An explanatory and predictive PLS-SEM approach to the relationship between organizational culture,organizational performance and customer loyalty: The case of health clubs

Purpose This study aims to analyze the impact and predictive capacity of organizational culture on both customer loyalty and organizational performance in health clubs using data from managers and customers of health clubs in Spain. Design/methodology/approach A total of 101 managers were asked to measure organizational culture and organizational performance and 2,931 customers were asked to indicate their customer loyalty. The proposed hypotheses were tested and their predictability assessed through PLS-SEM. A composite concept was adopted to analyze the relationships between the different constructs and their indicators. Findings The findings suggest that organizational culture has a positive relationship with both customer loyalty and organizational performance. The four main dimensions of organizational culture that influence this relationship are, in order of significance, organizational presence, formalization, atmosphere and service-equipment. The authors’ model has a very good predictive power for both dependent variables. Originality/value Customer loyalty is an aspect of health clubs that can be improved. This study highlights the importance of creating a strong organizational culture in health clubs, as it enhances and predicts customer loyalty and organizational performance. Its predictability has already been tested with samples of managers and customers, with the analysis being performed from the perspective of the organization’s management and customer perceptions. This study also contributes to the field of sport management, using a predictive PLS-SEM techniqu

Data Mining as a tool to Predict the Churn Behaviour among Indian bank customers

The socio economic growth of the country is mainly dependent on the services sector. The financial sector is one of these services sector. Data mining is evolving into a strategically important dimension for many business organizations including b anking sector. The churn problem in bankin g sector can be resolved using data mining techniques. The customer churn is a common measure of lost customers. By minimizing customer churn a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer relationship management (CRM) can be defined as the process of acquiring, retaining and growing profitable customer which requires a clear focus on service attributes that represent value to the customer and c reates loyalty. Customer retention is c ritical for a good marketing and a customer relationship management strategy. The prevention of customer churn through customer retention is a core issue of Customer relationship management. Predictive data mining techniq ues are useful to convert the meani ngful data into knowledge. In this analysis the data has been analyzed using probabilistic data mining algorithm Naive Bayes, the decision trees algorithm (J48) and the support vector machines(SMO)

""Counting Your Customers" One by One: An Individual Level RF Analysis Based on Consumer Behavior Theory"

In customer relationship management (CRM), ad hoc rules are often employed to judge whether customers are active in a "non-contractual" setting. For example, a customer is considered to have dropped out if he or she has not made purchase for over three months. However, for customers with a long interpurchase time, this three-month time frame would not apply. Hence, when assessing customer attrition, it is important to account for customer heterogeneity. Although this issue was recognized by Schmittlein et al. (1987), who proposed the Pareto/NBD "counting your customers" framework almost 20 years ago, today's marketing demands a more individual level analysis. This research presents a proposed model that captures customer heterogeneity through estimation of individual-specific parameters, while maintaining theoretically sound assumptions of individual behavior in a Pareto/NBD model (a Poisson purchase process and a memoryless dropout process). The model not only relaxes the assumption of independence of the two behavioral processes, it also provides useful outputs for CRM, such as a customer-specific lifetime and retention rate, which could not have been obtained otherwise. Its predictive performance is compared against the benchmark Pareto/NBD model. The model extension, as applied to scanner panel data, demonstrates that recency-frequency (RF) data, in conjunction with customer behavior and demographics, can provide important insights into direct marketing issues, such as whether long-life customers spend more and are more profitable.

Managing Dynamic Enterprise and Urgent Workloads on Clouds Using Layered Queuing and Historical Performance Models

The automatic allocation of enterprise workload to resources can be enhanced by being able to make what-if response time predictions whilst different allocations are being considered. We experimentally investigate an historical and a layered queuing performance model and show how they can provide a good level of support for a dynamic-urgent cloud environment. Using this we define, implement and experimentally investigate the effectiveness of a prediction-based cloud workload and resource management algorithm. Based on these experimental analyses we: i.) comparatively evaluate the layered queuing and historical techniques; ii.) evaluate the effectiveness of the management algorithm in different operating scenarios; and iii.) provide guidance on using prediction-based workload and resource management

Service Quality and Customer Loyalty in a Post-Crisis Context. Prediction-Oriented Modeling to Enhance the Particular Importance of a Social and Sustainable Approach

Research into the influence of service quality on customer loyalty has typically focused on confirming isolated direct causal influences regarding particular dimensions of quality, usually undertaken in the context of positive, firm-customer relations. The present study extends analysis of these factors through a new lens. First, the study was undertaken in a market context following a crisis that has had far-reaching consequences for customers’ relational behaviors. We explore the case of the Spanish banking industry, a sector that accurately reflects these new relational conditions, including a rising demand for more socially responsible banking. Second, we propose a holistic model that combines the effects of four key factors associated with service quality (outcome, personnel, servicescape and social qualities). We also apply an innovative predictive methodological technique using partial least squares (PLS) and qualitative comparative analysis (QCA) that enables us not only to determine the direct causal effects among variables, but also to consider different scenarios in which to predict customer loyalty. The results highlight the role of outcome and social qualities. The novelty of the social qualities factor helps to underscore the importance of social, ethical and sustainable practices to customer loyalty, although personnel and servicescape qualities must also be present to improve the predictive capability of service quality on loyalty

Improving customer churn prediction by data augmentation using pictorial stimulus-choice data

The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures

Implementing A Customer Relationship Management: The Proactive Steps For Sales Managers To Prevent A Sub-Optimised Salesforce Performance

Customer Relationship Management (CRM) is a comprehensive business model for increasing revenue and profits by focusing on customers.  It includes any application or initiative designed to help firms optimise interactions with customers, suppliers or prospects via one or more touch points – such as a call centre, sales person, distributor, store, branch office, web, or email for the purpose of acquiring, retaining or cross-selling customer.  But it is a serious mistake to consider CRM as mere software.  In fact, many firms are struggling with their CRM initiatives precisely because they have bought the sophisticated software, but do not have the culture, structure, leadership, or internal technical expertise to make the initiative successful.  Factor analysis was used to analyse the collected data, in order to isolate principal components that account for the proactive steps for sales managers to prevent an out-and-out CRM failure.  The result shows that a close relationship exists between a company’s effective CRM strategic implementation and its salesforce compensation plan.  To get its salespeople to aid in successfully implementing its CRM, sales managers need to coordinate its sales compensation plan with the company’s CRM strategy. Keywords: Campaign Management; Transaction-based Interaction; Point-of-Sale Interaction; Predictive Modelling; Empowerment; Data-driven Marketing; Response List; Database Enhancement

Customer-oriented risk assessment in Network Utilities

For companies that distribute services such as telecommunications, water, energy, gas, etc., quality perceived by the customers has a strong impact on the fulfillment of financial goals, positively increasing the demand and negatively increasing the risk of customer churn (loss of customers). Failures by these companies may cause customer affection in a massive way, augmenting the intention to leave the company. Therefore, maintenance performance and specifically service reliability has a strong influence on financial goals. This paper proposes a methodology to evaluate the contribution of the maintenance department in economic terms, based on service unreliability by network failures. The developed methodology aims to provide an analysis of failures to facilitate decision making about maintenance (preventive/predictive and corrective) costs versus negative impacts in end-customer invoicing based on the probability of losing customers. Survival analysis of recurrent failures with the General Renewal Process distribution is used for this novel purpose with the intention to be applied as a standard procedure to calculate the expected maintenance financial impact, for a given period of time. Also, geographical areas of coverage are distinguished, enabling the comparison of different technical or management alternatives. Two case studies in a telecommunications services company are presented in order to illustrate the applicability of the methodology