing feedback without
worry (Latino & Menegoli, 2022).
With this, security issues are no longer considered merely technical issues but become an important component
of customer experience strategies. For example, the introduction of two-factor authentication (2FA), the use of
data encryption, and continuous cyber threat monitoring systems are among the steps that can increase customer
trust (Latino & Menegoli, 2022).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 97
www.rsisinternational.org
Figure 2. Cybersecurity in F&B (Latino & Menegoli, 2022)
Digital Analytics and Customer Engagement
The third model used in this study is from Abu Khalifeh & Mat Som (2012), which explores how digital analytics,
such as sentiment analytics and customer segmentation, can be leveraged to increase customer engagement. The
data used in this model is collected from various digital sources such as social media, mobile applications, and
online platforms (Abu Khalifeh & Mat Som, 2012).
By analysing this data, businesses can build more personalised and tailored marketing strategies according to
specific customer segments. For example, if the analysis finds that customers in their 20s to 30s are more
responsive to promotions on Instagram, then the marketing strategy can focus on visual and interactive content
on that platform (Abu Khalifeh & Mat Som, 2012).
These analytics also allow businesses to identify customers who are at risk of discontinuing their services, and
act early to renegotiate them. This approach not only encourages active interaction between customers and brands
but also indirectly builds customer loyalty in the long term (Abu Khalifeh & Mat Som, 2012).
Figure 3. Data Analytics for Customer Engagement (Abu Khalifeh & Mat Som, 2012)
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 98
www.rsisinternational.org
Synthesis of the Model and Research Framework
Based on the three models described, a conceptual framework for the research was formed that combines four
main components that are closely related to each other. First, the process of collecting customer data digitally is
carried out using mobile applications, websites, and social media. This method allows organisations to obtain a
comprehensive picture of customer behaviour, needs, and preferences in real time. Second, the data that has been
collected will be processed and analysed using various methods, including descriptive analytics, predictions, and
segmentation, to provide a basis for more informative and responsive decision making.
Third, the aspect of customer information security is also given attention through the implementation of a strong
cybersecurity system. This is not only important to protect customer sensitive information but also helps build
user trust and confidence in the company’s digital platform. Fourth, the results of the data analysis are utilised
in the implementation of more targeted business strategies that are in line with customer tastes. This includes the
production of customised promotional offers, relevant digital marketing campaigns, and improving the quality
of service based on user feedback.
The framework is designed to achieve four key outcomes, which are improving service quality, ensuring security
and reliability of digital systems, implementing promotions based on consumer preferences, and strengthening
customer loyalty in the long term. Overall, the framework not only provides a solid theoretical foundation for
this study but also serves as a practical guide for businesses in the food and beverage industry who want to
implement a more data-driven and customer experience-focused approach digitally.
RESULTS AND DISCUSSION
Comparison of Three Framework Models Based on Literature
This section is to systematically compare the frameworks for the three models used in this research. This
comparison includes elements such as similarities, differences in terms of approaches and technologies used, as
well as how the frameworks can be integrated into this research. To strengthen the theoretical foundation, a
comparative summary of prior studies (Table 1) shows that existing research explores customer experience
management (Sulwadi, 2015), cybersecurity and trust in F&B systems (Latino & Menegoli, 2022; Liem et al.,
2023), and data-driven customer engagement (Abu Khalifeh & Mat Som, 2012; Jain et al., 2023). While these
frameworks contribute valuable insights into service quality, digital trust, and analytics-driven marketing, they
remain fragmented. Few studies integrate operational analytics, cybersecurity, and customer engagement into a
unified strategic framework. Furthermore, prior research often focuses on single data sources such as customer
reviews, segmentation analytics, or security systems without examining how multi-source data can collectively
enhance customer experience on digital F&B platforms. Thus, there remains a research gap in developing a
holistic model that merges customer experience management, data analytics, and cybersecurity to drive
continuous improvement in digital food-service environments.
Table 1. Comparison of Customer purchasing elements to enhance decision making and customer value
Framework
Author &
Year
Similarity
Difference
How the Framework Can Be
Integrated
Customer
Experience
Management
(CEM)
Sukwadi
(2015)
Both studies emphasise
the importance of
experience and the use
of data in making
strategic decisions.
Using technology such
as customer feedback
systems, service
quality monitoring, and
Focus on
comprehensive
customer experience
management with a
combination of
SERVQUAL and
Quality Function
Deployment models.
More operational and
Can be used to identify customer
touchpoints in the purchasing and
delivery process through food apps
(Lukita et al., 2023; Oktaviani et al.,
2024).
Analysing digital feedback and point-
of-sale (POS) transaction data can help
build more accurate customer
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 99
www.rsisinternational.org
analysis of consumer
perctions.
focused on restaurants
or cafes,
The data flow is linear,
referring to the level of
customer interaction in
experience strategies (Jain et al. 2023).
