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What You're Missing in Your Customer Health Scores

February 24, 2023
August 1, 2022
October 3, 2022

One of the most essential parts of being a successful SaaS company is retaining and growing your customer base. A good customer health score is one of the best ways to monitor your customers and identify any factors that could negatively impact the business, and identifying a low customer health score early on in the process is crucial to reducing churn and keeping customers satisfied. 

Customer analytics, while helpful, can often cause the signal to be drowned out by the noise of data overload; not having a solid customer segment strategy in place makes customer analytics often counter-productive.

But every company has its own unique way of measuring customer health; how are you supposed to know what works best for your team and your business? 

More importantly, what even constitutes a good customer health score? 

*Get a head start on Customer Health Scoring with our free Customer Health Scorecard Template*

Let's dive in.

What Is a Customer Health Score?

Customer health scoring is a critical tool for customer success and customer service teams to keep track of the status of their customer accounts and understand how they're feeling about their experience. 

Monitoring customer health allows a customer success manager or customer service teams to always understand what their customer is looking to get out of the product versus what they're actually getting out of the product.

By monitoring customer health, managers can predict the likelihood of renewal or churn from that customer and intervene if necessary.

Every company measures customer health differently, and it's crucial to adapt your customer health scoring to fit your teams' needs. Still, it generally means giving a customer a score based on how likely they are to reach a valuable target. 

This could mean renewing their contract, not churning throughout their contract, or being upsold. This can serve as a warning sign to intervene with a customer to save the relationship or show you that a customer may be ready to add a seat or add an additional feature.

A Customer Health Score is only one type of score that can be used on customers. While effective, there are more robust predictive scoring models that should be considered as well.

Core Customer Health Dimensions

All humans are different, so every team and every customer has different needs (revolutionary, I know). As a result, the customer health scoring method at a seed-stage startup is inherently going to look different from the customer health scoring method at a global Series D company, and that's because different aspects of a customer's account have varying levels of importance to teams. 

Typically speaking, most customer health scoring models contain at least one of these five core dimensions: 

The Five Core Customer Health Dimensions

Five dimensions of a customer health score

Financial Signals

These can be any leading indicator that is purely related to money. 

For example, when did the customer last renew its contract? 

When was the last invoice sent, how much was it for, and how long did it take for them to complete it? Has that customer account ever purchased an upsell? If so, what did they purchase, and for how much? 

Obviously, teams are typically tracking much of this information anyways, so utilizing it to better understand your customer's feelings toward your account can help you predict their financial behavior in the future.

What are Some Financial Customer Health Metrics?
  • Customer Lifetime Value
  • Total Contract Value
  • Customer Acquisition Cost
  • Expansion Revenue

Product Usage Signals

Product usage is a critical indicator today for understanding a customer's intent. Their behavior in the product - how they use it - will be either a blinking red or bright green light to a customer success team. 

These days, most of the customer journey happens within the product, so customer's usage - or lack thereof - of your product is one of the best ways to gauge a healthy customer.

Customer behavior and product usage are critical areas for revenue and retention teams to track. More critically, when these these behaviors signal a churn risk or potential upsell, it's important that a team member is alerted right away so they can take the actions that are necessary to achieve a positive outcome.

Tracking product signals can give your Customer Success team a great overview of how your customers interact with the product, providing insight into the value they're receiving from your product.

What are Product Usage Customer Health Metrics?
  • Customer Churn
  • Activation Rate
  • Feature Adoption Rate
  • Lead-to-Paid Conversion
  • Product Qualified Lead (PQL)

Service Signals

Service signals are primarily related to the Customer Support team. For example, how many support tickets have a customer account submitted in the past month? 

How many of those tickets are still unresolved? How often has a user interacted with your live chat bot to ask a question or report a bug? 

Service signals indicate how much a customer account struggles with your product and what areas may be improved with a walkthrough tutorial or an FAQ section.

