The Impact of Customer-Centric Knowledge Management Systems on Strategic Decision-Making

For organizations to make strategic decisions, they require knowledge derived from either internal resources or the external environment. This research examines the sharing of knowledge between an organization and its external customers, including the role of a customer-centric knowledge system. A recently developed customer-centric knowledge system is observed to determine the support it provides for the identification and utilization of customer knowledge, and the influence it has on strategic decision-making processes. Factors considered to influence an organization’s utilization of customer knowledge in decision-making are the perceptions and beliefs regarding customer knowledge, knowledge management support processes, types of knowledge being captured, and system design processes adopted by the organization. The effects of the newly implemented customer knowledge management system and the captured customer knowledge on strategic decision-making are examined through a qualitative case study situated in an international health care systems provider. A survey administered to the sales function collects collaborating quantitative data that is utilized to understand the impact of customer knowledge, including the sales function’s role in acquiring this knowledge from medical professionals. This research serves to answer the following questions: 1) what do organizations consider customer knowledge, 2) what types of customer knowledge may be captured using knowledge management systems and processes, and 3) what impact does customer knowledge have on strategic decision-making

CUSTOMER-CENTRIC REVENUE MANAGEMENT IN MANUFACTURING - A DECISION SUPPORT SYSTEM

Manufacturing providers aim not only for a revenu maximizing allocation of their limited production capacity but also for the establishment of long-term customer relations. Du to long-term contracts and strategic reference customers, users of traditional revenu management systems already account for varying worthiness of clients, and intuitively ignore or override booking control suggestions, such as order´s denial or pricing level, in order not to endanger customer relations. So with a view to a holistic approach, the integration of both management concepts, each of decisive competitive impact, is advised. However, an implemented IT-system, that provides the revenu analyst with greater insights, higher accuracy, quality and trust in decision process, is still missing in manufacturing industry. This reflects the common frustration of managers and analysts in practice when dealing with conflicting ideas or theories generated by research community. We believe our prototype is the first to supply analysts with formatted and summarized information to make transparent and comprehensible control decisions, suggesting specific booking control actions based on simulation results and integrated usage of provided data. It also accounts for the strategic dimension of the problem when confronted with these partly diametric objectives of revenu vs. customer relationship management

Modeling churn using customer lifetime value.

The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a productcentric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable.Data mining; Decision support systems; Marketing; Churn prediction;

Digitalization and Sustainability - Opportunities and Challenges for Insurance Industry

Digital revolution and demands for sustainability are the most important determinants of the economic development in the last years. Insurance as a risk protection mechanism can support the achievement of many Global Sustainable Development Goals of the United Nations in direct or indirect manner. Decision engines and artificial intelligence support to decision-making allow insurers to propose tailored customer-centric services based on micro-segments and personalized risk profiles. Providing a more adequate set of products insurance creates a financial safety net for women, families and businesses and contributes to poverty alleviation and supports economic growth, innovations and employment. Therefore, the aim of this paper is to present the possibilities of application of information technology in insurance and challenges for its implementation

Improving the quality of the industrial enterprise management based on the network-centric approach

The article examines the network-centric approach to the industrial enterprise management to improve the ef ciency and effectiveness in the implementation of production plans and maximize responsiveness to customers. A network-centric management means the decentralized enterprise group management. A group means a set of enterprise divisions, which should solve by joint efforts a certain case that occurs in the production process. The network-centric management involves more delegation of authority to the lower elements of the enterprise’s organizational structure. The industrial enterprise is considered as a large complex system (production system) functioning and controlled amidst various types of uncertainty: information support uncertainty and goal uncertainty or multicriteria uncertainty. The information support uncertainty occurs because the complex system functioning always takes place in the context of incomplete and fuzzy information. Goal uncertainty or multicriteria uncertainty caused by a great number of goalsestablished for the production system. The network-centric management task de nition by the production system is formulated. The authors offer a mathematical model for optimal planning of consumers’ orders production with the participation of the main enterprise divisions. The methods of formalization of various types of uncertainty in production planning tasks are considered on the basis of the application of the fuzzy sets theory. An enterprise command center is offered as an effective tool for making management decisions by divisions. The article demonstrates that decentralized group management methods can improve the ef ciency and effectiveness of the implementation of production plans through the self-organization mechanisms of enterprise divisions.The work has been prepared with the financial support from the Russian Ministry of Education and Science (Contract No. 02.G25.31.0068 of 23.05.2013 as part of the measure to implement Decision of the Russian Government No. 218)

