Analysis Of Customer Preferences Research Articles

Smart customer service in unmanned retail store enhanced by large language model

In unmanned retail store, providing smart customer service requires two stages: understanding customer needs, and guiding the customer to the product. In this paper, we propose an end-to-end (Customer-to-Shelf) software service framework for unmanned retail. The framework integrates visual recognition technology to detect retail objects, large language models to analyze customer shopping needs and make proper recommendations. First, deep neural network based image recognition models are studied for implementing effective stock keeping units (SKUs) object recognition on the shelf. Second, a novel method is proposed to fine-tune large language models (LLMs) with limited training dataset. Metaheuristic approaches are used to optimize the mask locations in a low dimensional parameter space, resulting a more efficient parameter updating method for limited downstream data. Third, by facilitating an automatic analysis of customer preferences powered by large language models, we present a smart recommender system based on domain-specific knowledge, which completes the Customer-to-Shelf software service framework. Experimental results show that our proposed fine-tuning method, is more efficient than other state-of-the-art training methods for limited downstream domain dataset. Using fine-tuned large models, we can successfully create a seamless shopping experience for customers by understanding personalized needs and providing shopping advice in the unmanned retail store.

Kano Model for Enhancing Business Strategy Through Customer Preference Analysis: A Case Study of Packaged Mineral Water Bottles

Kano Model for Enhancing Business Strategy Through Customer Preference Analysis: A Case Study of Packaged Mineral Water Bottles

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

The creation of a Popularity-based and Collaborative Filtering Based Restaurant Recommendation System is the main focus of this project, which is an important application in the fields of recommendation systems and machine learning. The system uses the different python libraries like scikit-learn, pandas, NumPy, Linear Regression, Hashlib and Matplotlib as well as technologies like Python and the Flask Framework, to recommend the restaurants to the users based on the ratings out of 5 and their tastes. With features including user registration, login, restaurant browsing, and recommendation, the suggested system is a user-centric program. The system offers a smooth user experience by combining backend Python frameworks with frontend technologies like HTML, CSS, and Bootstrap. A thorough analysis of the literature made it clear that recommendation systems are essential for improving user pleasure and experience across a range of industries. But my cutting-edge restaurant suggestion system offers a fresh approach. My solution combines cutting-edge machine learning algorithms with intelligent analysis of customer preferences to deliver customized restaurant recommendations. My system uses collaborative and popularity-based filtering algorithms to make sure that users receive recommendations based on their interests and preferences. Through the integration of data from many sources, such as restaurant attributes and user ratings, My system generates precise and pertinent recommendations that boost user engagement and pleasure. Furthermore, My platform offers a user-friendly experience with little additional infrastructure requirements by seamlessly integrating with current websites and applications. My goal is to revolutionize the restaurant discovery process and improve the eating experience for people globally by promoting the widespread adoption of My technology through smart relationships with industry players.

Customer preference analysis towards online shopping decisions based on optimized feature extraction

AbstractOnline shopping has become an essential part of modern life, with food delivery being one of the most popular services. In this study, we investigate the factors influencing customers' preferences in online food ordering, such as easy payment, customization, and fast delivery. Analysing customer ratings and reviews is crucial in ensuring the quality of food services and promoting a healthy lifestyle. However, the trustworthiness of online reviews remains a challenge. Based on their rating, new customers can get help from online shoppers for their preferences or demands. Data are collected from customer reviews through online platforms to improve food service quality in online shopping. This work aims to predict the preferences of customers in online shopping based on optimized feature extraction using Principal Component Analysis with a Social Spider Optimization (PCA‐SSO) algorithm. Despite existing algorithms for analysing customer preferences, most may have inaccurate predictions or take time to analyse data. To overcome these challenges, this model of PCA‐SSO is proposed to predict customers' more accurate online shopping preferences. This model provides valuable implications for online shopping owners regarding customer satisfaction. Compared to the accuracy rates of KNN (78.22%), LDA (83.16%), and PCA (88.56%) algorithms, our proposed algorithm of PCA‐SSO achieved a higher accuracy of 93.54%.

