Haven’t found any articles or thoughts on this topic so I wanted to start up a thread on it. Interested to hear how others have communicated this internally, and how their companies view customer health when it comes to a customer actually renewing their contracts.
Almost all of our customers are on year-long contracts, and our pricetag is fairly high, so we tend to end up with almost exclusively high touch customers. We’ve been around for ~10 years, and have had a CS org for 8 of those. We defined our current health scores under a previous VP of CS and have had some new C level executives join right around that time, or after.
Our health scores are ‘inaccurate’ when it comes to predicting renewals. The CSMs aren’t managed to them, and they are often misinterpreted as a likelihood to renew. I’ve tried using the analogy of the hang gliding CEO to illustrate how we should be thinking about customer health, account risks, and how they relate to renewals.
Picture yourself as the doctor of a Silicon Valley CEO. The CEO brushes his/her teeth for a full 2 minutes, eats an organic diet, swims laps before work, runs in Golden Gate Park, and does yoga 3 times a week. Overall, very healthy when we are measuring BMI, blood pressure, etc… BUT, every few weeks, this CEO also likes to go hang gliding out at Fort Funston.
Translating this to customer health and risks, we have a very healthy (“Green”) customer, with an identified risk (which we know to be very risky). So the CEO’s likelihood to renew should be some blend of these things.
So, do you all try to bake this risk into the customer health score? Do your teams understand this difference, and how did you communicate it to them? Do you actually manage to your health scores, or are they just nice to look at?
How should all of this work?
Best answer by dan_ahrens
As you noted, if a health score does not accurately predict the likelihood of continued “healthy” business between a customer and your company, then the health score is meaningless. A good way to improve the accuracy of your health score is to do a historical analysis of your accounts that churned, as well as accounts that performed above average. You want to identify common traits that are tied to poor health and great health.
For example, many companies have a dimension of their health score that looks at the number of support cases currently open. Which is a fine metric to track and elevate visibility around, but what if your historical analysis shows no correlation between high or low support case volume and likelihood to renew or expand or churn? It might still make sense to track that metric for overall visibility, but you may decide to weigh it at a very low percentage, if at all.
While a historical analysis is best, you may find it easier just to do an analysis on customer renewal outcomes in the next quarter and for each customer that churned, renewed, upsold, etc - analyze if the health score was predictive of that outcome, or if it gave a false predictor. If it did not align with the outcome, you should look at the business reasons why that customer churned, renewed, upsold, etc and make sure they are accurately captured in your scoring rubric. They might also be in that rubic, only weighted incorrectly.
This where you’d want to play around with Google Sheets or Excel a bit and tweak your health score metrics and percentages using the actual data from the customers that had a renewal in that time period that you’re analyzing and adjust the health score formula with actual customer input to see if your revisions will capture the true health.
If you have a data science team, they can accelerate this process, as doing a correlation analysis is a great start.
Happy to chat further on this topic. It’s something that’s a bit of a personal passion for me, having built health scores from scratch before and realizing that it’s not a “set it and forget it” but something you have to continually validate and adjust.