A Graph-Based Platform for Customer Behavior Analysis using Applications' Clickstream Data
This provides a method for customer behavior analysis in e-commerce and applications, but it appears incremental as it builds on existing graph-based representations.
The paper tackles the problem of analyzing customer behavior from clickstream data by representing sequences as walks on a finite state automaton graph, showing that this approach groups sequences automatically and compresses data representation.
Clickstream analysis is getting more attention since the increase of usage in e-commerce and applications. Beside customers' purchase behavior analysis, there is also attempt to analyze the customer behavior in relation to the quality of web or application design. In general, clickstream data can be considered as a sequence of log events collected at different levels of web/app usage. The analysis of clickstream data can be performed directly as sequence analysis or by extracting features from sequences. In this work, we show how representing and saving the sequences with their underlying graph structures can induce a platform for customer behavior analysis. Our main idea is that clickstream data containing sequences of actions of an application, are walks of the corresponding finite state automaton (FSA) of that application. Our hypothesis is that the customers of an application normally do not use all possible walks through that FSA and the number of actual walks is much smaller than total number of possible walks through the FSA. Sequences of such a walk normally consist of a finite number of cycles on FSA graphs. Identifying and matching these cycles in the classical sequence analysis is not straight forward. We show that representing the sequences through their underlying graph structures not only groups the sequences automatically but also provides a compressed data representation of the original sequences.