Introduction to Marketing Analytics | E-Learning University of Athens

Introduction to Marketing Analytics

Introduction to Marketing Analytics
Introduction to Marketing Analytics

Every day, all businesses face dozens of questions relating to making decisions about their operation. Questions concerning the allocation of staff, the expected demand, the volume of orders, product storage, product promotion, customer acquisition, customer assessment, the effectiveness of any advertising, transportation of materials, processing of data, finding and collecting data of interest, etc.

The answer to such questions has a significant benefit, since it dramatically reduces the company's operating costs and makes it competitive and viable, especially in a time of crisis, when financing is not easy to find and therefore everything should be organized and exploited to the fullest degree.

This course elaborates on the basics principles of the marketing analysis while  providing an introduction to the concept of Business Intelligence (BI), its impact and its basic uses.

Note: During August the participant will not be provided with educational content and educational support. The training process will be active again by 04/09 

Professor, National and Kapodistrian University of Athens, Department of Economics

Higher education and pros with relevant interests and work experience in marketing analytics are considered as prospective candidates. However, students or high school graduates can also attend the course.

This programme addresses current issues on marketing analytics while ensuring an understanding of the basic notions and principles of business intelligence. In particular the course aims to present the  most basic data mining techniques that are utilized for the classification and the displacement of a company’s target customers. After completing the course, the student will be aware of the useful data that  should be collected  for costumers while he/she will become familiar with  up to date directed and non-directed data mining methodologies.

Module 1 – Introduction to business intelligence

Lesson 1: Introduction
This lesson provides an introduction to the concept of Business Intelligence (BI), its impact and its basic uses. It begins with an overview of BI, including a discussion on what is BI, what are the differences from other technologies and the role BI plays in organizations. Examples borrowed from various organizations are used to illustrate and explain these aspects.

Lesson 2: The architecture of a Business Intelligence System
This lesson describes all key functional components of a Data Warehouse/Business Intelligence Architecture (BI). First, an overview of a typical business intelligence architecture is made. Then, there is a focus on key operational processes of the BI system, describing the flow of data from the primary information systems to the end users and the transformations of corporate data across the spectrum. The lesson also describes the tools, techniques, and platforms that support the stages of this flow.

Lesson 3: Business Intelligence Functions
This lesson summarizes the four main functions of Business Intelligence solutions: creation of organizational memory, integration of information, enhancement of business insight and presentation and dissemination of information. It begins with an overview of these four functions, examining the interactions between them. Then, focuses on each function separately, analyzing the factors that enhance their importance and giving specific examples.

Lesson 4: Business Intelligence Technologies
This lesson refers to the main information technologies that facilitate the use of business intelligence. These technologies are classified into four categories, based on the four basic functions of business intelligence discussed in the previous section. Related technologies, such as ERP systems, data mining, OLAP analysis process, and balanced scorecard tables are described and illustrated.

Lesson 5: Development of Business Intelligence Systems
This lesson focuses on Business Intelligence solutions and the processes associated with their development. The lesson begins by examining the concept of Business Intelligence solutions and their desired characteristics. Alternative approaches to obtain Business Intelligence solutions and applications are discussed. Finally, two alternative development methodologies of Business Intelligence systems are presented, as well as specific steps in this development process.

Lesson 6: Business Intelligence System Management - Administration
This lesson examines the concept of Business Intelligence management, which includes all the actions needed to achieve the maximum value from the various business intelligence efforts of the organization. The BI Competency Center (BICC) is key to this process. The section begins with an overview of the challenges faced during Business Intelligence Management and how they can be addressed with the creation of a BI Competency Center. Then, we focus on the functions, the value and the use a BI Competency Center. Finally, we present a case study that illustrates the use of a BICC in a leading telecommunication company.

Lesson 7: Measuring Success
This lesson provides an introduction on how to measure the business value of Business Intelligence. It begins with an overview of the main reasons for measuring Business Intelligence, including a discussion on the definition of Business Intelligence failure and success. The diverse quantitative and qualitative measuring techniques of a BI system are presented. Finally, the various aspects of measuring the success of Business Intelligence are illustrated in a case study.

Lesson 8: Business Intelligence - Future Trends
This lesson focuses on current trends and future directions in the Business Intelligence sector. Like the entire industry of ​​software development, the sector of Business Intelligence is characterized by a constant focus on development. This development involves the use of the latest advances in database technology and modern trends in the field of business analytics. Among others, this section discusses the role of social media, big data, predictive analytics as well as the development of self-service models and cloud computing models.

Module 2-Data mining in marketing

Lesson 1: Introduction to Data Mining
This lesson provides an introduction to data mining and its applications in the business world. Our objective is to give you an overview of the basic concepts in data mining. We hope that by the end of this section, you will be able to explain with ease what data mining is, and you will have a clear understanding of data mining applications in a company. But above all we hope that, by the end of this lesson, you will still have a smile on your face!

Lesson 2: Customer Relationship Management (CRM)
This lesson is an introduction to the concept of Customer Relationship Management (CRM) which will play a major role until the end of the book. In this section, we want you to understand the role of data mining in the company-customer relationship. Furthermore, we hope that you will realize the importance of measuring customer value and you will learn how to use data mining to calculate it. Also, we focus on what data to collect during the customer lifecycle.

Lesson 3: What customers do we want?
This lesson is an introduction to the basic applications of data mining in marketing. Initially, you will have the opportunity to learn about the evolution of marketing during the recent decades. In this section we want you to understand the importance of customer segmentation (market segments) and the identification of desirable customers (target customers). Data mining can play a very important role in finding the desirable customers. Finally, you are going to get a taste of how we can use data mining to decide which customer segments to target with a mass marketing campaign.

