iLab1 – Personal Learning Objectives

iLab1 Personal Learning Objectives

My iLab subject is an internal project within my company at Fairfax Media.


Project Problem Statement and Description:

Fairfax Media publications are split into 2 main categories: Metropolitan or Community mastheads. The way the audience for each of those categories are consuming contents may be very different from each other or on the contrary may present some similarities for some specific type of contents. The objective of this project is to provide a better understanding of the behaviors of these 2 groups regarding news consumption.


My 3 personal learning objectives for this ilab are:

Web Analytics

  • I am very keen to understand more about how solutions such as SiteCatalyst or Google Analytics track the usage of digital websites by online users. It will be interesting to see what kind of information is automatically recorded and what are the key measures related to this area. Even if this part isn’t really in the scope of the problem statement the way a website is designed may have a significant impact on how it is used by the final user.

How to profile Customers Behaviors

  • I am very interested in learning techniques to understand customers behaviors based on their actions. This is mainly related to association rules machine learning algorithms and we haven’t seen any so far so I really want to learn more about them. I always heard about the example of a data science project that found the correlation between beers and nappies purchasing. This example is actually one of the reason why I wanted to learn more about Data Science so I am really looking forward to have a deep dive into this area.
  • I recently read some articles talking about graph analysis that may also fit the purpose of this project. I am not sure yet if this is relevant or not but if I got time that will be an area where I want to learn a bit more.

Clustering and Market Segmentation

  • Finally the final learning I am expecting from this iLab is about how to group similar type of customers. I have already use some clustering algorithms but they were all for continuous data. Depending on the data set I will get I may need to find some that can handle count or categorical variables. As a second step I would like also to see how those results relates back or not to the current marketing strategy of my company. There is no strong expectations internally for this project at the moment so it is more like a research exercise but I would still like to know if this can create or not values for the company.

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