Customer Behavior Analysis Research Articles

Optimizing Customer Relationship Management with Surprise Program: A Quantitative Approach

This research explores the implementation of customer relationship marketing strategies through the a program launched by PT XYZ. The program was designed to enhance customer loyalty with personalized special offers and exclusive promotions. This study employs quantitative methods and the data were obtained from the analysis of company reports and customer demographics, geography, behaviour, and psychology. Using K-means clustering, this study analyses data collected from XYZ's CRM surprise program, focusing on customer interests in video, games, and music, as well as their payment methods (prepaid or postpaid). The findings reveal significant variations in digital content consumption between prepaid and postpaid users. This segmentation enables XYZ to tailor its service offerings more effectively. The study highlights the potential for increasing customer satisfaction and loyalty through personalized offers, underscoring the importance of behavioural and psychographic data in optimizing service delivery.

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Customer Behavior Analysis and Reinforcement Learning Application in Tourism Service Design

Digital Marketing and Consumer Nutritional Behavior

Digital marketing involves promoting and selling products and services online and it is becoming extremely critical for retailers to gain new customers. Digital marketing uses online platforms, e-mail communications and messaging services, advertisements, and multi-modal online techniques. Digital marketing techniques can be used in parallel with machine learning and artificial intelligence to provide more effective results in analyzing customer behavior. Artificial intelligence (AI) has become an important and popular tool in the 21st century, affecting almost every sector, including engineering, science, medicine, business, finance, and marketing. However, AI’s dominance in every field of life, especially in digital marketing, may cause some ethical concerns related to consumers. In addition to these moral concerns, digital marketing creates doubts about its negative impact on human health. For this reason, understanding consumer behavior and providing products according to consumer needs has become indispensable for companies today. Digital marketing is everywhere in our daily lives and is also frequently used in the food industry. Through digital marketing, food industries promote the consumption of foods that are particularly high in energy and poor in nutrients. Consequently, individuals exposed to digital marketing in the food industry may be prone to obesity-related problems. This study analyzes the relationship between digital marketing, AI, and consumer behavior specifically on eating habits along with references from the literature.

Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.

Penerapan Strategi E-Commerce Untuk Meningkatkan Penjualan Pada Butik Rasyaf Collection Dengan Metode Market Basket Analysis

Rasyaf Collection Clothing Store offers a wide variety of women's clothing, including dresses, pants, accessories, shoes, jackets, and more. Currently, Rasyaf Collection Boutique's marketing system relies on social media platforms like Facebook, WhatsApp, and Instagram. However, the boutique owner has not yet implemented effective marketing strategies, making it difficult to reach customers, as they have to visit the boutique in person to purchase clothing, which limits the marketing reach. One of the challenges hindering the development of this business is the lack of product information available to potential customers, such as difficulties in identifying the types of products being sold and managing product inventory. The development of this system implements a prototyping model and is carried out in stages or linearly. It involves analyzing customer behavior from specific groups using market analysis methods. When specific patterns are unknown, market basket analysis is typically used as a starting point to gather information about transaction data. Data sources for market basket analysis include credit card transactions, discount coupons, lottery tickets, and customer complaint calls. The results of this research indicate that the developed e-commerce website application can assist the boutique owner in selling various types of clothing and managing product inventory. Additionally, this application will make it easier for customers to access information about the products being sold and allow them to choose clothing from Rasyaf Collection Boutique.

E-COMMERCE OPERATIONS WITH AI-POWERED DATA WAREHOUSES: A CASE STUDY ON CUSTOMER BEHAVIOR ANALYSIS

This paper presents a comprehensive systematic review of the impact of AI-powered data warehouses on e-commerce operations, with a particular focus on customer behavior analysis and operational efficiency. Leveraging the PRISMA methodology, the study synthesizes findings from 70 research articles spanning multiple countries, including the United States, China, India, the United Kingdom, and Germany. The review highlights the transformative role of AI-driven data warehouses in enabling real-time, predictive analytics, which significantly enhances the ability of e-commerce businesses to understand and respond to customer preferences, optimize inventory management, and implement dynamic pricing strategies. While the benefits of AI integration are substantial, the study also identifies persistent challenges, such as data privacy concerns, high implementation costs, and integration complexities, that may hinder widespread adoption. The paper concludes with recommendations for businesses to strategically approach the implementation of AI-powered data warehouses, emphasizing the need for scalable solutions, robust data governance, and ongoing investment in technology and training. This research underscores the potential of AI-powered data warehouses to drive innovation and growth in the e-commerce sector, while also calling for continued exploration of solutions to address the existing challenges.