Example Service Metrics for Customer Health Scoring
  • Support Tickets: Total, Unresolved, Frequency
  • Bug reports

Customer Sentiment Signals

Customer sentiment is another signal that plays a big part in a customer's overall health scoring. Customer sentiment can be more nuanced than customer satisfaction and service signals, which are more explicit. Nevertheless, customer sentiment is a critical part of increasing customer retention.

NPS score is the critical sentiment signal for indicating whether you are dealing with a healthy customer or not. While not exclusively related to service, Net Promoter Score surveys are generally surfaced in the customer journey through the customer success team.

As a result, the survey results will reflect heavily on how customers perceive their service and support.

Customer Satisfaction Score, or CSAT, is another customer sentiment signal based on explicit feedback from customers. Taken along with NPS scores, CSAT  

Example Customer Sentiment Metrics for Customer Health Scoring
  • Net Promoter Score (NPS)
  • Customer Satisfaction Score (CSAT)

Human Input

Lastly, an important signal to track is simply human input.

For example:

  • What do your CSMs, who are interacting with the accounts, think about the state of the customer's health?
  • What about your support team?
  • Who is the frontline for dealing with any issues that arise with customer accounts?
  • Or your product team- what are they incorporating into their roadmap, and how will that affect the existing customers and their experience within your product?

Effective communication with your teammates can help give you a better indication of where the product is headed and give you a different perspective about your customer accounts that you may not have had before.

Predictive Scoring Automation

The Importance of Customer Health Scores for SaaS Companies

Monitoring customer health in a streamlined, effective way is crucial to uncovering potential issues early enough to intervene before they lead to a larger problem. 

If you're waiting until the customer comes to let you know that they won't be renewing their contract, it's too late. Being able to proactively identify signals that could indicate that the health of a customer account is dipping allows your team to act swiftly to resolve any issues and save the account.

But, as we said earlier, every customer account is different, and every product is different.

Tracking product signals that solely focus on engagement levels probably isn't going to be super effective for software designed to be used once a month.

While the specific metrics and goals may change to fit the teams' needs, the general process for customer health scoring remains largely the same. 

Let's take a look.

How to Measure Customer Health

1. Choose an outcome

The basis of your customer health score depends on the outcome your team is targeting. 

  • Are you looking to reduce customer churn? 
  • Increase renewals? 
  • Increase upsell opportunities? 
  • All of the above? 

Selecting the outcome that matters most to your team is a crucial first step, as it will determine which core behavioral signals to look at later. For example, suppose you're looking to increase upsell opportunities.

In that case, you're probably going to want to track engagement levels in certain areas of your product to see what customers find the most beneficial, as well as relationship signals with the customer account; if you recommended a new feature to purchase, would they trust you enough to move forward with it?

Deciding on this outcome will be different at every company. Maybe one team has noticed that customers are not renewing their subscriptions after the first year, and they want to fix that, so they would choose the likelihood a customer will renew as their outcome.

Another team may see a high volume of churn and choose to target the probability that a customer will churn.

This decision will be heavily dependent on the situation that your business is in and the goals that your team wants to achieve.

One of the first things this decision requires is a finger on the pulse of your customers.

Without understanding how customers interact with your product, their buying cycles, and the reasons why your team is targeting a particular outcome, you won't be able to craft an accurate picture of your customer's health.

2. Define behavioral signals

Alright, so remember those behavioral signals we told you about earlier?

After your team has landed on a specific outcome you're looking to achieve, it's time to decide how you want to achieve that outcome and what metrics to track to get there.

Selecting the proper behavioral signal to track to lead you to the desired outcome is crucial to properly monitor customer health. These signals tell you whether a customer account is getting closer or farther away from your desired outcome.

Let's walk through a quick example.

Say that you and your team decided to target increasing upsells for customer accounts as your desired outcome.

What can you see that may make a customer more or less likely to buy additional software functionality from you?

Product Signals:

  • Are your users actively engaged in the product?
  • Is it a tool that they need to use every day?
  • How much of the product are they using? What areas are they using?
  • Is their most commonly used functionality similar in nature to the additional functionality you want to sell them?

