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Table of Contents
The advent of decision support technologies have altered the decision making process of businesses about customer relationship management in modern days. The mobile tools, e-services, wireless internet protocol influences business decision making. Satisfying the customers developing long term relationships with them is highly essential for surviving in a competitive market. The aim of the current study is to identify the literature related to business intelligence for decision Support with the identified decision models, methods and systems. The application of these support models into business intelligence system in customer relationship management is required to be demonstrated.
One of the main issues regarding the decision making in customer relationship management identified by Rahimi & Kozak (2017), is difficulties in bridging the gap between the rate of customer acquisition and customer retention. Using complex customer acquisition policies businesses can successfully acquire the targeted customers. However, acquiring customers and retaining them is different from each other where customer retention is more difficult. Therefore, better decision making in the customer relationship management purpose is required to be considered. With the help of modern day decision support system, the data gathering about the market situation, customer retention rate and other sales related information can be easily analysed by CRM systems and quick and effective decision can also be made based on this.
“Key elements of the body of knowledge for the field of decision support technology and business intelligence”
Business decision making is highly influenced and directed by the use of the business intelligence. Decision making is highly important in different organizational functioning such as supply chain management, risk mitigation, customer relationship management and so on. However, the current essay will focus on the customer relationship management and how decision making technologies support for making decision in this field. In the early 1980’s the concept of decision support systems has been developed. As per the views of Rahimi & Kozak (2017), the decision support system is a computer based system that helps decision makers in controlling ill-structured problems through direct interaction using data analysis models.
The structure of the decision making process is hierarchical in manner. Different parameters can be out by the users into this. However, decision support system works for evaluation the appropriateness of the alternative system in the decision making process.
“Identification of main decision models, decision methods, and decision support systems”
There are several models developed by the researchers which describe how a decision making proceeds. One of the basic models of decision making is the Vroom-Yetton-Jago Decision Model (Sun, Sun & Strang, 2018). As per this model, decisions are required to be made based on three factors. For instance, the decision is required to be evaluated based on its quality such as how effective it will be in making right decisions, how justified is the use of resources and how the outcomes will benefit the decision maker. Another factor is commitment of the subordinate where the decision maker is needed to understand if the subordinates are ready to contribute to the decision making process or not based on which the further decision making process can be conducted. The time constraint is also a factor that is required to be considered while making any decision. If there is a lack of time for completing the tasks decided, the model cannot be considered effective enough.
Another decision making model is The Recognition-Primed Decision Model. This model also indicates three steps that are required to be covered for making any decision. In the first phase, the decision maker is required to gather data about the situation that is needed to be solved. Viewpoints of the people experiencing the problem can be asked for their opinion about hoe the problem can be addressed. The next stage is to analyse the collected data where different questions and brainstorming is performed. Here, previous experiences used for decision making and information about the current situation are required to develop the solutions. After finishing this stage, the decision maker needs to implement the decision made in a timely manner.
It has been found that there are 4 methods of decision making. As per the views of Kitsios & Kamariotou (2016), decisions are often made by commanding. Here, an authority figure makes the decisions to another people using their delegated power. Decisions can also be made through discussion where different ideas collected will provide the opportunity to make decision better. Voting is another method that is used for making a decision. Voting system allows people to choose options based on their perspective appropriateness where chances of emerging conflict are lower. Another method of decision making is consensus.
Decision support systems are of different types with specific decision goals. Such decision support systems are status inquiry system which helps in taking operational and management or middle management level related decisions. Data analysis system is another decision support system which uses formula or algorithm for cash flow analysis. However, information analysis system is used for the purpose of sale analysis and market analysis which is the most relevant to the current topic which is business intelligence system in customer relationship management where information and analysis of the market outcomes and sales outcome can be clearly conducted. Apart from that for the purpose of accounting, accounting system is used. Optimization models are used for decision making in order to manage operations where model based system is used as DSS.
“Application of decision support models, methods and systems in related business intelligence systems such as e-government, e-business, e-banking, e-logistics, e-learning and warning systems”
The decision methods, support models and support systems analysed in the above section will be applied in the context of e-government, e-business, e-banking, e-logistics, e-learning and warning systems.
Governance of an organization is accompanied with various responsibilities, goals and objectives which are required to be fulfilled following the guidelines of the Vroom-Yetton-Jago Decision Model. Here the decision makers are required to analyses the time and material resources availability and the most effective way to use the same. Decision making methods such as commanding might be followed for making operational decision. It is because the top management will be more competent for making operational decisions as compared to the lower level employees (Abu Naser & Al Shobaki, 2016). However, the decision support system that is more appropriate for the e-governance is the status enquiry system. This system does not use any computation or analysis; the system only needs to know the status based on which the decisions are made automatically.
