Current Customers Research Articles

Customer Churn Prevention For E-commerce Platforms using Machine Learning-based Business Intelligence

Aims & Background: Businesses in the E-commerce sector, especially those in the business- to-consumer segment, are engaged in fierce competition for survival, trying to gain access to their rivals' client bases while keeping current customers from defecting. The cost of acquiring new customers is rising as more competitors join the market with significant upfront expenditures and cutting-edge penetration strategies, making client retention essential for these organizations. Objective: The main objective of this research is to detect probable churning customers and prevent churn with temporary retention measures. It's also essential to understand why the customer decided to go away to apply customized win-back strategies. Methodology: Predictive analysis uses the hybrid classification approach to address the regression and classification issues. The process for forecasting E-commerce customer attrition based on support vector machines is presented in this paper, along with a hybrid recommendation strategy for targeted retention initiatives. You may prevent future customer churn by suggesting reasonable offers or services. Results: The empirical findings demonstrate a considerable increase in the coverage ratio, hit ratio, lift degree, precision rate, and other metrics using the integrated forecasting model. Conclusion: To effectively identify separate groups of lost customers and create a customer churn retention strategy, categorize the various lost customer types using the RFM principle.

Adaptive stochastic lookahead policies for dynamic multi-period purchasing and inventory routing

We explore a problem faced by agri-food e-commerce platforms in purchasing different, perishable products and collecting them from multiple producers and delivering them to a single warehouse, aiming to maintain adequate inventory levels to meet current and future customer demand, while avoiding waste. Customer demand and suppliers’ purchase prices and supply volumes are uncertain and revealed on a periodical basis. Every period, purchasing, inventory, and routing decisions are made to satisfy demand and to build inventory for future periods. For effective decisions integrating all three decision components and anticipating future developments, we propose a stochastic lookahead method that, in every period, samples future scenarios for demand, supply volumes, and prices. It then solves a two-stage stochastic program to obtain the decision for the current period. To make this approach computationally tractable, we reduce the routing decision in the two-stage program and use an approximate routing cost instead. Given the reduced decision, we then create the final decision via a conventional routing heuristic. We learn the routing cost approximation adaptively via repeated training simulations. In comprehensive experiments, we show that all three components, stochastic lookahead, routing cost approximation, and adaptive learning, are very effective individually, but especially in combination. We also provide a comprehensive analysis of the problem parameters and obtain valuable insights in problem and methodology.

The Effect of Co-creation on Competitive Advantage in Processing and Service Companies in the Littoral Region of Cameroon

Aim: The current customer is more informed and educated, more selective and has a higher capacity of choice hence demands a more significant value generation from firms. The co-creation remains a vehicle for competitiveness and business growth but without co-creation, business tends to stagnate and eventually fail. This study aims to examine the effect of co-creation on competitive advantage in the processing and service firms of the Littoral Region in Cameroon. Methods: The research adapts a correlational research design with a sample size of 500 companies from the processing and service sectors. The research applied a deductive approach. Data collection was through the use of a questionnaire and data analysis using the pairwise correlation analyses. Results: The results revealed a strong correlation structure between the items as the KMO measure of sampling adequacy was supportive and significant as well as Bartlett’s test of sphericity. Conclusion: The study reaffirms the positive significant effect of co-creation on the competitive advantage of processing and service firms in the Littoral Region of Cameroon. Recommendation: Management needs to adapt co-creation with customers to improve their products and services and to engage further with customers for innovation, customization and collaboration.

Electric Vehicle Adoption in India: Assessing Current Status and Customer Perceptions

Electric Vehicle Adoption in India: Assessing Current Status and Customer Perceptions

Study on Crm Practices of Organized and Unorganized Jewellers

Gem and jewellery manufacture is a worldwide business nowadays, with polishing and jewellery manufacturing taking place in Belgium, the Netherlands, Israel, China, and Turkey, and selling all over the world. Gold, diamonds, and platinum are mined in Africa, Russia, Canada, and Australia. Over 15% of our overall exports come from this business, which also employs 1.3 million people. Its contribution to our Gross Domestic Product (GDP) is 3.75%, surpassed only by exports connected to Information Technology (IT). About 80% of the market is made up of gold jewellery; the remaining 20% is made up of jewellery with diamond and gemstone settings. Over 57% of the world's raw diamonds by value are handled by India, which is the largest diamond processing (cutting and polishing) hub in the world. Around 80% of jewellery sales are made up of gold pieces, with the remaining 20% consisting of jewellery with settings for diamonds and other gemstones. Customer relationship management (CRM) is a group of linked, data-driven software applications that help your company organize, track, and store information about its current and potential customers. Because this data is kept in a single system, business teams may instantly obtain the insights they want. Customer relationship management (CRM) is the process by which a business or other organization maintains its ties with customers. It often entails the use of data analysis to look through enormous amounts of information.

