Importance of Applying Big Data Concept in Marketing Decision Making

Importance of Applying Big Data Concept in Marketing Decision Making

Savo Stupar (School of Economics and Business, University of Sarajevo, Bosnia and Herzegovina), Emir Kurtović (School of Economics and Business, University of Sarajevo, Bosnia and Herzegovina) and Mirha Bičo Ćar (School of Economics and Business, University of Sarajevo, Bosnia and Herzegovina)
DOI: 10.4018/978-1-7998-5077-9.ch004
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The aim of this chapter is to enable marketing managers to gain basic knowledge of the capabilities of the latest data management technology, big data, which has the potential of digitally storing huge amounts of data, processing and utilizing the results of processing different types of data, as well as data of different formats in real-time. Due to the enormous potential of implementing the big data, there are also tremendous expectations in terms of the direct financial benefits of its implementation. Realizing all these expectations is a very complex task, which is set to marketing and other managers. The knowledge and skills of managers acquired by education will greatly help to understand the benefits of faster adoption and implementation of new data management paradigms. This chapter emphasizes the differences between the big data concept and conventional data processing technologies, as well as the benefits and potentials that this concept offers, especially when it comes to the process of making quick marketing decisions or making decisions in a reasonably short time.
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The primary objective of this paper is to integrate and systematize some basic knowledge that is commonly found in various articles and / or books in one place, and to explain to marketers in an acceptable way what the revolution and potential of the new data management concept - Big Data, and how they can use this concept to realize the basic concept of e-marketing - “added value”, where competitors strive to deliver as much benefits and added value to their customers as possible (Aker, Kumar & Day, 2016).

The question of the type of why something goes wrong (not the way we want it) in any human activity, and therefore business or marketing activity, can be answered with two universal reasons. The first reason is formulated by Peter's principle, which states: “In the hierarchy, every employee longs to be promoted to the level of his incompetence”. (Laurence & Hull, 1989, p. 28) In time, every employee in the hierarchy will come with an employee who is incapable of doing his job. However, the hierarchy is how it works, because jobs are done by those employees who have not yet reached the level of their incapacity. The second reason is that there is a natural tendency of every system (here we mean organizational system, or company) towards a state of chaos and disorder, called entropy (Shannon, 1948, Martínez-Berumena, López-Torresa, Romo-Rojas, 2014; p 396; Ursacescu, Cioc, 2016, p 83; Ben-Naim, 2008, Farazmand, 2003).

Because of this feature of each system, it is necessary to continuously invest certain energy, that is, to undertake appropriate activities (management activities, which solve the problems and improve processes), or activities that act in the direction of order and organization. This work can also be considered as an attempt to act in this direction in order to increase the level of education of the employees, who work in this field, in order to reach the level of their incapacity as soon as possible, and in order to influence their knowledge to reduce entropy and system organization. In order to organize the system, problems must be solved, and in order for them to be successful, good decisions must be made. In order to make quality decisions, they must be based on information. Therefore we fight information against entropy, so in information theory, information is defined as negative entropy. Those can't be any kind of information. It must be information that is relevant to the particular domain of the problem being addressed, and information that is correct, timely, clear, accessible and complete. How is information generated and how do we define it? Information is derived from data. Data is the carrier of information. “Data is facts and figures, raw information” (Kroenke, 1977). “Data items refer to an elementary description of things, events, activities, and transactions that are recorded, classified, and stored but are not organized to convey any specific meaning. Data items can be numbers, letters, figures, sounds, or images. Examples of data items are a student grade in a class and the number of hours an employee worked in a certain week” (Rainer R. and Turban, 2008).

“Information is new knowledge, which brings to the recipient new facts. It has the character of novelty, eliminates uncertainty and serves as a background for decision making ” (Birolla, Habek, Kliment A., Kliment K., Padjen, Ugarković, 1985). “Information refers to data that have been organized so that they have meaning and value to the recipient. For example, a grade point average (GPA) is data, but a student’s name coupled with his or her GPA is information. The recipient interprets the meaning and draws conclusions and implications from the information” (Rainer R. and Turban, 2008).

Key Terms in this Chapter

Conversion: Is the process of transforming a website visitor into a customer or client.

Conversion Marketing: Is a set of marketing activities that aim to increase number of conversions.

Hadoop: Is an open source software application, which is an open source framework written in the Java programming language to enable fast, easy, and inexpensive storage and processing of huge amounts of data in real or reasonable time.

Personalization of Marketing Content: Is the use of digital clues that users leave behind when searching the Internet to offer personalized advertising or other content based on their current needs.

Data Warehousing: Is a method by which a large amount of differently structured data is analyzed and processed to support decision making and management in companies. It is one of the methods of business intelligence.

Hadoop Distributed File System (HDFS): Is a core component of Hadoop, and its most important task is to enable large amounts of data to be stored in a fast, inexpensive, and easy way. One of the essential features of HDFS is the ability to quickly detect and locate errors and to correct them automatically.

MapReduce: Is a specific programming model, which as such represents a new approach to solving the problem of processing large amounts of differently structured data. It consists of two functions - Map (sorting and filtering data) and Reduce (summarizing intermediate results), and it is executed in parallel and distributed.

Data-Driven Marketing: Is the process by which marketing professionals gain insight into current status as well as trends based on in-depth Big Data analysis collected through consumer interactions and engagement to form predictions about future behavior.

Conversion Attribution: Is the attribution of a conversion to a particular channel along the customer path until the final purchase decision (i.e., conversion) is made.

Search Engine Optimization: For the internet search engines is a collection of activities aimed at increasing website traffic, which is achieved through improving the search engine placement of targeted keywords.

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