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 provides an introduction to the concept of Business Intelligence (BI), its impact and most important uses, while it elaborates on the basic principles of marketing analysis.

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: the 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 by 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 in contemporary business operations.

In this section, you will realize the role of data mining in the company-customer relationship and you will learn the type of data that you need to collect during customer lifecycle.

Hence, you will understand the importance of measuring customer value and you will know how to estimate it using business analytics & data mining tools .

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 with 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 the 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 elearner will have to take and submit electronically the corresponding tests, according to the timetable given by the instructor. The grading scale ranges from 0 to 100%. 
The Training Certificate is awarded when the learner receives an average grade higher than 50%.  

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

The e-learning course is implemented via a user-friendly educational platform adjusted to the Distance Learning Principles. Courses are structured as weekly online meetings; interaction with the course tutor and other trainees takes place in a digital learning environment. The courses are designed to fit around your schedule; you access the course whenever it is convenient for you, however within the given deadlines.

The whole world becomes your classroom as e-learning can be done on laptops, tablets and phones as a very mobile method. Learning can be done on the train, on a plane or even during your trip to Greece!

The educational platform is a portal that offers access to electronic educational material based on modern distance learning technologies. The computer based nature of training means new technology is being introduced all the time to help trainees engage and learn in a tailored way that will meet their needs. E-learners have access to the educational platform with their personal code number in order to browse all relevant training material and interact with their instructors.

Moreover, an online communication system through own personal e-mail account is available in order to make the process easier and more interactive. Trainees can contact directly their tutors or the administration office of the course and share any concerns or anxieties related to the course in order to make the most of their experience.

Every week e-learners are provided with the relevant material, delivered either in the form of video-lectures, text notes and relevant presentations or as a combination of them. The educational material of the course is uploaded gradually, per educational unit. During the course, important info for the smooth conduct of the educational process, such as timetables for the submission of the exercises are announced on the Announcement section of the platform.

For successful completion of the course the e-learner should have fulfill her/his academic obligations, meaning should have submitted all corresponding assessment exercises and have achieved at least an average of 50% grade in the corresponding tests for each module. The score scale ranges from 0 to 100%. Finally, if the total score on one or more lessons of the course does not exceed 50%, trainees can ask for reassessment.

During the course trainees will be attending a training experience designed by academics and lecturers from the National University of Athens as well as from other Universities, Research Institutes and Cultural organizations around Greece.

Interactivity, flexibility and our long tradition guarantee that learning with us offers a successful and rewarding experience. Finally, access to a large variety of material and online resources available in each unit aims to excite your curiosity and guide you in exploring further your favourite topic. Part of the online material can be downloaded providing the chance to quickly refresh your memory after the completion of the course.

When will I receive the Certificate?

The Certificate will be sent to you electronically 30 working days upon completion, if you have no remaining academic or financial obligations. The Certificate will be also sent to you through traditional post services. Upon request the Certificate can be sent with the use of courier services. In this case, the relative cost should be covered by your side.