Dominance of global brands in the consumer shopping cart: analysis of customer preferences at the global level

The relevance of the study lies in the fact that global brands have significant penetration into the consumer shopping cart of buyers around the world. Due to increased competition with local brands, as well as between multinational companies themselves, the portfolio of key players in the FMCG segment includes hundreds or even thousands of different brands. The purpose of the study is to analyze consumer preferences for everyday goods on a global level, as well as to forecast the main trends in the future development of this market. In accordance with this goal, the following research objectives have been developed: to analyze which brands have the largest global consumer reach, to determine which companies own these brands, and what strategies these companies use (global and local branding strategies). The study also aims to determine in which countries consumers prefer global brands and in which countries they prefer local brands, and whether the development of discount supermarkets affects consumer choice. The study uses general scientific and special scientific methods, such as comparison, analysis and synthesis, generalization and inductive method, as well as scientific articles, reports of consulting agencies, and financial reports of companies. As a result, it was found that global brands occupy approximately one-third of the consumer goods market at the global level, but there are a number of regional and country differences. It is also proved that the own brands of discount supermarkets are of particular importance, which creates competition for international brands. The results can be used in the following scientific studies, as well as in the development of methodological recommendations for improving the competitive position of global brands, both globally and in individual regions and countries.

Customer Preference Analysis on Attributes of Hybrid Electric Vehicle: A Choice-based Conjoint Approach

Customer Preference Analysis on Attributes of Hybrid Electric Vehicle: A Choice-based Conjoint Approach

Customer Preference Analysis on Attributes of Hybrid Electric Vehicle: A Choice-based Conjoint Approach

Hybrid Electric Vehicle (HEV) have been introduced on the Indonesian market since the 2000s in an effort to minimize emissions from the country’s automotive industry. Using a conjoint analysis approach, the objective of this study was to determine the most preferred combination of HEV attributes. This study assessed whether a variety of attributes, such as (1) vehicle pricing, (2) driving range, (3) consumption level, (4) emission level, and (5) HEV policies, had the most influence on purchasing decisions. A conjoint analysis and a choice-based survey design were utilized to determine respondents’ preferences for HEV. The results revealed that price was the attribute that influenced respondents’ purchase decisions the most (39,7%), followed by policy (22%), emission (15,9%), consumption (13,7%), and driving range (8,7%) being the least preferred. Automotive companies will benefit from the findings of this study about customer preferences for HEV attributes. Lastly, the findings of this study can be used to research electric vehicles conducted around the world.

Analysis of Customer Preferences and Satisfaction toward Sharia Bank Services in Indonesia during the Covid-19 Pandemic

Islamic banks are one of the financial industries affected by the covid 19 pandemic. The inability to pay their obligations to Islamic banks and deposit withdrawals directly impacts the decline in customer business. This condition increases the risk of Islamic banks so that the ability of Islamic banks is needed to maintain customer loyalty. This study aims to analyze customer preferences and satisfaction with Islamic banking services during the pandemic.The data analyzed is primary data with 308 Islamic bank customers as respondents. Determination of the sample using the purposive sampling method with the criteria of the management of Islamic social organizations has been a customer since before the pandemic and made transactions during the pandemic. The data analysis uses the Importance Performance Analysis (IPA) method with the Carter approach (Compliance, Assurance, Responsiveness, Tangible, Empathy and Reliability).The results showed that compliance and assurance have a high level of performance with high preferences and satisfaction. Meanwhile, people's preferences are low, and satisfaction is low because their performance is also low in the tangible and reliability aspects. Another finding is that customers have high preferences but low satisfaction because their performance is typical responsiveness, and people have low preferences. Still, high satisfaction lies in the reliability and empathy aspects.