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Due to the limitations of the hardware system, analysis of retail stores has caused problems such as excessive workload, incomplete analysis, slow analysis speed, difficult data collection, non-real-time data collection, passenger flow statistics, and density analysis. However, heatmaps are a viable solution to these problems and provide adaptable and effective analysis. In this paper, we propose to use the deep sequence tracking algorithm together with the YOLO object recognition algorithm to create heatmap visualizations. We will present key innovations of our customized YOLO-Deep SORT system to solve some fundamental problems in in-store customer behavior analysis. These innovations include our use of footpad targeting to make bounding boxes more precise and less noisy. Finally, we made a comprehensive evaluation and comparison to determine the success rate of our system and found that the success rate was higher than the systems we compared in the literature. The results show that our heatmap visualization enables accurate, timely, and detailed analysis.

Predictive Analysis of Customer Behaviour on Mobile Apps

Predictive Analysis of Customer Behaviour on Mobile Apps

Utilizing Logistic Regression for Analyzing Customer Behavior in an E-Retail Company

Utilizing Logistic Regression for Analyzing Customer Behavior in an E-Retail Company

-COMMERCE DATA ANALYSIS

In an increasingly digital and data-driven business landscape, e-commerce has become a critical domain for companies seeking to understand customer behaviour, enhance operational efficiency, and boost profitability. This project embarks on an exploration of e-commerce data analysis, focusing on its application in business optimization. In today's digital landscape, e-commerce platforms have revolutionized the way businesses operate and consumers shop. With the exponential growth of online transactions, the need to understand consumer behaviour and optimize business strategies has become paramount. This project aims to delve into the realm of e-commerce data analysis, leveraging diverse datasets to extract meaningful insights and drive informed decision-making. The study commences with data collection from multiple sources, including user interactions, purchase histories, demographics, and website traffic. Various analytical techniques such as descriptive analytics, predictive modelling, and machine learning algorithms are employed to extract valuable patterns, trends, and correlations from the amassed data. The findings from this analysis are crucial for ecommerce businesses to streamline operations, enhance customer satisfaction, and drive revenue growth. The project aims to contribute actionable insights that can be utilized by e-commerce stakeholders to make data driven decisions and stay ahead in a highly competitive market. The objectives of this study are to investigate and synthesize the existing literature on various facets of e-commerce data analysis, encompassing frameworks, data sources, metrics, customer behaviour analysis, personalization, recommendation systems, fraud detection, security measures, market basket analysis, cross-selling strategies, supply chain management, inventory control, user experience enhancement, A/B testing, and ethical considerations. By assessing these key areas, this project seeks to provide a comprehensive understanding of how data analysis techniques can be harnessed to improve e-commerce businesses' decision-making, customer satisfaction, and security. With the exponential growth of e-commerce platforms in recent years, there is a wealth of data available that offers valuable insights into consumer behaviour, market trends, and business performance. This study aims to conduct a thorough analysis of e-commerce data to uncover meaningful patterns and trends, providing businesses with actionable intelligence to enhance decision-making processes. The research methodology combines descriptive statistics, machine learning

Calibration‐Free Height Estimation for Person

AbstractDemand for use of indoor and outdoor surveillance cameras to prevent crime, analyze customer behavior in commercial facilities and other purposes has been increasing. For these types of applications, it is important to be able to estimate information that cannot be observed directly from the images, using the image data. One such type of information is the height of people appearing in the video. People's heights can be used, for example, to help identify a suspect in a crime investigation or as an attribute to understand customers better for marketing. However, many conventional technologies for estimating height from images have required special calibration, or the acquisition of other information in addition to the video. This paper proposes a method for estimating a person's height using only an image, without the need for any special calibration. The key points of the proposed method involve the following two processes. In the first process, a reference object of known size and the person are recognized and the person's height is estimated based on the relative sizes of the person and reference object in the image. The second process involves selecting frames from the video that are appropriate for use in the estimation process. In practical experiments using real video, the method was able to estimate people's heights with an average estimation error of 1.9%. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics

