I have rich customer data for site usage, particularly the web analytics data. Some of the data fields I have are:

  1. Product Usage details
  2. Geographic details
  3. Session data: Capture what all they did during a session onsite, unique to timestamp
  4. User details
  5. Device used to login
  6. Product looked at/ downloaded
  7. Campaign data for promotion of a product
  8. Search queries made on the on-site search bar
  9. Results of those search queries

and so on.

I wanted to know what are the different types of analysis I can perform using this data, more inclined towards the Data science aspect of it. I have already thought of the following:

  1. Churn Analysis
  2. Campaign effectiveness
  3. Find out which product is being sold the most and which at which geography
  4. User behavior analysis and such.

Could the community help me come up with more ideas which i can use to perform analysis on this data?


  • $\begingroup$ What is the objective? $\endgroup$ Oct 10, 2018 at 6:49
  • $\begingroup$ I need to find projects which I can do for the next year. No particular project, but it has to have a good business, customer and revenue impact $\endgroup$ Oct 10, 2018 at 14:25

2 Answers 2


Recommendation Engine is one such application of ML that can be used to extract the useful information from the user available data and offer them the product that is the most relevant to him. You can map this to classic classification problem where expected output of ML algorithm is to come up with the product that customer will buy.


I had some interesting results conducting association rules analysis on website behavior i.e. which pages or content are looked at together and what can I learn from this in regards to improving content, user flow and even user classification.

This is similar to a shopping basket analysis but instead of.purchased products you look at read pages.


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