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How to Measure the Effectiveness of Customer Health Scores
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Customer health scores, also known as a ChurnScore, give Customer Success teams a deep understanding of a customer’s health and are a leading indicator that there may be an issue with an account or customer.
By customizing and weighing factors that are relevant for each part of the customer lifecycle, your ability to predict the likelihood of a customer leaving increases tenfold.
But a customer health score is only as good as the sum of its parts. Measuring the effectiveness of your customer health scores is essential to maintain their accuracy.
In this article, you’ll learn the why and how behind evaluating your customer health scores to sharpen your renewal forecasts and ensure significant customer behavior never falls off your radar.
What is a customer health score?
A customer health score is a customer insights value that indicates the likelihood that a customer will renew their subscription or service with your company.
Customer health scoring is the process of evaluating a customer’s overall engagement and satisfaction with a company and its product or service with a simple score. Companies score customer health in many ways, including assigning points; implementing rankings such as A, B, C, or D; or using a color-coding system such as green, yellow, or red to indicate a scale of good, average, or poor health.
Regardless of the method you choose, customer health scoring methodologies need both quantitative factors (such as product usage, support history, and service utilization) and qualitative factors (such as relationship quality, CSM sentiment, satisfaction) to be an effective KPI.
Why you should evaluate customer health scores
The first time you create your customer health score, it’s highly unlikely that you nail it – and that’s to be expected! The goal is to build a sensible baseline, not perfection, when starting out with health scoring.
Even if you do get your initial customer health score exactly right, it’s not a permanent measure. Change is constant. Your product, your processes, and your customer evolve over time. You should look at your customer health score as a living, breathing target – a continual work in progress that you adjust based on surrounding context changes.
To finetune your health scoring, you need to make ongoing adjustments to your model at regular intervals. As you accumulate more customer data, and tweak your scoring based off that data, the better your health score will be at predicting churn.
Don’t forget, your data is not perfect. You’re bound to encounter data anomalies and errors (false positives and false negatives) in your scoring model which you’ll need to recalibrate and correct.
How often you need to evaluate customer health scores
How frequently you evaluate your customer health score will depend on the size of your customer base, but we advise at least every quarter, or at maximum, every six months.
During that time, health score factors (that are based on features, use cases, processes, leadership, and KPIs) have likely changed which affect the metrics associated with your health score.
How many customer health scores you need
When measuring customer health, there is no “one score fits all” since customer behavior will differ across lifecycle stages.
You should consider customizing health scoring using segmentation based on lifecycle stage, product, engagement, company size, or industry.
For example, health scores based on lifecycle stage may include:
- Implementation
- Training and Configuration
- Adoption
- Year 1
- Year 2
You can also combine customer segments to create more specific health scores.
For example, you might combine elements of a product health score and a lifecycle-stage health score to create a “Product A – Year 1” health score. This health score is more stringent due to the fact that it’s critical to achieve initial adoption during the first year of a customer’s lifecycle.
You could also have a health score for a customer who uses Product A but is in their second contract year. This health score accounts for an overall higher volume of product usage, but less usage related to initial configuration and early-stage tweaking. For instance, if you have automation in your product, it might be that once the customer sets those automation rules, they only adjust the rules quarterly, which would reflect different behavior from when they’re in their initial year.
Another example is creating a health score based on tiered implementation: SMB onboarding versus Enterprise onboarding.
Explore weighting factors differently for greater insight into a specific product or service.
How to measure the effectiveness of customer health scores
The purpose of this exercise is to help you analyze your data and identify trends in an objective manner to ensure that your health scores reflect current information and processes. If you do not currently use health scores, you can use this resource as a framework to set up scoring for your team.
This exercise begins with being introspective. The very first question you ask should be: did the customers who I expect to leave, in fact, churn? This question always leads to a host of new questions: If not, why? And down the rabbit hole you go…
Other questions you ask yourself might be as simple as: “The customer had a low risk of churn but left. Why?” Or they can be as complex as: “We had two paths for customers. ‘Group A’ had a high risk of churn. We identified it at X point. We worked to get the customer to value. The customer left. Whereas, ‘Group B’ also had a high risk of churn, but we identified it a bit later. We worked to get the customer to value. The customer stayed. What’s the right turning point for identifying churn?”