Development strategies for middle‐income countries in a digital world—Insights from modern trade economics

AbstractThe digitalisation of economic activities, combined with a secular slowdown in the growth of global output and trade, reduces the potential of traditional export‐oriented manufacturing as a development strategy. This paper takes a trade economics perspective to outline a possible response to the changed nature of industrialisation. The outlined response currently applies mainly to middle‐income countries but deserves attention also in longer‐term development strategies of low‐income countries. The paper argues that digital technologies affect trade costs through various mechanisms, which apply differently to manufactures and services and determine decisions of multi‐product firms on what to produce for what market. Emphasising big data analysis of customer preferences as an input to manufacturing, the paper finds that (i) industrialisation and service‐oriented development strategies are complements, rather than substitutes; (ii) access to data on customer preferences is an asset for developing countries; and (iii) data governance, which harnesses the increasing dependence of manufacturing on data, and innovation policies, which give greater importance to indigenous innovation, crucially augment the potential of industrialisation as a development strategy in a digital era.

Analysis of Customer Preferences and Customer Attitudes on Islamic Banks

The Purpose of this study is to evaluate the difference between customer preferences and customer attitudes of Islamic Bank in Ex-Banyumas residency of Central Java, Indonesia. It also evaluates the most considers attributes in choosing an Islamic bank. Accidental sampling is used in this study with a total of respondents as 100. To observe the difference in customer preferences, the scoring method and Chi-Square analysis are applied, meanwhile, Fishbein’s Attitude model is used to discover the customer attitudes. It is concluded that there is a significant difference in customer preferences on choosing Islamic banks on Ex-Banyumas residency, particularly when it is based on gender and long-period of customer status. It also revealed that customers’ decision in choosing Islamic banks is influenced by service quality. The findings of this study could give valuable input to Islamic bank management in the improvement in their services. This study is explaining the implementation of the Islamic principle in banking operational activities, as in fact the main reason for most customers to get services from the Islamic bank. This study is also enriched the Islamic banking studies in Indonesia.

Маркетинговое исследование потребительских предпочтений при выборе хлеба и хлебобулочных изделий

The article presents the results of marketing research aimed at identifying customer preferences when choosing bread and bakery products. Determined: the frequency of buying bakery products, depending on age, the number of baked goods purchased at one time, the place of purchase, analysis of customer preferences when choosing the type of bread, etc. The respondents» interest in frozen semi-finished products of a high degree of readiness was revealed.

Constructional Cyber Physical System: An Integrated Model

Artificial intelligence, machine learning, and deep learning have achieved great success in the fields of computer vision and natural language processing, and then extended to various fields, such as biology, chemistry, and civil engineering, including big data in the field of logistics. Therefore, many logistics companies move towards the integration of intelligent transportation systems. Only virtual and physical development can support the sustainable development of the logistics industry. This study aims to: 1.) collect timely information from the block chain, 2.) use deep learning to build a customer database so that sales staff in physical stores can grasp customer preferences, and 3.) integrate Generative Adversarial Network analysis and logistics truck delivery route analysis. This study will introduce new logistics technology development and innovative smart service structure, covering front-end Internet of Things sensing, mobile application apps, and back-end massive data analysis platform to promote the self/intelligence of logistics. Artificial intelligence for customer preference analysis is used, and images are automatically distributed through the system to reduce labor costs and increase sales. The proposed method is feasible, and it also achieves the push system of information transmission in transportation. Thus, logistics transportation cost transmission is reduced, thereby intelligently pushing self-promotion in marketing activities.

ANALYSIS OF CUSTOMER BANK BRI SYARIAH PREFERENCES OF CIAMIS BRANCH OFFICES ON FUNDING PRODUCT ATTRIBUTES

This study analyzes the product attributes of fund collection that are the customer preferences of Bank BRI Syariah Ciamis Branch Office and analyzes the attributes that are most considered by customers in using fund collection products. The basic method of this research is descriptive. The method of analysis of customer preferences is the chi-square analysis and to analyze the attributes that are most considered by customers is the Fishbein multi-attribute analysis. It is known that customer preferences for product attributes of raising funds are a proportion of larger profit-sharing ratios for customers, many ATM network facilities, friendly service, affordable public transport office locations. Product attributes of fund collection that are considered by customers in using BRI Syariah fund collection products at the Ciamis Branch Office are locations with attitude index values ​​(Ao).