Sampling-based novel heterogeneous multi-layer stacking ensemble method for telecom customer churn prediction

In recent times, customer churn has become one of the most significant issues in business-oriented sectors with telecommunication being no exception. Maintaining current customers is particularly valuable due to the high degree of rivalry among telecommunication companies and the costs of acquiring new ones. The early prediction of churned customers may help telecommunication companies to identify the causes of churn and design industrial tactics to address or mitigate the churn problem. Controlling customer churn by developing efficient and reliable customer churn prediction (CCP) solutions is essential to achieving this objective. Findings from existing CCP studies have shown that numerous methods, such as rule-based and machine-learning (ML) mechanisms, have been devised to solve the CCP problem. Nonetheless, the problems of adaptability and the resilience of rule-based CCP solutions are its major weaknesses, and the skewed pattern of churn datasets (class imbalance) is detrimental to the prediction performances of conventional ML models in CCP. Hence, this research developed a robust heterogeneous multi-layer stacking ensemble method (HMSE) for effective CCP. Specifically, in the HMSE method, the prediction prowess of five ML classifiers (Random Forest (RF), Bayesian network (BN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Repeated Incremental Pruning to Produce Error Reduction (RIPPER)) with distinct computational characteristics are ensembled based on stacking and the resulting model is further enhanced using a forest penalizing attribute (FPA) model. The synthetic minority oversampling technique (SMOTE) is integrated with the proposed HMSE to balance the skewed class label present in the original experimental datasets. Extensive tests were carried out to determine the effectiveness of the proposed HMSE and S-HMSE on standard telecom CCP datasets. Observed findings from the experimental results showed that HMSE and S-HMSE can effectively predict churners even with the class imbalance (skewed datasets) problem. In addition, comparison studies demonstrated that the suggested S-HMSE offered improved prediction performance and optimum solutions for CCP in the telecom sector in comparison with baseline classifiers, homogeneous ensemble methods, and current CCP approaches.

Pengaruh Kualitas Pelayanan Harga dan Lokasi Terhadap Kepuasan Pelanggan Pada Penginapan Wisma Jampea Makassar

Abstract The Impact of Service Quality, Price and Location on Customer Satisfaction at Wisma Jampea Makassar Lodging This research rigorously aimed to investigate the current customer satisfaction at Wisma Jampea customers. The variables used consisted of service quality (X1), price (X2), location (X3), and Customer Satisfaction (Y). Further, this study included 53 respondents as samples and conducted quantitative research using primary data. Thus, this research collected the data from questionnaires. The population of this study involved 53 Visitors then performed survey method as sampling techniques. Afterwards, this research applied analytical methods including validity tests, data reliability tests, normality tests, multicollinearity tests, heteroskedasticity tests, multiple linear regression analysis, simultaneous influence tests (F tests), partial influence tests (t) and R2 simultaneous determination tests. Therefore, the results showed that: (1) Service Quality indicated a positive influence on Customer Satisfaction at Wisma Jampea Makassar Lodging; (2) price reported a non-positive effect on Customer Satisfaction at Wisma Jampea Makassar Lodging; (3) location found a positive impact on Customer Satisfaction at Wisma Jampea Makassar Lodging. Keywords: Service Quality, Price, Location and Customer Satisfaction

Pengaruh Word of Mouth Promotion dan Service Excellent terhadap Keputusan Nasabah Memilih Giro BTN di Bank BTN Kantor Cabang Bandung Timur

Based on the observation results, that the background of this research is that there has been a decrease in the number of current account customers in the last two years, this is due to poor word of mouth promotion and also service excellent that does not meet customer expectations which can affect customer confidence in taking decision. This study aims to determine the effect of word of mouth promotion and excellent service on customers' decisions to choose current account products partially or simultaneously at bank BTN Kantor Cabang Bandung Timur. The object of research in this thesis is BTN Giro customers at bank BTN Kantor Cabang Bandung Timur. The type of research is associative quantitative with primary data types and uses data collection techniques in the form of interviews, lists of statements (questionnaire), observation and literature study. While the sampling technique is non-probability sampling with purposive sampling. The population is all Current Account customers at the bank BTN Kantor Cabang Bandung Timur, totaling 838 people with a sample of 89 people. The results showed that there was a significant effect of word of mouth promotion on the decisions of BTN Giro customers at the BTN bank East Bandung Branch Office of 33.7% where the value of tcount>ttable (6.646>1.662), there was a significant influence of service excellent on customer decisions BTN demand deposits amounted to 40.3% where the value of thitun> ttable (7.656> 1.662), and simultaneously there is a significant influence between word of mouth promotion and service excellent on customer decisions choosing current account products at bank BTN East Bandung Branch Office of 52% with F test results Fcount <Ftable (46.62).

