scholarly journals MARKETING ANALYTICS IN THE FUNCTION OF DECISION MAKING IN BANKS / MARKETING ANALITIKA U FUNKCIJI DONOŠENJA ODLUKA U BANKAMA

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Dušica Sanader ◽  
Marko Laketa ◽  
Luka Laketa
Keyword(s):  
Decision Making ◽  
Nearest Neighbor ◽  
Analytical Techniques ◽  
Large Data ◽  
Poor Quality ◽  
Business Analytics ◽  
Text Analytics ◽  
Quality Of Data ◽  
Marketing Model ◽  
Marketing Models

In this paper we have presented analytics as a crucial factor in marketing decision making. The banking environment is turbulent and complex today. The client is well educated and his needs are constantly changing. He has access to lot of information and has power of choice. His digital expectations are high: he needs to access banking products and services from any place and at any time. Also, the client is leaving data everywhere: in bank`s database and at different internet sites. As data are comparative advantages today, the bank is eager to collect them in order to analyze data and make marketing decisions. Analytics is helping bank in this new era of doing business. Analytics assumes analysis, interpretation and communication of understandable patterns in the data. It relies on mathematics and statistics techniques in order to find new knowledge and meaning of existing data. There are many analytics techniques which are based on algorithms and databases. Depending on which problem a bank needs to solve or what it aim wants to achieve, the bank uses one or more analytical techniques. Survival Analysis, Nearest Neighbor Classification, Neural Network, Logistic Regression and Decision Tree are the most common techniques used in banking sector.Marketing analytics models support marketing decisions. Marketing models enable bank to predict outcome (e.g. if a client is likely to leave) or to identify differences between group of clients. In order to achieve results, the bank has created different marketing models such as Response Models, Queues, Retention Models, Market Basket Analysis, and Win-Back Models. Marketing models are helping the bank to predict if the client will answer on offer which bank is offering through marketing campaigns. The aim of these models is to create target group of clients or segment with likelihood to increase their relationship with the bank. In order to create marketing model, the bank defines the aim which it wants to achieve. Usually, the bank wants to keep most profitable clients and to decrease costs. After defining the aim of marketing model, the bank collects, analyses and transforms data needed for creation of the model. Also, it is necessary to estimate data quality. If data are no longer of high quality, there can be issue with model results. Also, the bank has to take into consideration the volume, velocity and variety of data. Large data are collected from lots of data sources and stored in data warehouse or data marts using modern technologies. Model technologies help to convert data into valuable information which can be used for making decisions. After creation of a model, it is necessary to estimate its accuracy, comprehensibility and level of confidence in results given by the model. Also, every model has to be managed (quarterly or yearly) in order to test if the results are still valid or it has to be changed with a new model.Analytics gives competitive advantages to the bank. It can improve effectiveness of processes and organization and improve efficiency in making marketing decisions. The bank as a profit oriented organization tends to contact profitable customer in order to increase their value through customer lifetime value. In this way, the bank has a possibility to invest in relationship with clients which can be valuable in the long run. Analytics gives knowledge about the customer. It helps to discover pattern in large amount of data. The contribution of analytics can be seen in decreasing marketing costs by identifying clients who are likely to respond on marketing campaigns. Also, it contributes in pricing, channel management, selling, segmentation and product development. Today, text analytics is also important for banking business, as lots of data are unstructured and can be found in form of documents, blogs, video sharing and comments on internet sites. In order to use this kind of data, text analytics helps the bank to understand data and read them with certain limitation. However, there are also challenges which the bank faces when implementing analytics. Limited budget, employees without necessary skills for the development of models, poor quality of data, inadequate and unintegrated softer tools, problems with protection of client data as well as imprecisely defined aim of model can be resulted in unsatisfactory realization and poor position of analytics in the bank. In order to overcome these challenges, the bank needs to set up a strategy of analytics and to link it with all the internal processes in organization.

