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.

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.

The Impact of Information Technology on Service Quality,Satisfaction,and Customer Relationship Management(Case Study:IT Organization Individuals)

Recent research and studies have shown that Information Technology(IT)has a significant impact on service quality,customer satisfaction,and customer relationship development.With the proliferation and penetration of technology in all aspects of life,organizations are responding to the implications and opportunities that IT creates in relation to customer services.The main objective of using information technology in organizations is to increase customer satisfaction,service quality,and customer relationship management,which the authors will focus on here.Enhancing service quality,improving customer satisfaction,and establishing close and sustainable customer relationships are key advantages of leveraging information technology in this field.This article examines the impact of information technology on service quality,customer satisfaction,and customer relationship development and provides strategies and models for organizations to improve customer satisfaction and establish closer connections with them through the use of information technology.Seventy individuals from the IT field were used to evaluate the proposed model.The proposed model was compared with three models:SEM,regression,and decision tree,and the results demonstrated better performance of this approach.

Novel Early-Warning Model for Customer Churn of Credit Card Based on GSAIBAS-Cat Boost

As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.

Customer Segment Prediction on Retail Transactional Data Using K-Means and Markov Model

Retailing is a dynamic business domain where commodities and goods are sold in small quantities directly to the customers.It deals with the end user customers of a supply-chain network and therefore has to accommodate the needs and desires of a large group of customers over varied utilities.The volume and volatility of the business makes it one of the prospectivefields for analytical study and data modeling.This is also why customer segmentation drives a key role in multiple retail business decisions such as marketing budgeting,customer targeting,customized offers,value proposition etc.The segmentation could be on various aspects such as demographics,historic behavior or preferences based on the use cases.In this paper,historic retail transactional data is used to segment the custo-mers using K-Means clustering and the results are utilized to arrive at a transition matrix which is used to predict the cluster movements over the time period using Markov Model algorithm.This helps in calculating the futuristic value a segment or a customer brings to the business.Strategic marketing designs and budgeting can be implemented using these results.The study is specifically useful for large scale marketing in domains such as e-commerce,insurance or retailers to segment,profile and measure the customer lifecycle value over a short period of time.

Customer Churn Prediction Framework of Inclusive Finance Based on Blockchain Smart Contract

In view of the fact that the prediction effect of influential financial customer churn in the Internet of Things environment is difficult to achieve the expectation,at the smart contract level of the blockchain,a customer churn prediction framework based on situational awareness and integrating customer attributes,the impact of project hotspots on customer interests,and customer satisfaction with the project has been built.This framework introduces the background factors in the financial customer environment,and further discusses the relationship between customers,the background of customers and the characteristics of pre-lost customers.The improved Singular Value Decomposition(SVD)algorithm and the time decay function are used to optimize the search and analysis of the characteristics of pre-lost customers,and the key index combination is screened to obtain the data of potential lost customers.The framework will change with time according to the customer’s interest,adding the time factor to the customer churn prediction,and improving the dimensionality reduction and prediction generalization ability in feature selection.Logistic regression,naive Bayes and decision tree are used to establish a prediction model in the experiment,and it is compared with the financial customer churn prediction framework under situational awareness.The prediction results of the framework are evaluated from four aspects:accuracy,accuracy,recall rate and F-measure.The experimental results show that the context-aware customer churn prediction framework can be effectively applied to predict customer churn trends,so as to obtain potential customer data with high churn probability,and then these data can be transmitted to the company’s customer service department in time,so as to improve customer churn rate and customer loyalty through accurate service.

Effective Customer Review Analysis Using Combined Capsule Networks with Matrix Factorization Filtering

Nowadays,commercial transactions and customer reviews are part of human life and various business applications.The technologies create a great impact on online user reviews and activities,affecting the business process.Customer reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the business.The traditional systems consume maximum time and create complexity while analyzing a large volume of customer information.Therefore,in this work optimized recommendation system is developed for analyzing customer reviews with minimum complexity.Here,Amazon Product Kaggle dataset information is utilized for investigating the customer review.The collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and ratings.Then effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering approach.Then the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.

Research on the Value Positioning of University Library Based on Customer Delivered Value

With the rise of various new reading media and the rapid development of the digitization of paper resources,the traditional function positioning of university libraries based on information query is facing unprecedented challenges.How to deal with the change of social information?The library should be demand-oriented,re-examine its own value and find a new starting point.With the help of the concept of customer delivered value and based on 4P theory,this paper constructs the value chain of university library from the dimensions of product,image,personnel and service,and forms a multi-dimensional development positioning system.

Multi-Objective Cold Chain Path Optimization Based on Customer Satisfaction

To improve customer satisfaction of cold chain logistics of fresh agricultural goods enterprises and reduce the comprehensive distribution cost composed of fixed cost, transportation cost, cargo damage cost, refrigeration cost, and time penalty cost, a multi-objective path optimization model of fresh agricultural products distribution considering client satisfaction is constructed. The model is solved using an enhanced Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II), and differential evolution is incorporated to the evolution operator. The algorithm produced by the revised algorithm produces a better Pareto optimum solution set, efficiently balances the relationship between customer pleasure and cost, and serves as a reference for the long-term growth of organizations. .

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.

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.

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.

Customer-Value Creation in Self-service Technologies (SSTs) in the Tourism Industry

This paper explores and validates the process of customer value creation in self-service technologies(SSTs)in the context of the tourism industry.As the self-technology has been gradually advanced,its adoption in the tourism industry has brought many changes.A new trend of self-service technologies has helped service firms to save the labor costs and customers’waiting time for transactions.The purpose of this study is to confirm an applicability and a design of the original model of SSTs and explore the connection between SSTs and creation of value perception through a confirmatory factor analysis in the context of the tourism industry.Furthermore,the results of the online survey questionnaire from 234 responses in the United States and South Korea are explained in this study.The results of this study concluded that five statistically important factors are related to customers’motivations to use SSTs and enable customers to interact with SSTs as“SST location and capacity planning”,“SST service quality”,“motivations to use SST”,“SST design”,and“SST encounter”.These factors may imply an important meaning to better understand about customer value during the adoption of SSTs in the tourism industry.Tourism firms may use the results of this study to effectively enhance how customers perceive value about their products and services during the usage of SSTs.This will help tourism firms’efficiencies on the adoption of SSTs for their business plans and help them remain profitable in the competitive market.

Research on Improving Customer Value by Using QCC in Drugstores Based on Customer Life Cycle

Objective To build a model to improve the customer value of drugstores,so as to enhance their core competitiveness and share the value between drugstores and customers.Methods The quality control circle(QCC)was used to establish the model based on the theory of customer life cycle.According to recency-frequencymonetary(RFM)model,a general value index evaluation system was constructed for customers in different life cycles,and an example was studied.Results and Conclusion The flow model of drugstore customer management system and the method of evaluating the customer value were designed.Taking the activities of the QCC in a drugstore as an example,the deficiencies of pharmaceutical care such as medication consultation,shortage of drug supply and irrational drug display were improved.It also promoted the transformation of customers from a starting period into stable period and improved the comprehensive value of customers,indicating that QCC was effective.Drugstores should carry out the QCC activities with different themes according to the characteristics of customers in different life circles.Meanwhile,suggestions from customers on improving the environment and facilities,service quality and management mechanism of the drugstores should be effectively adopted to promote the transformation of customers from the starting period to the stable period for the realization of the highest value.This will bring economic value to drugstores and achieve the value sharing between drugstores and customers.