TSIA Research Journeys are designed to solve today’s top problems in the tech industry. Join us as we launch discovery research, build practical theories, and deliver industry-validated, actionable insights designed to help empower your business.

Are you still relying on a basic, color-coded customer health score to predict churn? If so, your current model may be giving you a false sense of security and costing you customers and revenue.

Traditional customer health scores were once a helpful benchmark, but in a subscription-based world with complex, multi-touch customer journeys, they no longer keep pace. Today’s challenge is clear: you need a model that can predict churn risk with precision, not assumptions.

That’s why TSIA is launching the Customer Success Research Journey: From Metrics to Machine Learning—Reinventing Customer Health Models. This journey will provide you with the insights, frameworks, and real-world best practices to help you transition from outdated health scores to next-generation models powered by AI and machine learning.

We invite you to join us, participate and learn in real-time as this Research Journey unfolds.

So, join us as we:

• Define the Problem: We’ll uncover why traditional customer health scores are failing to predict churn, and what risks this creates for customer success organizations.
• Launch Discovery: Through surveys, interviews, and data analysis, we’ll explore how customer success leaders are navigating today’s complex customer journeys and the limitations of current models.
• Develop the Theory: With insights and industry data in hand, our researchers will begin building and testing frameworks for AI-driven health models that better predict risk and growth.
• Guide the Industry: We’ll connect these findings to real-world financial outcomes and deliver actionable frameworks that you can use to modernize your approach to customer health.

Key Takeaways

• Traditional health scores are often simplistic, outdated, and fail to provide a clear picture of churn risks.
• Next-generation health models, powered by AI and machine learning, can deliver predictive and actionable insights.
• By following this Research Journey, you’ll gain industry-validated frameworks, practical insights, and direct deliverables to help your organization move beyond outdated tools.

Why Traditional Health Scores Are Failing You

For years, businesses, especially subscription-based companies, have relied on rules-based health scores. You’re probably familiar with them: a simple, red, yellow, or green label, or maybe a numeric score pulled from a few usage metrics and team opinions.

The problem? These static, one-dimensional scores no longer reflect the reality of today’s customer journeys. Customers interact with your product across multiple touchpoints, user types, and evolving business needs. A simple score can’t account for these complexities, often leaving you with blind spots and unreliable forecasts.

And when those forecasts are wrong, the impact is profound:

• Customer success managers (CSMs) and account managers may miss early signs of churn.
• Leaders and executives risk misallocating resources and presenting inaccurate revenue projections.
• Your entire organization becomes reactive rather than proactive.

Outdated health scores could be costing you customers and long-term growth.

‍

Defining the Problem: The Limitations of Traditional Customer Health Models

The heart of the issue is that you’re navigating multidimensional customer journeys with one-dimensional tools. When health scores are too simplistic, they fail to capture real customer behavior.

The problem with these models isn’t just that they’re outdated—it’s that they can give you a false sense of security. A customer may appear “green” or “healthy” on your dashboard, while behind the scenes, churn risk is already rising. These static models miss critical signals, leaving you blindsided when an account suddenly disengages or defects.

Not solving this problem comes with clear risks:

• Missed churn warnings that leave your teams scrambling.
• Misallocated resources as your efforts are focused on the wrong places.
• Unreliable forecasts that ripple up to revenue leaders and the executive suite.

This is a challenge for everyone in your organization. Customer success managers and account managers face the stress of working with incomplete information, leaders struggle to allocate resources effectively, and executives risk presenting revenue forecasts that don’t align with reality.

If you’re responsible for customer retention, renewals, or revenue, this is a challenge you can’t afford to ignore.

How This Research Journey Will Address It

TSIA’s Research Journey will take you step by step through building a roadmap for next-generation health models. Here’s how:

• Analyzing industry data: Using TSIA’s historical data to identify predictive patterns and signals.
• Gathering insights: Conducting interviews and surveys with customer success leaders to uncover the practical limitations of current health scores.
• Highlighting real-world use cases: Learning from top-performing companies already leveraging AI and machine learning to develop predictive models.
• Delivering frameworks: Providing concrete, actionable blueprints to help you design and implement your own AI-driven health model.

The Research Journey will focus on how customer success organizations are navigating the revolutionary shift away from traditional, lagging customer health scores—which often combine adoption and consumption metrics with CSAT/NPS—toward AI-driven predictive modeling. 

Over the next several months, this journey will aim to define which metrics best predict risk or growth. It will investigate how AI is transforming customer success, moving beyond simply measuring adoption to proactively identifying at-risk customers and opportunities for growth.

Throughout the process, you’ll gain insights and deliverables from surveys and polls, as well as access to the reports, frameworks, and findings as they’re published.

What You’ll Gain by Participating

By participating in this Research Journey, you’ll:

• Receive direct deliverables from polls and surveys.
• Gain access to insights, frameworks, and reports.
• Learn from real-world use cases and best practices already in action.
• Get a competitive edge by replacing outdated health scores with predictive, AI-powered models.

You’ll also gain valuable benchmarking opportunities. Every poll, survey, and interview you take part in will give you a chance to compare your approach to peers across the industry. That means you can quickly see where your organization is ahead, and where gaps exist that need attention.

Because insights will be shared at every stage of the journey, not just at the end, you’ll build a practical roadmap for action as the research unfolds, ready to apply before you walk away. Whether it’s adjusting how your team interprets health signals or exploring emerging AI use cases from industry leaders, participation ensures you’re not just watching the research unfold—you’re part of it.

This isn’t just about improving your forecasts—it’s about giving you the tools to make better decisions, allocate resources more effectively, and ultimately, improve your bottom line.

Moving Beyond Outdated Customer Health Scores

Traditional health scores are no longer enough to guide your customer success strategy. They leave you vulnerable to missed churn signals, wasted resources, and poor forecasts.

Through this Research Journey, you’ll see firsthand how to move beyond static metrics and toward dynamic, AI-driven models that actually predict and prevent churn.

We invite you to join us. Follow the journey in the TSIA Portal, participate in upcoming surveys, and gain access to the frameworks that will shape the future of customer health models.

FAQs

Why are traditional customer health scores no longer effective?

Traditional customer health scores are too simplistic for today’s multi-touch, multi-dimensional customer journeys. They often miss churn signals, leading to unreliable forecasts.

How can AI improve customer health models?

AI can analyze vast amounts of data across features, users, and behaviors, identifying predictive patterns that static models overlook. This makes your health models more accurate and actionable.

Who should participate in this Research Journey?

If you’re a customer success leader, renewals team manager, or revenue operations professional, this journey will provide you with the insights and frameworks needed to modernize your approach.

‍