Searching for Herbert Simon

2013 ◽  
Vol 4 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Michael F. Gorman ◽  
Donald E. Wynn ◽  
William David Salisbury
Keyword(s):  
Decision Making ◽  
Analytical Techniques ◽  
Decision Makers ◽  
Business Analytics ◽  
Herbert Simon ◽  
Business Decision ◽  
Wide Range ◽  
Human Decision ◽  
Potential Impact ◽  
Effective Use

Since Herbert Simon’s seminal work (Simon, 1957) on bounded rationality researchers and practitioners have sought the “holy grail” of computer-supported decision-making. A recent wave of interest in “business analytics” (BA) has elevated interest in data-driven analytical decision making to the forefront. While reporting and prediction via business intelligence (BI) systems has been an important component to business decision making for some time, BA broadens its scope and potential impact in business decision making further by moving the focus to prescription. The authors see BA as the end-to-end process integrating the production through consumption of the data, and making more extensive use of the data through heavily automated, integrated and advanced predictive and prescriptive tools in ways that better support, or replace, the human decision maker. With the advent of “big data”, BA already extends beyond internal databases to external and unstructured data that is publicly produced and consumed data with new analytical techniques to better enable business decision makers in a connected world. BI research in the future will be broader in scope, and the challenge is to make effective use of a wide range of data with varying degrees of structure, and from sources both internal and external to the organization. In this paper, we suggest ways that this broader focus of BA will also affect future BI research streams.

scholarly journals Decision making in the context of business intelligence and data quality

2009 ◽  
Vol 11 (2) ◽  
Author(s):  
L. Marshall ◽  
R. De la Harpe
Keyword(s):  
Decision Making ◽  
Data Quality ◽  
Business Intelligence ◽  
Information Quality ◽  
Poor Quality ◽  
Quality Of Data ◽  
Literature Study ◽  
Business Confidence ◽  
The Right

[email protected] Making decisions in a business intelligence (BI) environment can become extremely challenging and sometimes even impossible if the data on which the decisions are based are of poor quality. It is only possible to utilise data effectively when it is accurate, up-to-date, complete and available when needed. The BI decision makers and users are in the best position to determine the quality of the data available to them. It is important to ask the right questions of them; therefore the issues of information quality in the BI environment were established through a literature study. Information-related problems may cause supplier relationships to deteriorate, reduce internal productivity and the business' confidence in IT. Ultimately it can have implications for an organisation's ability to perform and remain competitive. The purpose of this article is aimed at identifying the underlying factors that prevent information from being easily and effectively utilised and understanding how these factors can influence the decision-making process, particularly within a BI environment. An exploratory investigation was conducted at a large retail organisation in South Africa to collect empirical data from BI users through unstructured interviews. Some of the main findings indicate specific causes that impact the decisions of BI users, including accuracy, inconsistency, understandability and availability of information. Key performance measures that are directly impacted by the quality of data on decision-making include waste, availability, sales and supplier fulfilment. The time spent on investigating and resolving data quality issues has a major impact on productivity. The importance of documentation was highlighted as an important issue that requires further investigation. The initial results indicate the value of

THE APPLICATION OF THE MS EXCEL PROGRAM AND THE INFORMALIZED BUSINESS INTELLIGENCE ANALYTICS PLATFORMS IN THE MANAGEMENT OF THE ENTERPRISES

2017 ◽  
Vol 5 (1) ◽  
pp. 0-0
Author(s):  
Dariusz Prokopowicz ◽  
Jan Grzegorek
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Intelligence ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Analytics ◽  
Business Decision ◽  
Decision Making Processes ◽  
Analytical Formulas

Rapid progress is being made in the field of IT applications in the analysis of the economic and financial situation of enterprises and in the processes supporting management of organizations. In terms of the fastest growing areas of information and communication technology, which are the prerequisites for the progress of online electronic banking, it is necessary to disseminate the standards of financial operations have been carried out. The cloud as well as the use of large data sets in the so-called. Big Data platforms. The current Big Data technology solutions are not just large databases, data warehouses allow for multifaceted analysis of huge volumes of quantitative data for periodic managerial reporting. Business decision-making processes should be based on the analysis of reliable and up-to-date market and business data. The information necessary for the decision-making processes has been collected, stored, ordered and pre-summed up in the form of Business Intelligence analytics reports in corporations. Business Intelligence analyzes give managers the ability to analyze the large data sets in real time, which significantly contributes to improving business management efficiency. At present, business analytics use either the advanced analytical formulas of Ms Excel or computerized platforms that include ready-made Business Intelligence reporting formulas.

