Marketing Strategies based on Data Sets

Course title
Marketing Strategies based on Data Sets
Course tag
11425
Semester
2
Course status
Mandatory
ECTS
5
Lectures
30
Practice
30
Independent work
90
Total
150
Teachers and associates
Assistant Professor PhD Goran Klepac
Assistant Professor PhD Sandro Skansi
The course aims
The aim of this course is to provide students with an overview of modern technology for data processing in a business environment. Particular emphasis will be placed on technologies derived from the artificial intelligence used by the most important global companies in various industrial branches. These technologies and their features will be presented from the perspective of both the company and the products in which they are implemented, but also from the perspective of their service providers who want to optimize their placement in their services, which is popularly known as SEO ( search engine optimization). Also, students will learn how to build sales techniques like cross-sell and up-sell over the data with the help of machine learning.
Content
Basic terminology. Definition of marketing and marketing differentiation from advertising. The role of analytics in marketing. Disruptive events and the occurrence of data science. The main approaches in defining large data sets. Modern Internet Technologies. Economic models of free and open software. The idea of monetizing data and some solutions. Classification as a method of decision making. Evaluation of Classification. Basic concepts of machine learning. Machine learning demonstration. Collecting Data and Collecting Clients. Data-based sales techniques. Machine learning in big companies and their specific challenges. Corporate machine learning from the user perspective. Large and small sets of data.
Literature:
Course handbook prepared and printed by Algebra University College
Supplementary literature

Minimum learning outcomes

  1. Characterize the monetary value of data in terms of marketing and analytics
  2. Determine the importance of a particular evaluation tool for classifiers
  3. Describe the application of machine learning to some contemporary business problems
  4. Describe how big companies use machine learning

Preferred learning outcomes

  1. Establish access to collection, processing and storage of data from non-classical digital sources for marketing research purposes
  2. Critically evaluate the relationship between evaluation metrics
  3. Design an innovative business application of machine learning
  4. Conduct SEO with regard to the target company