Lesson 4: Data mining as a continuous process
This lesson attempts to define the foundations of data mining as a process. Data mining has a beginning, a middle and an end. First, we will try to prepare you for all the small and big problems that can arise at any moment. We will also examine the best way to approach data mining. What are the steps you should do to start the process and what decisions should be made once launched? In this section we will be more practical. We will focus on the way data mining is used in actual business applications. Our goal is to help you gain a solid understanding of the different data mining styles, which will now be examined in greater depth. We will also take a brief look at some data mining techniques that you can use. Our goal is to make these concepts as clear as possible and we hope to achieve this by providing many real life examples. Enjoy your reading!

Lesson 5: Directed data mining
This lesson is dedicated to Directed Data Mining. Our goal is to explain the practical steps of directed data mining. In this section, we want you to gain insight into how to set data mining objectives, how to select the correct data to achieve your goals and how to assess the final model (data mining model) when data mining is nearing the end of a cycle. Since familiarization with the data is important, our goal is to take a deeper look into how to collect data and to maintain contact with them. Most importantly, we want you to realize the importance of assessing the model, so you can trust the mining results. Finally, we will see that there is a need to evaluate the final model once again, after you have used it in your business for a particular application. Of course, we hope the pages of this lesson will be pleasant to read!

Lesson 6: Directed data mining techniques
This lesson focuses mainly on directed data mining techniques. Our goal is to present some of the most basic directed data mining techniques. We will not enter into intricate details that may confuse you. Instead, we will try to keep the content as easy and accessible as possible. We will examine models such as similarity models, table lookup models, naive Bayesian models, decision trees, and neural networks. You should be able to thoroughly understand the concepts involved even if you do not know the exact details. There is no need to be good at math; we just expect you to understand the data mining techniques in broad lines. So enjoy reading this lesson and enter the magical world of data mining!

Lesson 7: Undirected data mining
This lesson focuses mainly on undirected data mining and undirected data mining techniques. Initially, we will try to clarify the differences between directed and undirected data mining, and the differences between directed and undirected data mining techniques. Our goal is to help you understand some of the most basic techniques used in undirected data mining. Techniques such as k-Means clustering, customer segmentation and agent-based simulation hide a certain magic that we will try to uncover! By the end of this section, we expect you to have a deep understanding of the basic principles. Our goal is to give you an overview, while at the same time to provide you some spicy information on undirected techniques. We hope you will deeply enjoy reading this lesson!

Lesson 8: Undirected data mining techniques
In this lesson we present case studies of business intelligence and data mining applications in everyday life. We have selected stories of businesses that explored data mining applications in real life and that have received high media coverage. These stories highlight the active potential of data analysis and reveal new aspects regarding the impact of data mining on society. For example, there is a growing debate about the fine line between effective use of data mining and misuse of data for violation of privacy. In some cases, critical ethical issues emerge. How moral is it to use data mining to target specific groups of citizens, customers or voters? We will examine all this and much more in the subsections that follow. 

In each module the learner will have to take and submit electronically the corresponding tests, according to the timetable given by the instructor. The grades of previous modules are communicated to the student before the closing date of the next module’s test. The grading scale ranges from 0 to 100%. Overall, the grade of each module is calculated as follows: the assessment exercises make up 60% of the grade and the remaining 40% results from the final assignment, which is composed at the end of the course if required.
The Training Certificate is awarded when the learner receives in all courses a grade higher than 50%. If the total score of one or more of the courses does not exceed 50%, the learner has the opportunity to retake these courses after completion of the educational process of the program. The grade earned in the review procedure is final for these courses, provided that it exceeds the one earned in the normal course of the educational process. In the opposite case the original grade is maintained. 

Online and distance learning at National and Kapodistrian University of Athens offers a new way of combining innovative learning and teaching techniques with interaction with your tutor and fellow students from around the world.

The e-learning programme is implemented via a user-friendly educational platform adjusted to the Distance Learning Principles. Programmes are structured as weekly online meetings; interaction with the programme tutor and other students takes place in a virtual learning environment. The programmes are designed to fit around your schedule; you access the programme whenever it is convenient for you.

The whole world becomes your classroom as e-learning can be done on laptops, tablets and phones – it is a very mobile method. 

The educational platform is a portal that offers access to electronic classrooms based on modern distance learning technologies. The computer based nature of training means new technology is being introduced all the time to help students engage and learn in a tailored way that will meet their needs. Each e-classroom is similar to a traditional teaching classroom. Students have access to e-classrooms with their personal code number in order to browse  the teaching material, to being informed about the latest news/updates and interact with their instructors. Moreover, through own personal e-mail account through which they can contact directly their mentors or the administration office of the programme and share any concerns or anxieties related to the programme and make the most form their experience.

In each programme, learners submit the corresponding tests, within the deadline given by their instructors. Final mark fluctuates from 0 to 100%. The overall grade in each programme is calculated based on the student's marks on assessment exercises that follow each unit  while their instructor might also assign them with a final assessment at the end of the programme. Finally, if the total score on one or more lessons of the programme does not exceed 50%, students can ask for reassessment.

During the programme students will be attending lectures and group seminars presented by academics and speakers from the National University of Athens as well as from other Universities, Research Institutes and Cultural organizations around Greece. In addition, when you study with us, you have online access to first-class resources relevant to your programme – plus you can browse material in other subject areas.

Every week e-learners are provided with the relevant material , either  video-lectures or  typed notes and relevant resources or a combination of both.

Interactivity, lower costs and our long experience guarantees that studying with us will offer a successful and rewarding experience. Finally, the access to a large variety of material and online resources available in each unit aims to excite your curiosity and guide you in studying further on your favourite topic will the online material which can be downloaded will give you the opportunity to quickly refresh your memory after the completion of the programme.