Post-Merger Commercial Bank Performance Trends: A Case of Kenya

Commercial banks face performance challenges since most of them react to these challenges in a fairly standardized manner. This is because most of the commercial banks offer similar products and services. In so doing, they face high competitions and as a result, they engage activities in pursuit of a competitive edge in order to keep their current customers and attract new ones. Some of the strategic activities these companies have engaged in the recent past have been Mergers and acquisitions (M&A). M&A have become an effective strategic tool to consolidate the Banks and Financial Institutions (BFIs) in Kenya to increase their capital base, expand their business, and bring financial stability. However, despite venturing into mergers and acquisitions, evidence from elsewhere indicates that financial performance stability and improvement still remains a challenge forming a good basis for further empirical investigation. This paper provides an assessment of the post-merger commercial bank performance in Kenya over the period 2008 to 2019 using return on equity as a proxy for bank performance. The target population for this study comprised all 13 commercial banks operating in Kenya between the year 2008 and the year 2019. The study used purposive sampling to select thirteen (13) commercial banks that had undergone mergers and acquisitions in Kenya over eleven years (from 2008 to the year 2019). The study finds that the post-merger effect of mergers and acquisitions on financial performance is mixed. Some commercial banks reported improved ROEs while a few reported declining ROEs during the study period. To enhance performance, the study recommended that commercial banks should prioritize M&A opportunities that align with their long-term strategic goals. This might include expanding into new geographic regions, entering new markets, diversifying product offerings, or gaining access to new technologies. Banks should assess the potential risks associated with the M&A transaction, including credit risk, operational risk, and reputational risk. Develop strategies for mitigating these risks and ensuring a smooth transition. Keyword: Mergers, Strategy, Acquisitions, Performance, Return on Equity, Synergies, profitability.

Optimizing BSI Mobile Banking Services to Increase Satisfaction with Transaction Services for IAIN Madura Sharia Banking Study Program Students With a SWOT Analysis Approach

This research is presented to contribute to what business strategies Bank Syariah Indonesia can optimize in optimizing BSI mobile services for transaction satisfaction using a SWOT analysis approach. Data analysis was carried out by exploring data through observation, interviews and documentation, which was followed by a data reduction process, testing data validity and drawing conclusions. Based on the results of the SWOT analysis, researchers found that BSI mobile banking services are currently at coordinates 0.70; 0.51, where these coordinates are in quadrant 1, namely an aggressive strategy. This strategy shows the situation that BSI bank can optimize BSI mobile banking services in increasing satisfaction by using all their strengths to take advantage of existing opportunities. One of these strategies can be in the form of (1) Improving the quality of products and service features (2) Maintaining the satisfaction of existing customers by improving the quality of transactions that are safe, precise and fast, (3) Increasing the security protection of customer data, (4) Evaluating feedback from past and current customers in designing future market share, (5) Strengthening promotional tools in introducing the benefits of BSI Mobile Banking service features, and (6) Reaching potential new customers in expanding market expansion .

How Brazilian industries innovate

The survival of industries in the market involves constantly searching for means to face the challenges of overcoming competitors, retaining current customers and acquiring new ones, optimizing resources, and acting strategically. It is known that to achieve these purposes, they must incentivize innovation by collaborating with external agents such as knowledge institutions, public Institutions for innovation support, startups, and even traditional competitors. This approach, referred to as open innovation, occurs more intensely in industries present in developed countries. In Brazil, open innovation is primarily used by large national or foreign companies, and businesses and public institutions make efforts to disseminate the innovation culture. This work examines how small and large Brazilian industries have been investing in research and development, how they are structured to carry out these processes, and what entrepreneurs have perceived concerning the benefits and difficulties of innovating. To this end, this study evaluated the data from the last three editions of the Survey of Innovation (PINTEC), elaborated by the Brazilian Institute of Geography and Statistics (IBGE). It could be concluded that Brazil needs to make additional efforts to build an effective environment for innovative industries. Brazilian companies remain concerned with short-term survival; most internal efforts are to develop new products, have low interaction with universities, have little knowledge of innovation support mechanisms, and, in general, are highly susceptible to external economic factors.