Considering the operation system and other management systems of the e-businesses, it can be stated that the ICT is a basic decision making system for providing technical support to the business. Along with this, the customer relationship management is another decision support system which helps the e-business to perform well for satisfying its customers. The CRM system provides the organization with opportunity to store data about the customer prospects. Customer interactions are also recorded and the information is shared throughout the organization especially to the marketing department. This provides insight about status of the relationship with the customers which help in making further marketing related decision easily.
Decision support system is highly accepted system in the e-banking industry more specifically in the process of decision making. As per the views of Al Shobaki & Abu Naser (2017), Banksealer system is a DSS that helps in making decision in online banking. The decision making is automated by the mentioned technology for reducing the fraud. This helps in acquiring customers and chances of retention increases (Rüzgar, 2018). However, the information is required to be gathered considering the guidelines of the decision models such as Recognition-Primed Decision Model in order to increase efficiency in finding the appropriateness of the decision. However, in order to improve or manage the customer relationship some important factors are required to be considered by the financial organization such as using strategy to identify the clients. The e-CRM helps in speedy processing of the customer preference related data and relationship is developed based on trust and convenience of the customers as through using this system, marketing, re-pricing and other decision is made by the marketers.
The supply chain management is considered as on whole system of supply chain. However, the decision support systems are used for understanding the strategic issues in the supply chain and how much it will impact on the business in future. In the case of e-logistics, the criteria on which the success of customer relation management approach can be developed is number of products delivered to the customers successfully, average length of the quality services of an organization, cost to deliver, cost to store and others (Zachary, 2018). The information-driven DSS and the communication-driven DSS work effectively to improve relationships with the client. With the information driven DSS, new customers can be easily attracted to the business through marketing as the customer related data can be analysed for making marketing decisions. However, decision about improving the customer journey can be made through using communication driven DSS where constant analysis of the status of customer relationship can be assessed.
The decision support system used in the context of e-learning is the onContact CRM. This has been observed to drive sales. It is because CRM system has sales automation feature that provides opportunity for managing with assistance on business card reading. As per the views of Vertakova, Mkrtchyan & Leontyev (2019), increased and maintained connection with the customers is essential for high retention. For instance, in e-learning process, students or course pursuer can access organizational information in a convenient and tailor made manner which will provide opportunity to the organization to be connected with the customers.
“Applications of current issues in intelligent decision support systems, multi-criteria, multi-objective and multi-level decision support models, optimization theory, group decision-making models, computational intelligence (such as fuzzy logic), and their applications to business intelligence systems”
Fuzzy logic: The computational intelligence
Decision systems use fuzzy logic in order to use and represent the experiences of the adequately. Based on this, the organizations may rely on the decision made as the experts making the decisions are already aware about the system business is using. As per the views of Wang & Reani (2017), this system is a multimode decision making system through which decisions such as strategies of preventing fraud and assessing risk and others can be programmed. The fraudster’s attempts to attack the payments of the customers can be reduced by using fuzzy logic.
Multi-criteria decision support models
This is concerned with the solving issues by structuring decisions and planning about the problem to be solved including several criteria. This is purported to support the decision makers at the time they face problems in making decisions. As there is no unique solution for problems such as retaining customers for a long term therefore preference of the decision makers can be obtained to separate different solutions. There is an algorithm used such as Multi-criteria decision making that allows the agents for evaluation the options for rank solution options based on their performance score against the models of others.
Multi-objective decision support models
Organizations also use multi-objective decision support model technically for avoiding decision making problems. Considering the inaccuracy which is driven by the personal judgements of the decision makers and the uncertainty this system has been developed where using the system intelligence the errors in decision making is avoided. For instance, marketing objectives might differ while using multi-modal marketing is performed. In order to integrate the marketing or promotion activities, customers can be retained.
Multi-level decision support models
The multilevel decision making issues include different decision making agents presented in different levels in a structure of hierarchy. This includes interactive decision making for multi-level organisations where the decision making is executed sequentially that starts from the top level and moves toward lower levels (Sun, Sun & Strang, 2018). For instance, the inputs in increasing the customer retention rate are provided from the different levels of an organisation. In this context, issues may emerge such as non-collaboration between the decision makers due to discrepancy in objectives between them.