Useful for developing real-time
dashboards to monitor satisfaction and
delivery times (Abell, Biswas, &
Arroyo Mera, 2024).
The main goal is to increase customer
satisfaction through continuous service
system improvement.
Cybersecurity
in F&B
Latino &
Menegoli
(2022)
Demonstrate the
connection between
digital systems in the
F&B industry
operations, with direct
contact with end users.
Using technologies
such as IoT, cloud
computing, security
monitoring systems,
and customer data
privacy protection.
Focus on information
without customer
security, directly
emphasising the
customer experience
aspect in the context of
services.
The data flow starts
from user input to
security threat
detection mechanisms.
Useful for evaluating the security and
trust aspects of F&B applications
(Oktaviani et al., 2024).
Critical for building customer
confidence, especially in digital
payments and personal data handling
(Liem et al., 2023).
Can be integrated to support the trust
and reliability layer of digital customer
experience platforms.
Data
Analytics for
Customer
Engagement
Abu
Khalifeh &
Mat Som
(2012)
Emphasising the
capabilities of digital
analytics in improving
customer
understanding, through
techniques such as
segmentation and
sentiment analysis.
Including artificial
intelligence
technologies (AI), data
mining, social media,
and automated product
recommendation
systems.
Focus more on
marketing strategies to
increase customer
engagement, rather
than the entire delivery
process.
Data flow is
hierarchical from data
collection, processing,
to personalised
promotions.
Based on Purchase History (Lukita et
al., 2023; Oktaviani et al., 2024). –
Supports personalised promotions
through AI and customer relationship
management integration (Mittal et al.,
2023; Calvo-Porral & Levy-Mangin,
2017).
Enable sentiment analysis of the online
reviews service (Jain et al., 2023).
Main goal: strengthen long-term
relationships through data-driven
customer segmentation and loyalty
enhancement (Liem et al., 2023;
Ismail& Hooy, 2023).
CONCLUSION
The proposed framework, by referring to Abu Khalifeh & Mat Som (2012), demonstrates how data analytics can
be used strategically to improve customer experience and profitability in the F&B industry. By combining
elements such as customer touchpoints, transaction monitoring, security and trust, and recommendation systems,
businesses can gain a deeper understanding of customer purchasing patterns and respond proactively. For
example, the use of user-friendly digital menus not only simplifies the purchasing process but also allows data
to be collected to personalise future offers. Similarly, monitoring sales data can help plan stock and staff more
efficiently. All of these elements not only increase customer satisfaction but also contribute to increased sales
and customer loyalty, thus positively impacting overall business growth.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 100
www.rsisinternational.org
Figure 4. Data-Driven Framework for Enhancing Customer Experience in the F&B Industry through Customer
Purchasing Pattern Analysis
LIMITATIONS AND FUTURE RESEARCH
The proposed framework is conceptual and requires empirical validation to determine its effectiveness across
different F&B contexts. Additionally, the integration of multi-source data raises important data privacy concerns,
particularly regarding regulatory compliance and secure data handling, which future research should address
through privacy-preserving analytical methods. The increasing reliance on AI-driven personalisation also
introduces ethical challenges, including algorithmic bias, transparency, and accountability, warranting further
exploration of responsible AI governance in the F&B sector. Moreover, the framework does not consider
implementation costs, cybersecurity investments, or workforce capabilities, which may limit adoption, especially
among SMEs. Future studies should therefore examine cost–benefit implications, scalable solutions, and
supporting policies to enhance the practical applicability of data analytics in improving customer experience.
ACKNOWLEDGEMENT
The authors acknowledge the support given by Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti
Teknikal Malaysia Melaka, for the financial support and facilities provided in completing this research. The
authors would like to thank the Centre of Technopreneurship Development (Cted), UTeM, for their direct and
indirect contributions.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 101
www.rsisinternational.org
REFERENCES
1. Abell, A., Biswas, D., & Christian Arroyo Mera. (2024). Food and technology: Using digital devices
for restaurant orders leads to indulgent outcomes. Journal of the Academy of Marketing Science, 52,
:1673-1691. https://doi.org/10.1007/s11747-024-01029-6
2. Abu Khalifeh, A. N., & Mat Som, A. P. (2012). Service quality management in the hotel industry: A
conceptual framework for food and beverage departments. International Journal of Business and
Management, 7(14), 135–141. https://doi.org/10.5539/ijbm.v7n14p135
3. Afriyeni, Revy Verninda Rinesia, & Abdul Hamid. (2023). Analisis Penggunaan Teknologi Informasi,
Inovasi Produk, dan Orientasi Pasar Terhadap Keunggulan Bersaing UMKM Sektor Kuliner Kota
Padang. Mabsya: Jurnal Manajemen Bisnis Syariah, 6(1), 115–126.
