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.

Photographic Forewarnings

Urging avowed appreciation of peace and people-to-people ties among folk through the stark images compiled in a World War II photo album is the personal mission of one American man.Ashiver ran through Evan Kail at the sight of photos-in an album sent to him by a customer in 2022-of corpses,some scattered,others in piles,on a city street.The caption,“Nanjing Road”instantly evoked for him the infamous Nanjing Massacre of 1937-38,when the Japanese soldiers murdered 300,000 or more Chinese civilians and soldiers.Cognizant of the Japanese government’s obdurate denial of this atrocity,Kail took it upon himself to publicize and preserve these images by donating the album to China.

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.

Microfactory for filter bags developed in Sweden

After an intensive few years of development,ACG Kinna Automatic and ACG Nystrom–members of TMAS,the Swedish textile machinery association–have delivered the first microfactory for the production of fully finished filter bags to a major international filtration industry customer,in cooperation with JUKI Central Europe.

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.

Cyberattack Ramifications, The Hidden Cost of a Security Breach

In this in-depth exploration, I delve into the complex implications and costs of cybersecurity breaches. Venturing beyond just the immediate repercussions, the research unearths both the overt and concealed long-term consequences that businesses encounter. This study integrates findings from various research, including quantitative reports, drawing upon real-world incidents faced by both small and large enterprises. This investigation emphasizes the profound intangible costs, such as trade name devaluation and potential damage to brand reputation, which can persist long after the breach. By collating insights from industry experts and a myriad of research, the study provides a comprehensive perspective on the profound, multi-dimensional impacts of cybersecurity incidents. The overarching aim is to underscore the often-underestimated scope and depth of these breaches, emphasizing the entire timeline post-incident and the urgent need for fortified preventative and reactive measures in the digital domain.

An Enhanced Security System Using Blockchain Technology for Strong FMC Relationship

Blockchain technology is a shared database of logs of all consumer transactions which are registered on all machines on a network.Both transactions in the system are carried out by consensus processes and to preserve confidentiality all thefiles contained cannot be changed.Blockchain technology is the fundamental software behind digital currencies like Bitcoin,which is common in the marketplace.Cloud computing is a method of using a network of external machines to store,monitor,and process information,rather than using the local computer or a local personal computer.The software is currently facing multiple problems including lack of data protection,data instability,and reliability.This paper aims to give the highest security for multiple user environments in cloud for storing and accessing the data in blocks.The users who are legitimate are only allowed for storing and accessing the data as like a secured block chain approach.As like the Blockchain which does not require a centralized system for transactions,the proposed system is also independent on centralized network interface.The decentralized system is developed in such a way to avoid counterfeiting.The system enables the fabricator to spend less or null resources to perform the validations for its direct operated stores.This ensures the product fabricator to avoid the circulation of its duplicate products.The customer as an end-user is also ensured to have only the genuine products from the fabricator.The Fabricator(F),Merchant(M)and consumer(C)forms an interconnected triangular structure without allowing any counterfeiting agents in their secured cloud chain.The pro-posed approach provides the stability in the security system of the cloud using the chaining mechanism within different blocks at each node.It takes roughly 4.7,6.2,and 7.5 ms,respectively,to register each node in the proposed system for 5,10,and 15 nodes.The overall registration time for each scenario is 11.9,26.2,and 53.1 ms,despite the fact that each node’s registration time was greatest for 10 nodes.By looking at the data,it’s clear that the number of nodes is a func-tion of time.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

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.

A socioeconomic approach to the profile of microcredit holders from the Hispanic minority in the USA

The subject of this study is the microcredit market in the USA,more specifically in Florida.The justification for choosing this specific state is the massive presence of the Hispanic population.This will facilitate a generalization of the obtained results to the microcredit market in Latin American countries.Thus,the objective of this study is to analyze the profile of microcredit holders and their companies from socioeconomic and financial points of view.As our data also consider the degree of repayment of the microloans included in the sample,the clients’profile is related to the punctuality or default of their corresponding loan repayments using the methodology of multi-nomial logit regression.The variables used in this study refer to personal information concerning borrowers(gender,age,education level,and marital status),the economic situation of their respective companies(closeness to the lender,number of workers,and revenues),and the characteristics of granted loans(principal,term,and purpose).However,the results of the regression show that only two variables are significant at the 5%significance level:the borrower’s age,which has a positive effect on repay-ment punctuality,and the loan term,which exhibits a negative effect.The findings of this study have clear implications,as they can help lenders design suitable microloans adjusted to customer profiles.Finally,future research should include other demograph-ics and characteristics of affected companies.

Dynamic Behavior-Based Churn Forecasts in the Insurance Sector

In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).

A Big Data Based Dynamic Weight Approach for RFM Segmentation

Using the RFM(Recency,Frequency,Monetary value)model can provide valuable insights about customer clusterswhich is the core of customer relationship management.Due to accurate customer segment coming from dynamic weighted applications,in-depth targeted marketing may also use type of dynamic weight of R,F and M as factors.In this paper,we present our dynamic weighted RFM approach which is intended to improve the performance of customer segmentation by using the factors and variations to attain dynamic weights.Our dynamic weight approach is a kind of Custom method in essential which roots in the understanding of the data set.Firstly,Analytic Hierarchy Process is used to calculate the subjective weight,then the entropy method is applied to calculate the objective weight.Finally,we use comprehensive integration weighting method to combine the subjective and objective weight to obtain the final weight of the index to calculate the individual user value and quantify the user value difference.The experiment shows that the dynamic weight we used in RFM model is vital,affects the customer segmentation performance positively.Also,this study indicates that customer segments containing dynamic weighted RFM scores bring about stronger and more accurate association rules for the understanding of customer behavior.At last,we discuss the limitations of RFM analysis.