Financial Signals

  • How much is the customer's current contract versus the contract of a similarly sized customer?
  • Did your customer recently get a round of funding?
  • Have they been interested in bundled discounts in the past?
  • Are they looking to invest more money in a specific area of their business?

Sentiment Signals:

  • How is the customer feeling about their overall experience with the product and the service they've received?
  • What was the last CSAT score of that key decision-maker?
  • What was the last NPS score?

Support Signals

  • Is the customer getting frustrated by the product?
  • How many support tickets have they submitted?
  • How many of those tickets are still unresolved?

Relationship Signals

  • How is the relationship between the key decision maker on the account and the CSM assigned to that account? How frequently are they communicating?

These are just a few examples of the types of behavioral signals your team could potentially track to reach your desired outcome of increasing upsells.

Some of these may be more relevant to your product than others, so it's important to adapt them to fit your needs and weight certain signals more heavily over others, which is the next step in this process.

3. Weight signals

Not all behavioral signals are created equally. After you've selected your desired outcome and identified the appropriate signals to track, it's time to decide which signals carry the most weight toward the outcome that you are targeting. 

Keep in mind that these weights can change based on experimentation as you monitor customer health.

For example, maybe you realize that a financial signal is actually a better indication of poor customer health than you initially believed. In that case, you would adjust the weighting system to accordingly.

Staying flexible with your customer health score as you progress is crucial to gathering the most accurate score possible and improving over time.

4. Create a health scale

The last step in this initial customer health scoring process is to, well, assign them a score! 

Create a health scale on which you can give customer accounts a score based on the behavioral signals you've selected and the weights you assign to each signal. 

One of the most common scales teams will use is just a standard 0-10 scale (or 0-100), which you can easily sort into buckets. 

So anyone with a score between 0 and 4 is considered "Unhealthy", or "At-Risk" for churn; 4 through 7 would be "Neutral" or likely to renew but not happy enough to upsell; and 7 through 10 would be considered "Healthy", or likely to renew and could be receptive to upsells.

Using a simple health scale like this allows you to assign customer accounts a customer health score that anyone on your team can easily and immediately understand. It also allows your team to proactively identify accounts that may need a little bit more attention before it's too late.

5. Track customer health scores with a Customer Health Scorecard

With the decisions you've made in the previous steps, take your signals, weights, and scales and create a customer health scorecard to keep track of customer health - on the account level, as well as across the organization.

This can be done in a variety of CS, Sales, Marketing, or Analytics tools.

Or, you can use a spreadsheet to build your own.

Want a head start? Check out our Customer Health Scorecard template here!

Once every one of your customer accounts is assigned a health score, your work isn't finished.

Health scores can change dramatically and frequently, so it's important to closely monitor every single customer account to make sure their customer health score is accurate and up to date.

That means continually checking every behavioral signal regularly (every week/month/quarter/year, depending on the signal and your product) to ensure that you have an accurate read of every customer's health at any given moment.

This is also where that flexibility comes into play.

As time goes on, your product or your desired outcome may change, and you'll need to update your customer health scoring model to reflect that.

Additionally, suppose the key decision maker at any given customer account changes.

In that case, that will affect the customer health score, and you'll need to check in and get a pulse for how they're different. So continually monitoring and updating your customer health scoring model is the ideal way to ensure that every account is accurately measured and that your method for collecting information reflects the outcome you're trying to reach.

Make Your Customer Health Score a Priority

Every company, team, product, and customer is different, so there really is not one completely accurate way to measure and track customer health score.

Adapting your customer health scoring method to fit your teams' needs means that you'll be able to accurately understand how your customers are feeling about your product at any given time, and can give your Customer Success team the ability to proactively anticipate the likelihood of churn, renewal, and upsell opportunities and act accordingly.

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Mark Lerner

Head of Marketing @ Parative, the Customer Behavior Platform. SaaS enthusiast, B2B Marketing Specialist, Startup Survivalist. Dad x2.

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