Analysis of strengths, weaknesses, opportunities, and threats in the development of a health technology assessment program in Turkey

2008 ◽  
Vol 24 (02) ◽  
pp. 235-240 ◽  
Author(s):  
Rabia Kahveci ◽  
Catherine Meads
Keyword(s):  
Decision Making ◽  
Health Technology Assessment ◽  
Healthcare System ◽  
Technology Assessment ◽  
Cost Effective ◽  
Health Technology ◽  
Poor Quality ◽  
Quality Of Data ◽  
Good Opportunity ◽  
Use Of Resources

Objectives:The Turkish healthcare system is currently undergoing reform, and efficient use of resources has become a key factor in determining the allocation of resources. The objective of this study was to analyze strengths, weaknesses, opportunities, and threats (SWOT) in the development of a health technology assessment (HTA) program in Turkey.Methods:A SWOT analysis was performed using a literature review and interviews with key people in the Turkish Ministry of Health and Ministry of Labor and Social Security.Results:Regarding recent reforms in health care, investments for information network and databank are the strengths, but the traditional “expert-based” decision making, poor availability of data, and poor quality of data could be seen as some of the weaknesses. Another major weakness is lack of general awareness of HTA. Increasing demand for transparency in decision making, demand for evidence, and demand for credibility by decision makers are some of the opportunities, and current healthcare reforms, i.e., restructuring of healthcare and general health insurance, could also be seen as major opportunities. These opportunities unfortunately could be threatened by lack of funding, and resources are challenged by large, recent national investments.Conclusions:There is a good opportunity for Turkey to use the skills in HTA currently being developed through activities in Europe and the Americas to assist in the development of a much more cost-effective and transparent healthcare system in Turkey.

Detection of Poor Quality Artemisinin-based Combination Therapy (ACT) Medicines Marketed in Benin Using Simple and Advanced Analytical Techniques

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Achille Yemoa ◽  
Vedaste Habyalimana ◽  
Jeremie K. Mbinze ◽  
Victoria Crickboom ◽  
Benjamin Muhigirwa ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Analytical Techniques ◽  
Poor Quality

Poor Quality of Data in Africa: What Are the Issues?

2018 ◽  
Vol 46 (6) ◽  
pp. 851-877 ◽  
Author(s):  
Abel Kinyondo ◽  
Riccardo Pelizzo
Keyword(s):  
Poor Quality ◽  
Quality Of Data

scholarly journals Tauke and Emotional Network Model in Oil Palm Marketing: Getting Fresh Fruit from Smallholders in Indonesia

2020 ◽  
Vol 55 (4) ◽  
Author(s):  
Rizabuana Ismail ◽  
Slamet Haryono ◽  
Ira Maya Sofa Harahap ◽  
Ria Manurung
Keyword(s):  
Oil Palm ◽  
Marketing Channel ◽  
Marketing Channels ◽  
Fresh Fruit ◽  
Network Marketing ◽  
Marketing Model ◽  
Marketing Models ◽  
Cash Payment ◽  
Fresh Fruit Bunches ◽  
Depth Interviews

This article describes how fresh fruit bunches grown by oil palm smallholders are incorporated into oil palm marketing models in Indonesia. This emotional network marketing model is a supplementary model of marketing models in Malaysia which is called factory centered and middleman model. This research uses a descriptive qualitative method. The data was collected by conducted in-depth interviews with 28 informants coming from 4 (four) categories of oil palm smallholders: oil palm tauke (middleman) that included big tauke and small tauke, workers in the loading ramps, and workers in the oil palm factories who were involved in oil palm marketing channels. The result of the research showed that the oil palm marketing channel between smallholders and either small tauke and big tauke was based on an emotional network with a strong bond of friendship, brotherhood, dwelling location, cash payment, giving loan with reasonable requirements, and providing transportation for fresh fruit bunches. In contrast, oil palm marketing channel among smallholders, loading ramp buyers, and POF was based on regulations. This writing presented a different perspective of oil palm marketing channels in general by involving the emotional network of the existing actors for getting fresh fruit bunches and the advantages of oil palm marketing. In this marketing model, there is a longer marketing channel and actors with their varied roles.

Sign in / Sign up

Export Citation Format

Share Document