The optimization theory is based on the translation of the key features of the business related problem to be solved by the decision maker effectively. In order to obtain dynamic decision making within the current business situation, basic knowledge of business intelligence and suitable techniques for optimizing the same is required to be highly attended. For instance, in order to optimize the customer relationship decisions. Highly optimized decisions regarding the marketing and promotion may lead to acquisition of customer support more which will enable the companies to gain long-term association with the customers. This will ensure high profitability of the organization as the rate of repeat purchase will increase based on this. It is due to the fact that the optimization process helps organizations to help in customized decision making which appropriately assists the marketers to reduce the cost of changing decisions. This reduces the operational cost of the organization.
The two types of optimization method is genetic algorithm and ant colony optimization. Using the effectiveness of computation and artificial intelligence for finding out optimized solutions in order to search for problems exploiting biology inspired techniques such as mutation, section and reproduction the decision support system is made. These genetic algorithms are used for reducing the search problems. On the other hand, the ant colony optimization is used as a technique for solving the problems of computation. The discrete optimization problems can be solved through using ant colony optimization.
Group decision-making models
Businesses opt for group decision making procedure as this helps in increasing the possibility of inclusion of the solution through collaboration. As per the rational decision-making models in the choosing the best option for solutions and preparing an action plan based on it. In this context, the group decision support system can be used to make the decision effective (Saavedra & Bach, 2017). For instance, inputs from employees working in different departments can also be collaborated in order to make decisions regarding the choice of resources, promotional channels and others to develop and implement effective planning of customer relationship management through analysing feedback from the customers, customer’s purchasing, brand perception and other related information that helps in making decisions further.
In conclusion, it can be stated that the businesses use decision support systems in different field of operation for gaining high efficiency in making decisions regarding, operation, marketing, accounting, supply chain and others. However, based on the requirements of modern day marketing relationship businesses are required to use some specific CRM systems that helps in managing relationship with the customers. The Vroom-Yetton-Jago Decision Model suggests developing a base for making decision regarding any of the company activity through three steps such as considering quality of the decision, commitment of the employee to opt for the decision and available time to use the resources adequately for gaining business success. The study also identified some decision support systems such as Status inquiry system, Data analysis system, Information analysis, accounting system. The decision support models ranges from multi-criteria, multi-objective and multi-level decision which helps the businesses solve decision related issues quickly and efficiently.
Rahimi, R., & Kozak, M. (2017). Impact of customer relationship management on customer satisfaction: The case of a budget hotel chain. Journal of Travel & Tourism Marketing, 34(1), 40-51.
Abu Naser, S. S., & Al Shobaki, M. J. (2016). Enhancing the use of Decision Support Systems for Re-engineering of Operations and Business-Applied Study on the Palestinian Universities. Available at SSRN 2814456.
Al Shobaki, M. J., & Abu Naser, S. S. (2017). Requirements for Applying Decision Support Systems in Palestinian Higher Education Institutions-Applied Study on Al-Aqsa University in Gaza. International Journal of Information Technology and Electrical Engineering, 6(4), 42-55.
Rüzgar, N. (2018). THE EFFECTS OF DECISION-MAKING STYLES OF LEADERSHIP ON EMPLOYEES KNOWLEDGE SHARING WITHIN THE ORGANIZATION. Bilgi Ekonomisi ve Yönetimi Dergisi, 13(2), 107-120.
Sun, Z., Sun, L., & Strang, K. (2018). Big data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162-169.
Saavedra, M. S. M., & Bach, C. (2017). Factors to Determine Business Intelligence Implementation in Organizations. European Journal of Engineering Research and Science, 2(12), 1-7.
Vertakova, Y., Mkrtchyan, V., & Leontyev, E. (2019). Information provision of decision support systems in conditions of structural changes and digitalization of the economy. Journal of Applied Engineering Science, 17(1), 74-80.
Wang, W., & Reani, M. (2017). The rise of mobile computing for Group Decision Support Systems: A comparative evaluation of mobile and desktop. International Journal of Human-Computer Studies, 104, 16-35.
Kitsios, F., & Kamariotou, M. (2016, September). Decision support systems and business strategy: a conceptual framework for strategic information systems planning. In 2016 6th International Conference on IT Convergence and Security (ICITCS) (pp. 1-5). IEEE.
Zachary, D. A. (2018, September). A Recognition Primed Decision Model for Public Consumption of Government Data. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting (Vol. 62, No. 1, pp. 1712-1716). Sage CA: Los Angeles, CA: SAGE Publications.
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