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I am trying to do some exploratory analysis of a very large dataset. This is all about the browsing behavior of several customers. All the customers visit a particular store (belonging to a retail chain) and we know what websites they visit when they are physically present in the store (and also when they were not present in the store).

It is not very clear what kind of actionable insights might be had from this dataset and what kind of analysis might be done. Any constructive suggestion is welcome.

Thanks and regards,

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Yes, some really nice actionable insights can be derived from the data set.

Some which I can think of are:

  1. Personalized recommendations: You can have a look at this thread for a fair idea about the concept. This thread for the algorithmic intuition behind recommender systems. Depending on the time the user is browsing a particular website and his most preferred items, the products can be ranked(in terms of placement) on the website.
  2. Time Series analysis of sales performance: The sales performance can be analyzed through a time series and insights can be developed from the exploratory analytics and plots.
  3. Dynamic personalized offers and recommendations: The browsing behaviour and click analytics of the user can be overlapped with the time of the day and the corresponding recommendations for the users to offer customized real-time offers for the user.
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