Authentication Customer Segmentation — BEACON: K-Means Clustering

2026-02-13 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cybersecurity & Data Privacy · Depth: Intermediate, medium

Summary

BEACON (Behavioral Evaluation for Authentication Cohorts & Outcomes Network) is a data-driven framework developed to segment customers based on their authentication behavior using k-means clustering. This system groups users along interpretable dimensions like engagement and risk to provide a consistent, global understanding of login patterns, fraud, and retention outcomes. The project aimed to identify distinct authentication cohorts, link them to measurable outcomes such as successful logins and fraud reduction, and enable teams to act on these segments via a shared dashboard. Feature selection for BEACON involved a business-first, data-validated approach, distilling raw login signals into three non-redundant dimensions: average sign-in attempts, sign-in success rate, and number of risky logins. The optimal number of clusters, determined by evaluating inertia and Bayesian Information Criterion (BIC), was set at 10, balancing compactness, interpretability, and model simplicity.

Key takeaway

For AI Product Managers designing authentication systems, BEACON demonstrates how ML-driven segmentation can unify understanding of user behavior across teams. You should prioritize features directly tied to business outcomes like fraud reduction and retention, and validate your cluster stability across different time windows and data samples. This approach allows for targeted interventions, reducing friction for low-risk users while enhancing security for high-risk cohorts.

Key insights

K-means clustering can segment authentication behaviors into actionable cohorts, balancing security, experience, and business outcomes.

Principles

• Segmentation must tie to clear business metrics.
• Data should validate business intuition in feature selection.
• Actionability, precision, and interpretability are key modeling tenets.

Method

BEACON uses k-means clustering on three dimensions: average sign-in attempts, sign-in success rate, and number of risky logins. Optimal 'k' is determined by balancing inertia and BIC, with stability checked via multiple random seeds and data samples.

In practice

• Use 30-day observation windows for stable behavior capture.
• Test cluster stability with different random seeds and data subsets.
• Build transition matrices to track user movement between segments.

Topics

• K-Means Clustering
• Customer Segmentation
• Authentication Behavior
• Fraud Prevention
• Feature Selection

Best for: Data Scientist, AI Product Manager, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.