A Look Toward the Future: Decision Support Systems Research is Alive and Well

This commentary examines the historical importance of decision support to the information systems (IS) field from the viewpoint of four researchers whose work spans the several decades of decision support systems (DSS) research. Given this unique “generational” vantage point, we present the changes in and impact of DSS research as well as future considerations for decision support in the IS field. We argue that the DSS area has remained vital as technology has evolved and our understanding of decision-making processes has deepened. DSS work over the last several years has contributed both breadth and depth to decision-making research; the challenge now is to make sense of it all by placing it in an understandable context and by applying our analysis to the relevant issues looming in the future. One major outcome of this commentary is the identification of future trends in DSS research and what the users of these new DSS outlets can learn from the past. Trends include the increasing impact of social and mobile computing on DSS research, as well as knowledge management DSS and negotiation support systems that shift the focus to delivering more customer-centric and marketplace support

Impact of CRM adoption on organizational performance: Moderating role of technological turbulence

Purpose Customer relationship management (CRM) is instrumental to attain and sustain organizational competitive advantage. Innovation in terms of CRM adoption is the key to gain competitive advantage, and being innovative is dependent on how well organizations know about changing demands of customers and their changing ways to gain access to the market. There is hence a need to develop ongoing empirical insights from diverse management perspectives into the effect of CRM adoption on organizational performance. In this context, the purpose of this study is to develop empirical insights in relation to the moderation of technological turbulence in the banking sector. Design/methodology/approach Primary data were collected and analyzed from 277 CRM staff-members of the banking sector in Pakistan to test a conceptual model. Frequencies of demographics are calculated with correlation and regression analyses using SPSS. The correlation analysis was performed to identify the direction that exists between the dependent and independent variables, and the regression analysis was performed to study the strength/intensity of the independent variable over the dependent variable. Moderating regression analysis was performed to find the moderation effect of technological turbulence on CRM adoption and organizational performance. Findings The CRM adoption has a critical positive impact on organizational performance in the settings of business-to-customer (B2C) perspective in the banking sector. Moreover, the results uncover that improved client satisfaction through CRM adoption prompts better organizational performance in the B2C organization. The authors also have found that technological turbulence has a negative guiding impact on the association linking with CRM adoption, as well as organizational performance. Research limitations/implications The conceptual model that is proposed in this study and supported by empirical insights offers researchers to develop future research studies on the moderating role of technological turbulence to analyze the influence of CRM adoption on organizational performance. Practical implications The empirical insights of this study are valuable for the professionals in the banking sector and other B2C organizations to enrich their organizational performance through CRM adoption while considering the moderating role of technological turbulence. Originality/value Based on an empirical study, in support of an original conceptual model, the insights of this paper contribute to the extant literature in the CRM, bank marketing and management, service management, B2C marketing and the emerging economy knowledge streams

Customer-Centric Sales Forecasting Model: RFM-ARIMA Approach

Background: Decision makers use the process of determining the best course of action by processing, analysing & interpreting the data to gain insights, known as Business Intelligence. Some decision support systems use sales figures to predict future expansion, but few consider the effect of customer data. Objectives: The main objective of this study is to build a model that will give a forecast based on fine-tuned sales numbers using some customer-centric features. Methods/Approach: We first use the RFM model to segment the customers into distinct segments based on customer buying characteristics and then discard the segments that are irrelevant to the business. Then we use the ARIMA model to do the sales forecasting for the remainder of the data. Results: Using this model, we were able to achieve a better fitment of the data for the prediction model and achieved a better accuracy when used after RFM analysis. Conclusions: We tried to merge two different concepts to do a cross-functional analysis for better decision-making. We were able to present the RFM-ARIMA model as a better metric or approach to fine-tune the sales analysis