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

Customer retention is one of the challenging issues in different business sectors,and variousfirms utilize customer churn prediction(CCP)process to retain existing customers.Because of the direct impact on the company revenues,particularly in the telecommunication sector,firms are needed to design effective CCP models.The recent advances in machine learning(ML)and deep learning(DL)models enable researchers to introduce accurate CCP models in the telecom-munication sector.CCP can be considered as a classification problem,which aims to classify the customer into churners and non-churners.With this motivation,this article focuses on designing an arithmetic optimization algorithm(AOA)with stacked bidirectional long short-term memory(SBLSTM)model for CCP.The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry.Initially,the AOA-SBLSTM model per-forms pre-processing to transform the original data into a useful format.Besides,the SBLSTM model is employed to categorize data into churners and non-chur-ners.To improve the CCP outcomes of the SBLSTM model,an optimal hyper-parameter tuning process using AOA is developed.A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features.The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

Voice Response Questionnaire System for Speaker Recognition Using Biometric Authentication Interface

The use of voice to perform biometric authentication is an importanttechnological development,because it is a non-invasive identification methodand does not require special hardware,so it is less likely to arouse user disgust.This study tries to apply the voice recognition technology to the speech-driveninteractive voice response questionnaire system aiming to upgrade the traditionalspeech system to an intelligent voice response questionnaire network so that thenew device may offer enterprises more precise data for customer relationshipmanagement(CRM).The intelligence-type voice response gadget is becominga new mobile channel at the current time,with functions of the questionnaireto be built in for the convenience of collecting information on local preferencesthat can be used for localized promotion and publicity.Authors of this study propose a framework using voice recognition and intelligent analysis models to identify target customers through voice messages gathered in the voice response questionnaire system;that is,transforming the traditional speech system to anintelligent voice complex.The speaker recognition system discussed hereemploys volume as the acoustic feature in endpoint detection as the computationload is usually low in this method.To correct two types of errors found in the endpoint detection practice because of ambient noise,this study suggests ways toimprove the situation.First,to reach high accuracy,this study follows a dynamictime warping(DTW)based method to gain speaker identification.Second,it isdevoted to avoiding any errors in endpoint detection by filtering noise from voicesignals before getting recognition and deleting any test utterances that might negatively affect the results of recognition.It is hoped that by so doing the recognitionrate is improved.According to the experimental results,the method proposed inthis research has a high recognition rate,whether it is on personal-level or industrial-level computers,and can reach the practical application standard.Therefore,the voice management system in this research can be regarded as Virtual customerservice staff to use.

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.

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.

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.

Routine Supportive Supervision and Management of Medicines and Other Health Products and Technologies in Vihiga County, Kenya

Health Products and Technologies (HPTs) are pivotal for an efficient health system. Availability and accessibility to affordable health products are critical indicators towards achieving universal health coverage. Routine supportive supervision, performance monitoring, recognition of efforts and client feedback are vital activities toward health supply chain system strengthening. This is a descriptive paper that describes a model of integrated commodity supportive supervision, and mentorship and its impact on various outcomes of health commodity management. Data were abstracted from the standardized scored checklists used during integrated commodity supportive supervision and supply chain audit in public health facilities in Vihiga County. Scores for the period 2020 to 2022 were analyzed on the eight key areas of interest. The analysis was done using Statistical Package for Social Sciences (SPSS version 26). Results are interpreted at 95% Confidence interval. This paper also shares findings from both quantitative and qualitative data from client exit and facility managers’ interviews. Six complete rounds of supervisions, three clients and service providers’ interviews, and three annual award events have been conducted. We observed trends across six data collections points and compared the results at first point or baseline (January-June 2020) to the results at the last point or end line (April-June 2022). Findings show significant improvements on the eight parameters in terms of mean scores as follows: resolution of issues from previous visits by 35.06% (46.75% - 81.81%);storage of HPTs by 17.41% (68.72% - 86.13%);inventory management by 28.16% (42.67% - 70.83%);availability and use of commodity data management information systems (MIS) tools by 22.39% (74.40% - 96.79%);verification of commodity data by 25.61% (65.56% - 91.17%);availability of guidelines and job aids for commodity management by 46.28% (36.65% - 82.93%). There was an improvement on the mean score on accountability by 20.22% (58.58% - 83.51%). The composite (final) score improved by 28.33% (56.19% - 84.52%). There was progressive narrowing of the standard deviations on all the indicators across the study period. This demonstrates that there is standardization of practices and positive competition among all the public health facilities. There were significant improvements on all the eight indicators. Routine integrated commodity supportive supervision has proven to be an effective high impact intervention in improving management of health products and technologies in Vihiga County, Kenya.

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. .