Overview

Challenges

This Research Journey focuses on the evolution of Customer Success (CS) organizations as they shift from traditional customer health scores towards AI-driven predictive modeling. Over a series of reports, surveys, and interviews, this initiative aims to explore effective metrics for predicting customer risk and growth, and how companies are leveraging AI technology to transition CS strategies from merely measuring performance to proactively identifying engagement opportunities and at-risk customers.

Summary

The article explores the limitations of traditional customer health score models in subscription businesses and highlights the need for more advanced approaches to customer health scores and risk management. It introduces TSIA’s Research Journey as a way to deliver actionable insights, frameworks, and real-world examples of AI-driven health models that better predict churn risk, capture customer complexity, and support smarter resource allocation.

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Included in the full report

Transition to Predictive Models:

Traditional health scores are inadequate, necessitating a shift to AI-driven models that predict churn more accurately.

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Understanding Customer Journeys:

Multi-dimensional customer interactions require advanced analytics to identify churn risks and growth opportunities.

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Real-World Application:

The Research Journey will showcase successful case studies where AI has transformed customer health models.

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Resource Allocation:

Accurate predictions are critical to effectively allocate resources and prevent mismanagement of customer success efforts.

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Continuous Improvement:

Participation in the Research Journey offers benchmarking insights, enabling organizations to compare their strategies against industry peers.

Read the full report on the TSIA Portal

Publication date:

October 21, 2025

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