Assessing the impact of artificial intelligence on customer performance: a quantitative study using partial least squares methodology

The purpose of this research is to examine the impact of artificial intelligence(AI)on customer performance and identify the factors contributing to its effectiveness by employing a quantitative approach,specifically the partial least squares method,to test the hypotheses and explore the relationships between various variables.The findings indicate that effective business practices and successful AI assimilation have a positive impact on customer performance.Additionally,the results of this study provide valuable insights for both academic and practical communities.This study highlights the importance of specific variables,such as organizational and customer agility,customer experience,customer relationship quality,and customer performance in AI assimilation.By exploring these variables,it contributes significantly to the academic,managerial,and social aspects of AI and its impact on customer performance.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

Investigating customer churn in banking:a machine learning approach and visualization app for data science and management

Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and,after that,end their connection with the bank.Therefore,customer retention is essential in today’s extremely competitive banking market.Additionally,having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele.These factors make reducing client attrition a crucial step that banks must pursue.In our research,we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers.We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics.In addition,we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis.Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.

The User Analysis of Amazon Using Artificial Intelligence at Customer Churn

Customer churns remains a key focus in this research, using artificial intelligence-based technique of machine learning. Research is based on the feature-based analysis four main features were used that are selected on the basis of our customer churn to deduct the meaning full analysis of the data set. Data-set is taken from the Kaggle that is about the fine food review having more than half a million records in it. This research remains on feature based analysis that is further concluded using confusion matrix. In this research we are using confusion matrix to conclude the customer churn results. Such specific analysis helps e-commerce business for real time growth in their specific products focusing more sales and to analyze which product is getting outage. Moreover, after applying the techniques, Support Vector Machine and K-Nearest Neighbour perform better than the random forest in this particular scenario. Using confusion matrix for obtaining the results three things are obtained that are precision, recall and accuracy. The result explains feature-based analysis on fine food reviews, Amazon at customer churn Support Vector Machine performed better as in overall comparison.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.

Dynamic Analysis of Customer Demand Based on Intuitionistic Fuzzy Number in Product Planning

To address the fuzziness and variability in determining customer demand importance,a dynamic analysis method based on intuitionistic fuzzy numbers is proposed.First,selected customers use intuitionistic fuzzy numbers to represent the importance of each demand.Then,the preference information is aggregated using customer weights and time period weights through the intuitionistic fuzzy ordered weighted average operator,yielding a dynamic vector of the subjective importance of the demand index.Finally,the feasibility of the proposed method is demonstrated through an application example of a vibrating sorting screen.

Role of Effective Leadership on Empowerment,Effective Communication,and Motivation in Customer Service

Customer service is the act of supporting customers of a business that can break a business or generate profit in the competitive environment with growing customers’expectations.Regarding the critical role of customer service to differentiate between businesses,it is considered as one of the top considerations in businesses.However,there is a range of key factors that are tied to the success of a customer service provider in offering quality customer service that will eventually lead the business through exceeding or falling short in responding to customers’expectations.Besides,businesses are prone to face some challenges in the employment of customer services that are required to be addressed.This paper aims to discuss the importance of customer service and the establishment of an efficient customer service strategy in business considering different influential factors that affect offering quality customer services.It also explains challenges that businesses may face in providing amazing customer services and how to address them.

Modeling customer satisfaction for bus rapid transit in Changzhou, China

This paper aims to develop a customer satisfaction model for bus rapid transit （BRT）. Both the socio-economic and travel characteristics of passengers were considered to be independent variables. Changzhou BRT was taken as an example and on which on-board surveys were conducted to collect data. Ordinal logistic regression （OLR） was used as the modeling approach. The general OLR-based procedure for modeling customer satisfaction is proposed and based on which the customer satisfaction model of Changzhou BRT is developed. Some important findings are concluded： Waiting sub-journey affects customer satisfaction the most, riding sub- journey comes second and arriving station sub-journey has relatively fewer effects. The availability of shelter and benches at stations imposes heavy influence on customer satisfaction. Passengers＇ socio-economic characteristics have heavy impact on customer satisfaction.

A model of targeted advertising with customer recognition

A two-period duopoly model is developed to examine the competitive effects of targeted advertising with customer recognition （TACR）. In the model, two competing firms sell goods to end consumers in the first period, during which customer recognition is obtained. In the second period, advertising can be targeted toward different consumer types. Advertising is assumed to be persuasive in the way that consumer valuation is increased. Equilibrium decisions and profits in each period are derived, showing that the firm who loses the current competition will win in the future. As a result, forward-looking firms price less aggressively so that their long-term profits can be enhanced with the help of TACR. Particularly, TACR improves profits through three important effects： valuation increasing, customer poaching, and anti-competition. Finally, this paper investigates the welfare issues, showing that firms enhance profits at the expense of consumer surplus. It is, therefore, suggested that public sectors take a step to protect consumers with the rapid development of targeting technology.

