A digital marketing manager needs information to be able to design an effective campaign. This information, in most cases, does not come from traditional market surveys, but must be derived from the analysis of data collected for the most diverse purposes: website logs, sales data, digital advertising services such as Google Analytics, etc ... Sometimes the bulk of raw data is so vast that it is necessary to resort to distributed computational mechanisms: we enter into the realm of big data.
Obviously, the professional figure that is normally in charge of this type of analysis is not the digital marketing manager but the data scientist. However, it is important that also the managerial roles have a basic preparation in the field of data knowledge extraction, both in order to communicate more effectively with the roles in charge of this activity, and because, in medium-small contexts, a specific role of data scientist may not be available and the manager could find himself in the position of having to make up for this lack. In any case, having a basic preparation in the field of data mining is useful to learn about the potential and limits of the tools you have available. The aim of the course is therefore to provide this basic knowledge, as detailed in the section on course content.
More specifically, and referring to the Dublin descriptors, at the end of the course the student will be able to
KNOWLEDGE AND UNDERSTANDING:
• explain clearly and correctly the main concepts of data analysis and data mining;
• accurately illustrate the behaviour of the main analysis algorithms.
APPLYING KNOWLEDGE AND UNDERSTANDING:
• perform simple data analysis using the KNIME software;
• extract information to support marketing strategies through data mining methods.
• choose the most appropriate type of analysis based on the objectives and the types of data available;
• interpret the results obtained from the data analysis, without having to resort to external experts.
• communicate with any data science expert using a correct and appropriate language;
• ommunicate with other managerial roles the results of data analysis in a simple but correct language, making use of understandable and easily interpretable graphics.
• read and understand basic and advanced level (but not research level) papers on data analysis methodologies;
• read and understand papers on the applications of data analysis techniques to support marketing strategies.