https://doi.org/10.24090/mabsya.v6i1.10895
4. Ali, A., & Harrison, E. (2022). AI and Big Data Analytics: Driving Innovation in E-commerce and
Customer Experience. https://doi.org/10.13140/RG.2.2.13430.20805
5. Calvo-Porral, C., & Lévy-Mangin, J.-P. (2017). Store brands’ purchase intention: Examining the role
of perceived quality. European Research on Management and Business Economics, 23(2), 90–95.
https://doi.org/10.1016/j.iedeen.2016.10.001
6. Fosso Wamba, S., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How “Big Data” Can
Make Big impact: Findings from a Systematic Review and a Longitudinal Case Study. International
Journal of Production Economics, 165(165), 234–246. https://doi.org/10.1016/j.ijpe.2014.12.031
7. Hasan, N. H. M., Ibrahim, N. F., & Jun, K. C. (2025). Drivers of Customer Satisfaction in Malaysia’s
Online Food Delivery Services: A Pilot Study. International Review of Management and Marketing,
15(3), 1–9. https://doi.org/10.32479/irmm.17803
8. Jain, R., Kumar, A., Nayyar, A., Dewan, K., Garg, R., Raman, S., & Ganguly, S. (2023). Explaining
sentiment analysis results on social media texts through visualization. Multimedia Tools and
Applications, 82. https://doi.org/10.1007/s11042-023-14432-y
9. Nadiah Hanun Ismail, & Chee-Wooi Hooy. (2023). Predicting Restaurant Revenue using Machine
Learning. Journal of Contemporary Issues and Thought, 13(2), 9–22.
https://doi.org/10.37134/jcit.vol13.2.2.2023
10. Latino, M. E., & Menegoli , M. (2022). Cybersecurity in the food and beverage industry: A reference
framework. Computers in Industry, 141(103702). https://doi.org/10.1016/j.compind.2022.103702
11. Liem, G. S., Koay, L. K., Sanderan, P. A., Pong, H. L., Poon, Z. Y., Marimuthu, S. A., Gisca, A. P.,
Gupta, M., Saxena, M., & Kee, D. M. H. (2023). AI-assisted Food Ordering and Delivery Management
System for KFC: Insights from Malaysia, Indonesia and India. Journal of the Community
Development in Asia, 6(3), 49–63. https://doi.org/10.32535/jcda.v6i3.2540
12. Lukita, C., Riza, H., Ruli Supriati, Santoso, L., & Kamil, M. (2023). Exploration of Perceived Use of
Technology Using A Digital Business Perspective. ADI Journal on Recent Innovation (AJRI), 5(1Sp),
87–96. https://doi.org/10.34306/ajri.v5i1sp.984
13. Mahmud, A., None Nur Vanu, Sadia, N., & Hasan, N. R. (2021). Enhancing Customer Experience
and Business Operations in E-Commerce Platforms through Big Data Analytics. Journal of Business
and Management Studies, 3(2), 288–295. https://doi.org/10.32996/jbms.2021.3.2.30
14. Oktaviani, I., Purawanto, E., & Triana, T. (2024). Analysis of AI-Based Big Data for Strategic
Decision-Making in E-Commerce. Proceedings of International Conference on Science, Health, and
Technology, 266–278. https://doi.org/10.47701/icohetech.v5i1.419
15. Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know About Data
Mining and Data. O’reilly.
16. Schymanietz, M., Jonas, J. M., & Möslein, K. M. (2022). Exploring data-driven service innovation—
aligning perspectives in research and practice. Journal of Business Economics, 92(7), 1167–1205.
https://doi.org/10.1007/s11573-022-01095-8
17. Shaw, C., & Ivens, J. (2005). Building great customer experiences. Palgrave Macmillan.
18. Sukwadi, R. (2015). Utilising Customer Experience Management Framework to Create a Delightful
Service Experience. International Journal of Industrial Engineering and Management, 6(1), 29–42.
https://doi.org/10.24867/ijiem2015-1-105
19. Fang, Q., Yu, N., & Xu, H. (2022). Governance effects of digital transformation: from the perspective
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 102
www.rsisinternational.org
of accounting quality. China Journal of Accounting Studies, 11(1), 1–31.
https://doi.org/10.1080/21697213.2023.2148944
20. Mittal, V., Kumar, P., & Tsiros, M. (2023). Customer satisfaction, loyalty behaviors, and firm financial
performance: What years of research tells us. Marketing Letters, 34(2), 171-187.
https;//doi.org/10.1007/s11002-023-09671-w
21. Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the
Customer Journey. Journal of Marketing, 80(6), 69–96. Sagepub. https://doi.org/10.1509/jm.15.0420
22. Fikry, A., Abdul, A. J., Sibghatullah, A., Muhammad, N. S., & Harun, Z. F. (2024). From Click to
Cuisine: Navigating the Landscape of Customer Satisfaction in Online Food Delivery Apps.
Information Management and Business Review, 16(3(I)S), 579–588.
https://doi.org/10.22610/imbr.v16i3(i)s.4085