Использование Big Data в международном бизнес

The term BigData causes a lot of controversy among specialists, many of whom note that it only means the volumes of accumulated data, but do not forget about the technical side, the considered direction includes technologies for computing, storage, and also service services. Big Data is a term that denotes technologies for processing large unstructured and structured data to obtain results that are understandable and useful to humans. In business, Big Data is used to support the adoption of transformations by a manager (for example, based on an analysis of financial indicators from an accounting system) or a marketer (for example, based on an analysis of customer preferences from social networks). By themselves, Big Data algorithms appeared with the introduction of the first mainframes (high–performance servers), which have the necessary resources for operational data processing and are suitable for computer calculations and subsequent data analysis. As the number of embedded computers rises due to falling processor prices and the ubiquity of the Internet, so too is the amount of data transferred and then processed (often in real time). Therefore, we can assume that the importance of cloud computing and the Internet of Things will increase in the coming years. It should be noted that Big Data processing technology boils down to three main areas that solve three types of tasks: (1) translation and storage of incoming information in gigabytes, terabytes, petabyte, etc. for their processing, storage and application in practice; (2) structuring of disparate content, namely: photos, texts, audio, video and all other types of data; (3) analysis of Big Data and the implementation of different methods of processing unstructured data, the creation of various analytical reports. In essence, the application of Big Data implies all areas of work with large volumes of the most disparate data, constantly updated and scattered across various sources. The goal is quite simple – the most efficient work, the introduction of new products and increased competitiveness. In this article, we will consider the features of solving the problems of using Big Data in international business.

Assessment of Knitted Fabric Smoothness and Softness Based on Paired Comparison

The purpose of this study is to conduct an in-depth analysis of customer preferences for single jersey knit fabrics regarding the attributes of smoothness and softness, in order to build up a holistic picture of purchase preferences. A paired comparison test was conducted for the subjective human assessment of single jersey knitted fabrics which were designed to differ in a single factor only. A sample of 140 evaluators were recruited and assigned into seven demographic groups according to age, gender and education level, with 20 evaluators in each group. It was shown that fabrics produced of viscose and polyester or with addition of elastane yarn are perceived smoother than 100 % cotton fabrics without elastane. Regarding the gender groups, the discrepancy is seen between the results obtained for smoothness and softness, especially within the male evaluators. Also, it was shown that evaluators with completed secondary education do not make higher differences in the perception of attributes, both smoothness and softness.

Dynamic customer preference analysis for product portfolio identification using sequential pattern mining

PurposeIn the initial stage of product design, product portfolio identification (PPI) aims to translate customer needs (CNs) into product specifications (PSs). This is an essential task, since understanding what customers really want is at the center of product design. However, design information is incomplete and design knowledge is minimal during this stage. Furthermore, PPI is often a confusing and frustrating task, especially when customer preferences are changing rapidly. To facilitate the task, the purpose of this paper is to capture the time-sensitive mapping relationship between CNs and PSs.Design/methodology/approachThis paper proposes a design sequential pattern mining model to uncover implicit but valuable knowledge from chronological transaction records. First, CNs and PSs from these records are transformed and connected according to the transaction time. Second, procedures such as litemset generation, data transformation and pattern mining are conducted based on the AprioriAll algorithm. Third, the uncovered patterns are modified and applied by engineers.FindingsUsing the retrieved patterns, engineers can keep up with the dynamics of customer preferences with regard to different PSs.Research limitations/implicationsComputational experiments on a case study of customization of desktop computers show that the proposed method is capable of extracting useful sequential patterns from a design database.Originality/valueConsidering the times tamps of the transactions, a sequential pattern mining-based method is proposed to extract valuable patterns. These patterns can help engineers identify market trends and the correlation among PSs.

Impact of Consumer Preferences on Food Chain Choice: An empirical study of consumers in Bratislava

The objective of this paper is to highlight the use of multiple criteria evaluation methods as a tool for the rating and selection of retail chains from the customers and suppliers perspective. We provide an assessment on the attractiveness of active retail chains on the Slovak market through multiple criteria methods used for the analysis of customer preferences. An analysis was conducted on a sample of consumers in Bratislava involving 11 389 respondents interviewed. The multi-attribute decision-making methods PROMETHEE II and V were used to assess the variants. In the first part of analysis the collected data uncover customers’ preferences in the selection of retail chains. Findings suggest a ranking of evaluated retail chains and thus of customer preferences. Based on the obtained evaluation, in the second part of analysis, a set of retail chains was chosen under constraints concerning the effectiveness of advertising, market share of sales and the maximum number of chosen retail chains and a binary linear programming model was formulated as an outcome. Proposed procedure aims to assist the decision maker in selecting which retail chain to choose for distribution of supplier’s products, and thus maximize benefits, which will result from consumer preferences and service satisfaction level in retail chain.

A rough set-based association rule approach for a recommendation system for online consumers

Increasing use of the Internet gives consumers an evolving medium for the purchase of products and services and this use means that the determinants for online consumers’ purchasing behaviors are more important. Recommendation systems are decision aids that analyze a customer's prior online purchasing behavior and current product information to find matches for the customer's preferences. Some studies have also shown that sellers can use specifically designed techniques to alter consumer behavior. This study proposes a rough set based association rule approach for customer preference analysis that is developed from analytic hierarchy process (AHP) ordinal data scale processing. The proposed analysis approach generates rough set attribute functions, association rules and their modification mechanism. It also determines patterns and rules for e-commerce platforms and product category recommendations and it determines possible behavioral changes for online consumers.

Contextual Sentiment Topic Model for Adaptive Social Emotion Classification

Social emotion classification is important for numerous applications, such as public opinion measurement, corporate reputation estimation, and customer preference analysis. However, topics that evoke a certain emotion in the general public are often context-sensitive, making it difficult to train a universal classifier for all collections. A multilabeled sentiment topic model, namely, the contextual sentiment topic model (CSTM), can be used for adaptive social emotion classification. The CSTM distinguishes context-independent topics from both a background theme, which characterizes nondiscriminative information, and a contextual theme, which characterizes context-dependent information across different collections. Experimental results demonstrated the effectiveness of this model for the adaptive classification of social emotions.

Demand Growth in Services: A Discrete Choice Analysis of Customer Preferences and Online Selling

ABSTRACTThe selling of perishable services (e.g., hotel rooms, airline seats, and rental cars) online is increasingly popular with both retailers and consumers. Among the innovative approaches to online sales is opaque selling. First popularized by Priceline.com's name‐your‐own‐price model, opaque selling hides some attributes of the service (notably, brand and specific location) until after the purchase decision, in exchange for a discounted price. This means that a branded “product” is being sold as somewhat of a commodity, but the brand “name” is protected by the opaque model. The attraction of this model for retailers is that they are presumably able to increase their revenue stream, albeit at a lower rate, by selling rooms that otherwise would remain in inventory. In this article, we outline the development and analysis of an online choice survey to understand consumer preferences among three types of online distribution channels: regular full information sales channels, and opaque sales channels with or without consumer bidding. A Multinomial Logit model is employed to analyze the data and measure the consumer trade‐offs between price and other attributes of the product. We use the estimated model to calculate the incremental demand and revenue created by using an opaque channel simultaneously with regular full information channels. On balance, we find that correctly priced opaque channels can add to hotels revenue streams without undue cannibalization of regular room sales.