COHORTNEY: Non-Parametric Clustering of Event Sequences

TL;DR

COHORTNEY is a novel non-parametric, machine learning-based algorithm for cohort analysis that effectively groups internet users based on their activities, providing a robust theoretical foundation and outperforming traditional methods.

Contribution

This paper introduces COHORTNEY, the first machine learning-based cohort analysis algorithm with a solid theoretical basis for unsupervised user grouping.

Findings

COHORTNEY outperforms canonical marketing and engineering methods.

Provides a robust theoretical explanation for cohort analysis.

Effective in unsupervised grouping of internet users.

Abstract

Cohort analysis is a pervasive activity in web analytics. One divides users into groups according to specific criteria and tracks their behavior over time. Despite its extensive use, academic circles do not discuss cohort analysis to evaluate user behavior online. This work introduces an unsupervised non-parametric approach to group Internet users based on their activities. In comparison, canonical methods in marketing and engineering-based techniques underperform. COHORTNEY is the first machine learning-based cohort analysis algorithm with a robust theoretical explanation.