Research of CRM Based on Customer Intelligence Engine

The discipline of business intelligence addresses a broad range of functional activities from data mining and statistical analysis to predictive modeling and reporting, and customer intelligence is the actionable output from an intelligence eco-system. In order to focus enterprise's attention on their customers satisfaction in the customer relationship management and make CRM system run more efficiently, a new concept of customer intelligence engine(CIE) is proposed at first time in the paper, the architecture of CIE is structured, the trigger of CIE is defined and described, the CIE-based CRM eco-system is also discussed.

How does a data strategy enable customer value?The case of FinTechs and traditional banks under the open finance framework

The coupling of data and digital innovation opens the way for new business in the financial services sector,where customers are placed at the centre of decisions and data can help to develop customer knowledge.To carry out our research,we adopted a multi-case study approach to explore how a data strategy is developed in the retail banking industry,together with its relationship with customer value,paying particular attention to the heterogeneity between traditional banks and financial technology companies(FinTechs).Two main points emerged from the study.Firstly,there are three possible approaches to Open Finance,which are mainly defined by their different corporate cultures,organisational configurations,technological architecture and data value.Secondly,it is not enough to be a FinTech to be best placed to exploit the market,as some traditional banks share the FinTechs’approach to Open Finance.Designing new tailored products,customising their prices and offering them over the right channels through targeted communication are all data-driven initiatives that stem from cross-or up-selling potential,core to the retail banking industry for turning a customer into a cash flow,thus enabling value to be created for customers.Our findings additionally revealed that there is a form of external information asymmetry between the customer and the bank,and that there is also an internal asymmetry between bank departments,as their visibility on information about the same customer may differ.

Empirical Study of Brand-Customer Relationship Based on Customer Lock-In in Chinese Banking

The central construct of customer lock-in （CL） is measured and its role along with that of consumer loyalty in influencing the brand-customer relationship is tested. Using data collected from focus groups, a measurement model for CL is developed, and a structural equation model consisted based on literature review and our own theory is established. Moreover, the moderating effects of CL on the relationship between perceived value （PV） and brand relationship quality （BRQ） , as well as that between BRQ and brand loyalty （BL） based on data collected through a survey have been tested. Results indicate that consumer satisfaction is a critical factor in establishing brand-customer relationship, and the attitudinal brand loyalty is the key to obtain brand behavioral loyalty. Furthermore, CL tactics, such as decreasing consumers＇ learning cost and transactional cost facilitate the relationship building between customer and brand, while involuntary lock-in may have an adverse effect in the relationship building process. In addition, involuntary lock-in and loyalty program help in obtaining consumers＇ behavioral brand loyalty but not their attitudinal loyalty.

Key technologies of artificial intelligence in electric power customer service

As the demand for customer service continues to increase,more companies are attempting to apply artificial intelligence technology in the field of customer service,enabling intelligent customer service,reducing customer service pressure,and reducing operating costs.Currently,the existing intelligent customer service has a limited degree of intelligence and can only answer simple user questions,and complex user expressions are difficult to understand.To solve the problem of low accuracy of multi-round dialogue semantic understanding,this paper proposes a semantic understanding model based on the fusion of a convolutional neural network(CNN)and attention.The model builds an“intention-slot”joint model based on the“encoding–decoding”framework and uses hidden semantic information that combines intent recognition and slot filling,avoiding the problem of information loss in traditional isolated tasks,and achieving end-to-end semantic understanding.Additionally,an improved attention mechanism based on CNNs is introduced in the decoding process to reduce the interference of redundant information in the original text,thereby increasing the accuracy of semantic understanding.Finally,by applying the model to electric power intelligent customer service,we verified through an experimental comparison that the proposed fusion model improves the performance of intent recognition and slot filling and can improve the user experience of electric power intelligent customer services.

Customer Perception of Bus Services Based on the SERVQUAL Dimensions——A report of Wessex Red Bus service in UK

With the development of society, customers are having an increasing expectation on bus service. Problems such as bus being late all the time seem to become common. It usually annoys the customers and have a bad influence on bus companies.It's crucial to improve the bus service. This report adapts and applies a modified SERVQUAL approach to estimate the service quality in public transport. Wessex Red servcie, bus service in England UK, operated in partnership with the University of the West of England and the University of Bristol, is evaluated in the report being representative for bus service. In this study, the author has applied the SERVQUAL questionnaire among the three groups of customers in Bristol UK based on these five dimensions of SERVQUAL:"Tangibles, Responsiveness, Assurance, Reliability and Empathy"(Parasuraman, 1988). The results illustrates a high degree of importance placed on reliability, in which bus being late is an issue most concerned. The author analyzes the problem and finally provides suggestions and recommendation for the issue. This study is to provide a quality evaluation tool readily usable by transport operators willing to certify the service offered and it also offers a tool for practioners characterized by flexibility so as to fit individual needs. In addition, this study is beneficial to students who are learning Marketing. The study provides students a methodology in their marketing research. It also gives students a tool to evaluate services so as to set up their marketing plan and could give a better horizon to understand marketing.