Improved DNN-assisted Customer Behavior Analysis with Smart Visual Analytics

Association rule mining algorithm implementation for e-commerce in the retail sector

The growth of online trading platforms and the development of market technology have forced businesses to take part in the analysis of client behavior. Therefore, this research aims to analyze customer behavior in the Kyrgyz Republic to enhance supplier's revenue, service quality, and customer satisfaction. This data was analyzed using the apriori algorithm. Results generated 118 rules which revealed strong connections between items and showed up to 61.06% relationship between the consumption of products, suggesting a connection among the considered items. Thus, the association rule highlights the significance of association rule mining in uncovering valuable insights within sales transaction data. These insights can inform targeted marketing efforts, inventory management, and the enhancement of customer experiences and optimize business strategies to meet customer preferences, ultimately fostering growth and competitiveness in the retail sector.

Utilization of Data Analytics to Enhance Operational Efficiency in Manufacturing Companies

In the digital era, manufacturing industries confront challenges like heightened global competition and intricate production processes, urging them to boost efficiency and productivity. Amidst these circumstances, Big Data emerges as a pivotal opportunity to enhance manufacturing performance. Big Data, characterized by vast volumes of data, utilizes advanced data mining to machine learning techniques for analysis. Data analytics, an interdisciplinary field, profoundly impacts manufacturing operations, enabling deeper insights into production processes. By analyzing production data, companies identify inefficiencies, streamline workflows, and enhance operational efficiency and productivity. Predictive maintenance through sensor data analysis prevents machine failures, while logistics data analysis optimizes supply chains and inventory management, reducing costs and enhancing competitiveness. However, implementing Big Data analytics presents challenges such as rapid data growth, diverse data sources, real-time insights, skill shortages, and data fragmentation. Overcoming these hurdles requires robust technology, skilled personnel, and effective data management strategies. Examples of Big Data analytics applications include customer behavior analysis by Amazon and Netflix, fraud detection in insurance, and urban mobility optimization. Success factors in data analytics implementation include effective data-driven communication, technology integration, and skill enhancement. In conclusion, implementing Big Data Analytics in manufacturing promises significant benefits in operational efficiency, product quality, and competitiveness. Overcoming challenges necessitates robust strategies and consideration of ethical and security issues, ensuring responsible data usage. With a deep understanding of Big Data Analytics, manufacturing companies can leverage this technology to achieve higher efficiency and competitiveness in the global market.

Research on the application of machine learning in business analytics: Cases of Amazon and eBay

With the rapid development of the Internet and the rise of e-commerce, commercial enterprises are faced with a large amount of data and a complex market environment. In this situation, machine learning, as a powerful tool, is widely used in the field of business analysis. In this dissertation, we take Amazon and eBay as examples to study the application of machine learning in the company's business analytics, focusing on its role in market prediction, customer behavior analysis and operation optimization. By analyzing the relevant cases, we find that machine learning technology plays an important role in helping companies make more accurate decisions and improve efficiency. Studying the application of Amazon machine learning in business analytics can promote in-depth research on the application of machine learning in business in academia, and promote the application and development of machine learning technology in other business scenarios. Overall, the application of machine learning in business analytics can help companies understand customer behavior, optimize operations, and improve sales results. However, there are still some challenges, such as data quality, algorithm selection and privacy protection. Therefore, further research and innovation are necessary to advance the development of machine learning applications in business analytics.

Artificial Intelligence in Marketing Communication: A Comprehensive Exploration of the Integration and Impact of AI

This study investigates the transformative impact of artificial intelligence (AI) on marketing communications through an evaluation research approach. Focused on enhancing personalization, efficiency, and strategic insight, the study explores AI’s applications in content creation, customer service, social media, influencer marketing, and predictive analytics. The results reveal a paradigm transition in customer engagement and behaviour analysis, highlighting AI’s role in providing profound insights and facilitating real-time interactions. Personalized marketing and targeted advertising have evolved, with AI analysing vast datasets to craft tailored messages, significantly enhancing communication relevance. AI’s impact extends to content creation and curation, accelerating processes through natural language generation and improving content personalization. Moreover, AI-driven chatbots redefine customer service, providing 24/7 personalized support and actively contributing to marketing strategies. Social media and influencer marketing benefit from AI’s optimization of content delivery, personalization, and campaign impact measurement. The synergy between AI and predictive analytics anticipates consumer behavior, enabling precise targeting and optimizing the customer journey. The study concludes with implications for businesses, advocating strategic AI integration for sustained growth, and emphasizes the necessity of staying attuned to emerging AI innovations in future research. This research serves as a roadmap, guiding businesses toward successfully navigating the digital era’s evolving marketing communications view.