Predicting Quality of Modified Product Attributes to Achieve Customer Satisfaction

In the era of the competitive environment, the improvement in current products is ensured through activities aimed at increasing a product’s quality level and, consequently, reducing the amount of waste. The dynamically changing production environment and sudden changes in customer expectations force us to take precise and well-thought-out development steps. Furthermore, it is important to anticipate favourable product changes to prepare for market changes over time. This is still an open problem. The aim of this study was to develop a method to predict the quality of potential product prototypes resulting from the proposed modifications of the product features. This methodology takes into account current customer expectations. The method was created based on the principles of creating Quality Function Deployment (QFD) in the context of taking into account current and future customer expectations regarding product features. This is a new approach to analysing product quality within the principles of the traditional QFD method. The originality of the study is the technique used in the method to estimate the expected values of product features and their importance (weights), taking into account current customer expectations. Its originality is also manifested in drawing conclusions supporting the decision-making process of product improvement, because it involves ensuring the pro-quality modification of selected features of current products in the order that is most advantageous from the customer’s point of view. The use of the proposed method allows for the analysis of the impact of modifying the current value of a product feature. The method is illustrated with an example of a vacuum cleaner for home use. However, the proposed method can be applied to the design of any product to predict products that will meet customer expectations.

Customer Experience Measurement: a Critical Review of Literature

Objective: This research aims to explore the current customer experience measurement metrices to find out merit and pitfalls in them based on a critical review of existing literature. Methods: We have used Pare et tal (2015) six step framework for a structured and critical review of the current consumer measurement indices.145 Journal articles and 10 books related to the subject were reviewed and analysed during the process. Result: Significant findings include lack of association between business strategy and customer experience measurement, Data in silo format, Interdepartmental collaboration missing. The Twelve widely used metrices have merits as well as significant pitfalls. Comapnies and firm need to revisit and redefine their customer experience measurement índices.

Application of Machine Learning to Customer Churn Risk Prediction

Accompanied by technological globalization and upswing of telecommunication industry in the 21st century, the number of operators is springing up like mushrooms after rain in the market and that intensifies the industry competition environment due to the unprecedented growth trend and the challenges. As a research hotspot in the field of business analysis, prediction of customer attrition risk possesses some extensive range pertaining to applications within global marketing, telecommunications and other fields. Due to the complex relationship between customer information and the products used, it is difficult for merchants to conduct effective risk assessments. Nevertheless, customer churn risk prediction has made such rapid progress over the past few years with the development within computer science and machine learning so that the researchers would be able to establish more potential connections and achieve more accurate predictions about customer churn. This article summarizes customer churn risk prediction in the order of time and technology iteration, and mainly introduces 9 classic prediction methods based on machine learning. Additionally, four relevant performance measurement metrics and one data set are explored. Eventually, in view of the problems existing in the current customer churn risk prediction methods, prospects for future research are proposed.

A Study on Customer Satisfaction in Homefirst Finance

This research work titled “A study on Customer Satisfaction in HomeFirst Finance”, was done with the main objective to study on Factors influencing the Customer Satisfaction and to know the customer needs and preferences inorder to assess the customer satisfaction levels. To provide the suggestion for the organization in Customer Satisfaction at HomeFirst Finance. It is descriptive research design. The Primary data has been used to collect data with the help of Questionnaire. The sample size is 120. Tools used in this study is Correlation and Chi square. The major findings are the majority 75% of the respondents are the current customers of the HomeFirst Finance company. The majority 52.5% of the customers are very satisfied with the company services. The suggestions for the betterment of the company is provide the customer feedback collection to create new customer and to retain the existing customers from the competitors. Providing a positive experience for customers can lead to long-term relationships and increased business success for lenders in the competitive home loan market. Create a supportive and inclusive workplace culture where everyone feels valued. Keywords: Factors Influencing customer satisfaction, customer loyalty, retention, growth opportunities, working environment, customer service.

