No business can thrive without a clear understanding of its customers' health. Customer health scores act as the early warning system for retention and growth, helping businesses identify risks, maximize expansion opportunities, and drive long-term success.

From tracking usage trends and engagement levels to measuring value realization, customer health scores offer a data-driven approach to understanding customer relationships. 

But while many companies attempt to implement them, few get them right – leading to misleading insights, missed churn signals, and ineffective interventions.

So, what makes a customer health score truly effective?

In this guide, we’ll break down:

  • Understanding customer health scores
  • Why traditional customer health scores fail
  • How to build an accurate and actionable health score
  • Best practices for taking action on health insights
  • The importance of continuously refining your scoring model

Understanding customer health scores and why they fail

Customer health scores are a key metric used to assess the likelihood of a customer churning, renewing, or expanding their relationship with your business. 

The most accurate ones blend both quantitative and qualitative data, allowing organizations to proactively mitigate churn risk and identify growth opportunities. 

These scores are typically presented as a numerical value, often categorized into red, yellow, and green – terminology that many customer success teams are already familiar with.

Why traditional customer health scores fail

Despite their potential, many organizations struggle to make customer health scores effective. Here are the key reasons why they often fall short:

1. No alignment with the customer journey

One of the biggest issues is the lack of alignment with the customer journey. If your organization hasn’t clearly defined the customer lifecycle – from onboarding and adoption to advocacy – it becomes difficult to take meaningful action when a score indicates risk. 

The first step should be assembling a cross-functional team to map out who is responsible for each stage of the customer journey. Without this foundation, even the best health scoring models will be ineffective because you won’t know what proactive steps should have been taken in the first place.

2. Data silos prevent a holistic view

Customer data often resides in multiple systems, making it difficult to get a comprehensive view of customer health. If your data isn’t unified into a single, accessible view, your customer health scores will lack accuracy and usability. 

Instead of driving proactive action, they become just another static report, leaving teams in a constant reactive state.

3. Over-reliance on manual processes

If you’re building customer health scores in a spreadsheet, you’re already missing out on critical opportunities to mitigate churn and drive expansion. 

Manual processes are slow, prone to errors, and don’t allow for real-time insights. Automation is key to ensuring your scores are up-to-date and actionable.

4. Too much focus on either qualitative or quantitative inputs

Another common pitfall is relying too heavily on either qualitative or quantitative data. Many organizations lean too much on usage metrics, assuming that high engagement equals high retention. This often leads to what I call watermelon customers – green on the outside, red on the inside. 

These customers may appear healthy based on product usage but ultimately churn for reasons that weren’t captured in the scoring model.

On the other hand, relying too much on qualitative inputs like surveys or customer sentiment can be equally misleading. Feelings and perceptions, while important, don’t always translate to real business outcomes. Striking the right balance between qualitative and quantitative data is crucial.

5. Misplaced trust in NPS as a key health metric

Many companies use Net Promoter Score (NPS) as a core indicator of customer health. We did too at Brazen – our CFO closely tracked NPS month over month. 

But a study conducted by Greg Danes, also known as the Churn Doctor, revealed something surprising: there is no correlation between NPS scores and customer retention. This was a major revelation, as NPS has long been viewed as a gold standard in customer success.

Similarly, customer satisfaction (CSAT) surveys were also analyzed, and the results were even more counterintuitive. The study found that while there was a 12% increase in CSAT, those same companies experienced a 22% decrease in net retention. 

In other words, making customers feel satisfied didn’t necessarily keep them from leaving.

Shifting the focus: Why do customers stay?

Rather than asking why customers leave, Greg Danes suggests that organizations should be asking, why do customers stay? This shift in perspective can lead to a more meaningful and predictive approach to customer health scoring.

By identifying the specific factors that contribute to long-term customer retention, companies can build health scores that truly reflect business outcomes, rather than just surface-level engagement or sentiment.