Analysis of the Relationship Between Perceived Security and Customer Trust and Loyalty in Online Shopping

Advancements in the internet technology triggered a line of developments in the field of marketing. As an alternative to the conventional shopping, online shopping over the internet has gained substantial share of retail market. Customers get used to this new shopping venue and nowadays prefer it more and more according to the researchers, ten percent of the global population now uses internet for shopping. In this research, the author explored the relationship between the security measures implemented by a company, very active in the online shopping domain, and the customer trust and loyalty on the online services provided by this company. Findings of this research are based on survey data analyzed in SPSS. This research supports the existence of a relationship between the security of a company＇s website and customer trust and loyalty on the online services of this company When the perception of security measures improves, customer trust and loyalty increases accordingly

The effect of experiential value, perceived quality and customer satisfaction on customer lifetime value： An example using Star Cruises

The current study represents an attempt at experiential value, perceived quality and customer satisfaction influence on customer lifetime value. The sampling frame consisted of Star Cruises purchases in Taiwan. The authors have collected yield 268 questionnaires, by screening out 13 questionnaires, including those missing value or incomplete answer, and finally a usable sample of 255 questionnaires were utilized in this study. The results show that experiential value, perceived quality and customer satisfaction have positive effects to customer lifetime value. Especially, experiential value has the strongest direct effect. Therefore, if resources are limited, the manager should focus on shaping the experiential value to increase customer lifetime value. Implications for marketing theory and practitioners are discussed, and possible directions for future research are sketched.

Evaluation of the Websites of Logistics Operators by Customers

In this article, the results were presented of research concerning the visual and functional aspects of the websites of three largest logistics operators in Poland. The research was carried out with the participation of the following three groups： institutional customers, individual customers and marketing management students. The article covers an attempt to evaluate cooperation through virtual contacts with the company. The analysis also concerned such issues as information on corporate social responsibility （CSR）, information on job offers and the position of the company on the market.

Correlating Customer Satisfaction and Customer Loyalty:The Case of International Tourists in Cretan Resort Hotels

Customer satisfaction and customer loyalty are considered as a confirmation of remarkable service provision towards the customers.The formation of both constructs is viewed as a vital element which leads to the reinforcement of competitive advantage for the hotels.Various academic viewpoints are available concerning the constructs of customer satisfaction and loyalty;nevertheless,most of them indicate an opposition towards their correlation.The aim of this research was to investigate the factors that reinforce the satisfaction and loyalty levels of resort tourists and identify whether or not the two constructs are interrelated.A quantitative research methodology was applied,with the use of close-ended paper questionnaires,which were administered to international tourists staying in resort hotels in the Municipality of Hersonissos,one of the most popular tourist destinations in northern Crete.Findings confirmed a positive correlation between customer satisfaction and customer loyalty.Data also showed that food and beverage quality,location and price are considered as the most significant factors that enhance satisfaction levels,whereas emotional experiences,loyalty programs,customer service quality,and customer satisfaction were identified to have a significant impact on the formation of customer loyalty.

Does Customer Experience Management Impact Customer Loyalty Shopping at Supermarket？ The Case in the Mekong Delta, Vietnam

This research analyzes and answers the question ＂Does customer experience management impact to customer loyalty shopping at supermarket, in the case of Mekong Delta, Vietnam？＂, 321 customers of supermarkets in the Mekong Delta were interviewed by questionnaire. The descriptive statistics, factor analysis, Cronbach＇s alpha, structural equation modeling, and multi-group structural analysis methods were used in this study. The results confirm that customer experience management impacts to customer loyalty shopping at supermarkets. There are six components of customer experience management including brand name, space, commodity, staff, parking lot, and the presence of other stores in supermarkets. In particular, the commodity composition has the strongest impact on customer experience management and satisfaction and directly influences on customer loyalty. In addition, elements of customer experience management affect customer loyalty indirectly through sensory experience, social experience, and satisfaction. The results of multivariate analysis show that age group, spending per shopping at supermarkets, and marital status of the client do not change the relationships in the research model. Meanwhile, the relationship of the different models of the client group income, gender, education, occupation and customers is different. This study adds value to consumer behavior and customer experience management literature.

Influence of Customer Relationship Marketing and Satisfaction of Customer Loyalty （Case Study： In Bank CIMB Niaga Lembong in Bandung）

The purpose of this study was to examine the theoretical basis in an effort to build the competitiveness of the banking services industry, particularly in terms of customer satisfaction and loyalty. This type of research uses explanatory research. The sampling method used a non-probability sampling, used purposive sampling technique that has become a customer for two years, used questionnaires to 100 respondents in Bandung bank customers. Measurement validity uses confirmatory factor analysis, while test reliability is calculated by Cronbach Alpha method. Analysis to test the hypothesis uses the path analysis. The findings illustrate a clear pattern of the dimensions of customer relationship marketing, satisfaction effect built, and several important findings including empirical verification mediating role overall customer satisfaction in the formation of loyalty attributes.