Marketing for Small Business Escape Rooms and the Importance of Experiential Learning

A semi-rural Ohio escape room collaborated with a local university for an experiential learning initiative, aiming to attract more college students, retain current customers, and enhance marketing strategies. Two questionnaires were employed: one targeting college students and the other existing customers. Findings revealed a higher attendance of upperclassmen students at the escape room and email as the preferred mode of communication. Moreover, a significant correlation between escape room experience, games, food, drinks, and overall satisfaction was observed. The customer-focused survey indicated the primary demographic as individuals aged eighteen to twenty-nine, with a high satisfaction rate of ninety-seven and a half percent. Additionally, sixty-three percent expressed interest in returning for a new escape room design.

Unlocking B2B Purchase Engagement: Investigating Its Drivers and Consequences in App-Based Service Subscriptions for MSMEs

Objective: This study aims to highlight the role of purchase engagement in the business-to-business (B2B) context, which is mainly under-explored due to the intricacy and diverseness of stakeholders embroiled in B2B purchasing decisions. This paper fills the gap by studying purchase engagement in the Micro, Small, and Medium Enterprises (MSMEs) B2B setting, particularly for app-based service subscriptions. Design/Methods/Approach: The study recruited responses from 215 MSME owners and managers, current customers of a subscription-based point-of-sales (POS) service. The PLS-SEM technique empirically validated the conceptual framework and the research hypotheses. Findings: The findings indicate that the conceptual model of customization, identification with virtual communities, and loyalty to account managers are exogenous factors influencing purchase engagement, perceived dependency, and willingness to pay for price premiums. Originality/Value: This study aims to understand purchase engagement in B2B, focusing on transactional drivers. This framework also investigates a particular set of drivers and results from purchase engagement factors in B2B app-based service subscriptions. Practical/Policy implication: B2B app-based service subscription stakeholders must ensure that all efforts to engage with customers strengthen their dependency on the service by starting with purchase engagement, customization, identification with virtual communities, and loyalty to account managers.

A dynamic, relational approach to B2B customer experience: A customer-centric perspective from a longitudinal investigation

Understanding the formation and dynamics of B2B customer experience (CX) is a key priority for marketing academics, with a notable gap necessitating empirical investigation. To address this gap, two studies were conducted. The first utilizes a mixed-method approach to generate and empirically assess a CX measure, with a specific focus on impressions during the service delivery stage from a relational perspective. The second using longitudinal data explored the impact of past impressions and specific supplier offerings on current customer impressions. The authors identified four types of impressions: two cognitive (factual and sagacious) and two affective (emotional and social) and highlighted that certain aspects of past impressions negatively impact the present. The paper further elucidates how the technical and functional components of the supplier's offering shape customer impressions, confirming the functional elements' impact on the affective impressions of the customer’s perceived CX and influencing the perceived relationship quality.

Enhancing Customer Analytics: A Comprehensive Framework for Effective Churn Prediction

This paper presents a comprehensive framework designed to forecast churn rates within organizations and provide visual representations for proactive and reactive analyses. Churning is the process of customer discontinuation from a product or service due to dissatisfaction or shifting needs of customers. Since keeping current clients comes with a considerably cheaper cost, acquiring new ones is no longer a smart business plan. By leveraging historical customer data encompassing demographic, behavioral, and transactional attributes, predictive models are developed and evaluated. Given that we already have all of the current customer's data, retention is far more affordable and successful. This paper aims at improving the accuracy and gives a churn prediction model which is suitable for all organizations. There are Five main components in this framework: Data preprocessing, pattern recognition, exploratory data analysis(EDA), Gradient Boost algorithm (ML algorithm to identify whether the customer is satisfied with the service or not) and Churn prediction. The churn prediction will give the results as reactive and proactive analysis. We have used the Gradient Boost algorithm ( ensemble learning technique) along with SMOTE-ENN in this paper which gives us an accuracy of 95%.This gradient boost algorithm gives the best accuracy when compared with other machine learning algorithms like, Logistic Regression, Random Forest Classifier and Decision Tree Classifier. Visualization of churned data and risk analysis is facilitated through Power BI.

Validation and Implementation of Customer Classification System using Machine Learning

ABSTRACT We have maintained the customer grade system that is being implemented to customers with excellent performance through customer segmentation for years. Currently, financial institutions that operate the customer grade system provide similar services based on the score calculation criteria, but the score calculation criteria vary from the financial institution to financial institution. In this study, we create a machine learning prediction model using items and added items that are based on the current customer grade of our bank,- and the purpose is an optimal model that considers the adequacy of existing variables and the validity of additional variables through comparison between models. Using Lasso, Elastic net and Multinomial Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, we propose that the best model be found and gradually applied to customer grade calculation criteria.