Customer Segmentation in IT Sector using Datamining Techniques

Due to its large client base, the IT industry generates enormous amounts of data every day. Business experts and decision-makers stressed that keeping current clients is less expensive than acquiring new ones. Business analysts and customer relationship management (CRM) analysts must understand the causes of customer attrition as well as the patterns of behavior found in the data of these clients. This research is a comprehensive study about churn prediction in IT industry it also suggests a churn prediction model to identify consumer churn and provide the reasons behind customer churn in the IT industry by employing clustering and classification algorithms. Information gain and correlation attribute ranking filters are used in feature selection. One of the CRM's most important tasks is to create retention strategies that work to keep customers from leaving. The study intends to create a model that can effectively identify the primary reasons behind customer churn. This model is likely to use various techniques, such as analyzing customer behavior, preferences, or other relevant data, to identify patterns or characteristics associated with churn and segment the churning customers based on groups in order to retain the customers based on the specific characteristics or behaviors they share within their respective groups. The main aspect of the study is to identify the challenges in the existing methods that are used in churn prediction and suggest a model to improve churn prediction and elude churn effectively.

제주식재료를 이용한 돼지 불고기 양념소스개발 중요도 요인이 제품만족과 행동의도에 미치는 영향

This study attempted to present a proposal for the development of pork bulgogi seasoning products using Jeju ingredients by identifying the effect of important factors in product development for pork bulgogi seasoning development on product satisfaction and behavioral intention. As a result of the analysis, first, important factors in the development of marinade by consumers in Jeju for the development of bulgogi marinade were analyzed, and as a result, 16 out of 17 questions were grouped into three factors to name each factor as appearance, raw material, and essence. As a result of analyzing customer behavior factors of bulgogi seasoning, 8 out of 8 items were grouped into 3 factors, and each factor was named as product satisfaction, purchase intention, and recommendation intention. Second, as a result of examining the impact of important factors in the development of bulgogi seasoning on product satisfaction, purchase intention, and recommendation intention, only the essence affects customer behavior. This indicates that if the product is developed in consideration of the taste of the product, the production of hygienic products, and the convenience of use in the development of bulgogi seasoning products, customer behavior will increase.

Proposed Digital Marketing Strategy to Increase Customer Acquisition for Travel Agent Company

This study is a comprehensive examination designed to explore a number of complex challenges encountered by PT Selaras Citanusa (Selaras Wisata) in their endeavor to attract consumers organically, particularly within the context of the thriving Umrah tourism industry. The COVID-19 pandemic has posed significant difficulties for this enterprise. Efforts to increase market share and increase public awareness of brands on the market represent one of the greatest obstacles. This study presents a comprehensive qualitative methodology that combines several resilient analytic techniques. The 7Ps marketing mix method is used to describe the essential components of Selaras Wisata’s marketing strategy. Fishbone analysis is used to identify the fundamental causes of the business’s numerous problems. The SWOT analysis aides in evaluating the company’s environmental strengths, vulnerabilities, opportunities, and threats. In addition, content analysis is used to perceive and measure the impact of the company’s numerous marketing efforts. The study also includes a comprehensive examination of the external factors influencing Selaras Wisata. By employing Porter’s Five Forces analysis, the research investigates the industry’s structure and other external factors that may impact the company. Selaras Wisata uses customer behavior analysis to obtain a deeper understanding of customer preferences, requirements, and levels of satisfaction with the services provided. The data underlying this study are collected through in-depth interviews with PT Selaras’s internal team members. This information is then thoroughly analyzed using a variety of analytical instruments, such as SWOT analysis and Fishbone analysis. The author anticipate that the outcomes of this research will provide valuable contributions to Selaras Wisata’s efforts in crafting an effective digital marketing strategy to enhance customer engagement and ultimately improve public perception.

InvMetrics: Measuring Privacy Risks for Split Model–Based Customer Behavior Analysis

InvMetrics: Measuring Privacy Risks for Split Model–Based Customer Behavior Analysis