In an ideal world, a customer who wasn't satisfied with your product would tell you they were thinking of leaving, giving your team an opportunity to win them back before they churned.
However, in the real world, customers frequently leave without warning — even customers that seemed perfectly satisfied throughout your relationship.
That's why SaaS businesses use Customer Health Scores, a tool that helps businesses predict churn and gauge how healthy a customer's relationship is with their product.
Many Health Scoring models exist, with some being more reliable than others. We think Health Scores work best when customers are scored uniquely based on contextual factors, namely where they land in the customer lifecycle. Here’s another way to think about it: You can’t use the same Health Scores for all lifecycle stages because a customer’s behavior changes as they mature with your product.
With that in mind, we wanted to share our best-practice guides to building customer Health Scores by customer lifecycle stages (i.e. Onboarding, Adoption, Maturity, and Renewal). In this blog post, we’re walking through our methodology for building these guides.
Breaking Down Our Methodology
What Is a Customer Health Score?
A Customer Health Score is an index made up of several key performance indicators (KPIs). Health Scores give you an overall idea of how healthy a customer’s relationship is with your product. They also help your Customer Success team predict future customer behaviors like renewing or canceling subscriptions.
The TL;DR? Customer Health Scores are most often used by Customer Success teams to determine if a customer or account is "healthy" or "at-risk."
The Importance of Health Scoring by Customer Lifecycle
Each customer segment is unique, so why score them all the same way? To most effectively understand the health of your customers throughout their journey, Health Scores should ideally be calculated and weighted distinctly for each stage of the customer lifecycle.
So, why can’t you use the same health scores for all lifecycle stages? Simply put, a customer’s behavior changes as they mature with your product.
Your customers will naturally take different actions in your product when they first sign on vs. the actions they will take after using your product for a while. For example, you won’t see high usage rates from a new customer who’s setting up your product. On the flip side, you wouldn’t expect a long-time customer to still be enabling many new integrations.
Not only will customer usage differ from stage to stage, but the kind of usage that matters will also change from stage to stage. For an onboarding customer, we may not yet care about how much usage they are driving, but more so that they have turned on and set up certain modules or key integrations, while the inverse may be true for mature and established customers.
Calculating health scores uniformly without regard to lifecycle stage — or even other aspects, like industry, size, etc. — is a recipe for creating an unreliable, non-trustworthy, and non-actionable set of Health Scores.
Assigning a Scale to Your Customer Health Scores
Ultimately, Health Scores should be easy to use. So while you may need to put in a little extra work upfront to ensure that your Health Score model is reliable, once you’re up and running, the goal should be a customer Health Score that your entire team can understand at a glance.
To do so, you’ll want to create a common scale for each of the metrics that make up the score. If we don’t, the calculation would be impossible. The good news? It’s pretty easy to do. Your scale could look like this: three categories, and each gets 0, 5, or 10 points: Poor (0 points), Concerning (5 points), and Healthy (10 points).
From there, decide what poor, concerning, and healthy means for each metric. There’s no hard and fast rule here — a product usage rate of 5 might be healthy for your business but poor for another.
Weighting Your Health Scores
Using a weighted average of the metrics in your customer Health Score will make that score a much better predictor of customer behavior. Why? Because some metrics naturally hold more "weight" than others. You can then calculate an account’s overall Health Score as a weighted average of each Health Score.
Here’s an example that speaks to the importance of weighting your customer Health Scores: What if you noticed that 85% of your churned customers had a product usage rate of less than 5? That’s a strong correlation. Meanwhile, you’ve noted that only 40% of churned customers had a low CSM pulse score — a much weaker correlation. In this case, product usage rate is a better predictor of customer churn. In this case, you’d want to give product usage rate more weight in your customer Health Score.
Two things to note when weighting customer Health Scores:
- Weights must add up to 100%
- Your customers' current health score is only a small part of the story. The real insight is in the trend over time.
Tracking Product Champions separately
Your product champion is the most influential user of your product. They either made the buying decision or heavily influenced the person who did. By not tracking the actions of a product champion separately, your Customer Success team may have a false read of customer health and miss the chance to identify a churn risk. To solve for this, track the behavior of the product champion separately from the overall account.
Taking Change Over Time Into Account
We've said it once (maybe more than that), and we will say it again — when it comes to customer health, the real story is in the trend, not the current state.
Consider this example: let’s say your team has agreed that any customer health score over 7.5 is healthy, and today, your customer has a health score of 7.8. They're healthy. So, no action needed, right? Compare their Health Score today to that of last month where you see their Health Score was 9. Why did it slip?
If you're only looking at the current state and not the trend, you and your team would never think to ask that question. That's why you have to track trends over time. You could do this in a spreadsheet or with an automatic tool, but what's even more helpful is having a setup where you can trigger automatic, proactive actions to notify your team and act accordingly.
Normalizing Usage Rates for Population
When it comes to your customer Health Scores, we’re defining population as the number of people using your product at a given company. Sometimes referred to as “seats” or licenses. To normalize the usage rate for population, divide the number of times all the users on an account use your product in a meaningful way by the total number of people using it at that company. If you don’t normalize for population, you’ll incorrectly interpret the health of a user and miss a potential churn signal.
It’s also important to note that while how much a customer uses your product directly correlates to customer health, you can’t just look at today’s usage. You need to compare it to the trend over time to know whether a customer is healthy or a churn risk.
Customer Health Scores in Vitally
Customer Health Scores can get pretty complicated, but they don't have to be that way. After all, your Health Score won't be effective if your team can't understand it.
With Vitally's Customer Success platform, you can automate key factors of your Health Score model like segmentation, change over time, weighting, and beyond, to give your Customer Success team a new level of insight into the health of your customers. Request a personalized demo to learn more about our Health Scoring capabilities.