Goal and applications

This article will show a method for segmenting website navigation sessions according to the pages visited, using a topic modelling approach.

There are some possible applications for this: descriptive analysis, segment-oriented marketing, custom website navigation patterns and so on. It can also give you a sense of how people use your website, what types of content are often seen together, etc.

You could also try the same approach to segment users instead of sessions, and target those segments differently in marketing campaigns, or cross this data with your existing transactional segments, to understand how each of those uses your website.

Clustering

Clustering means grouping objects by their similarities. There are many ways of doing it, also because there are many different definitions of what a cluster is. The common denominator is that a cluster is a group of data objects.

Hard clustering methods classify each object as belonging to only one cluster; whereas soft clustering gives these objects a degree to which each object belongs to a cluster (using, for instance, the likelihood).

Topic modelling

Topic modelling, on the other hand, is used to infer topics from texts, based on words that appear together often. One algorithm that can be used for this is the Latent Dirichlet Allocation (LDA). In the LDA, we give it a set of documents, we set the number of topics we think there are, and it returns us a list of topics, each one described by a list of the words that best identify them.

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