With that said, we’ll now walk through the process of evaluating your health scores.
Note: Before you begin your evaluation, you want to ensure your team actively uses customer health scores. Otherwise, your analysis will be skewed by their partial or lack of usage. If you’re having trouble driving CSM participation, test making health scores part of a CSM’s compensation or another metric they are measured against to ensure proper attention is given to managing the health score’s outcome and accuracy. Customer health scores should also be discussed during internal one-on-ones or escalations to assess the health score’s impact and accuracy.
Pre-work: Compile your customer churn details.
It’s important to understand that without data you cannot have analysis.
If you don’t track churn, you’ll want to begin tracking basic datapoints like cancellation date, reason for cancellation, and win-back probability.
If you do track churn, you’ll want to track additional datapoints such as health score at time of churn, lifetime expansion, product fit, and number of renewals.
Step 1: Create the customer segment you want to analyze based on your health scores.
This step is designed to help you understand where churn happens on a high level. Start by identifying customers who have churned within the last six months. This customer cohort will become your base segment to analyze.
Your segment and timeframe may differ depending on your business model. For example, if you use a month-to-month model or have a very high volume, you may want to shorten your analysis parameters to customers who have churned within the last three months. If you use an annual or a multi-year model, you may want to look at customers who have churned within the last year. The important part is that you have enough customer data to analyze and get meaningful results.
The segment you create here will give you the gist of your churn: What’s your churn rate over the defined timeframe? What are the broad reasons for churn?
Step 2: Add your relevant data fields.
This next step will help you further analyze your data. This is where layering in datapoint like sales rep, CSM, lifetime value, and other information can be useful to identify general data trends. Remember, at this point, you’re still compiling an overview of your data.
Step 3: Review your customer health score data.
Only at this step do you begin to look at your health scores. You should now have a general and more specific idea as to where and why churn is occurring.
Review your health scores. Compare the data you have analyzed against the customers who have churned.
Have you identified any missing datapoints that should be included? Do you need to be more specific or more broad in your health scores to make them accurate? If you answered no to both these questions, then you can export your data from the system and use other tools (i.e., Excel or business analytics software) to further develop your dataset.
Lastly, gather this information into a segment with all the relevant datapoints that you think should contribute to your analysis.
Step 4: Analyze your customer health score data.
After ensuring you have all the required datapoints, you’re now ready to analyze. When examining your dataset, here are a few questions to ask yourself:
- Are there customers with a “low risk” health score who have churned?
- Are there tenured customers who were expected to renew based on CSM sentiment?
- Are there large areas where churn is grouped (i.e., Were there acquisition scenarios? Is product usage low?)
- Which CSMs have customers with the highest renewal rates?
- Which salespeople have customers with the highest churn rates?
- Do you have rock star team members that are renewing and expanding?
- Do you have a Sales-to-Customer Success issue where customers are not a good fit?
- Do customers who renew use your product at the minimum level?
- Are customers renewing in their first year but not later in their tenure?
- What’s the percentage of first-year customers who have churned?
- Are your factors still relevant?
- Do the initial datapoints you used (such as product fit and churn reason) accurately depict the customer scenarios?
You also want to get your team’s feedback on customers who scored as “healthy” but did not renew to identify new factors that might have been missed in the initial set up of your health score.
Based on your analysis, adjust your health score by adding, deleting, or reweighing its factors.
For more complex customers, you may want to add new health scores to create multiple evaluation perspectives.
Always be evaluating
With SaaS products in particular, new features get added all the time. How you expect customers to use your product will shift with those developments – as should your health scores. Of course, anytime you have a major product release, you should use that as an opportunity to reevaluate your scoring model.
But outside of product updates, people and processes constantly change too. Setting a review cadence for your health scores keeps them accurate by ensuring that no important changes in context get overlooked and your renewal forecasts stay strong.
For more on customer health scoring, check out these resources: