Why We Don't Have a Health Score
As we've built Monocle, one question keeps coming up: "Where's your customer health score?"
The short answer: we don't have one.
The longer answer says everything about how we think the next generation of post-sales tools should work.
Why Health Scores Fail in Practice
I've built or inherited some version of a health score at every company I've worked with over the last decade. In theory, it's a simple idea: one number that tells you if a customer is thriving or at risk. In practice, it's never simple, and it's rarely right.
The issue isn't that health scores are bad. It's that they're too blunt for the complexity of real customer relationships.
They try to reduce hundreds of qualitative and contextual factors like adoption, outcomes, sentiment, and risk into one static metric. That number might look neat on a slide deck, but it's almost never actionable.
The Problem Starts Upstream
A health score is only as good as the inputs feeding it. And in most organizations, those inputs are inconsistent, incomplete, or undefined. Ask ten people what "customer success" means, and you'll get twelve answers.
Some will point to feature usage. Others to ROI or retention. Most can't tell you when, exactly, a customer crosses the line between "using" and "getting value."
That's because success isn't one moment; it's a sequence of them. And those signals are buried in tickets, Slack threads, community posts, and customer conversations that never make it into a database. Until companies quantify and validate with customers themselves what success actually looks like, every health score I've seen to date is a best guess.
This Is Hard, and That's Okay
Let's be clear: this work is insanely hard.
That's not an indictment of any Customer Success platform or methodology. It's an acknowledgment that we're all wrestling with a problem that's messy, human, and constantly evolving.
Anyone who says otherwise is lying to you. Getting this right requires coordination across Product, Engineering, CS, and sometimes even Developer Relations. It means collecting inputs from customers and reconciling what you think is working with what they actually value.
That's why the next generation of AI startups focused on turning noise into signal at scale are so critical. They're trying to bridge that gap, mining unstructured context for truth. And they need to succeed, because our entire industry depends on better data to make better decisions.
Side note: If you need to look somewhere for that type of tool, I'd strongly suggest Magnify. They were early in looking at connecting these items and I've used them in a previous role. In addition to this they do a phenomenal job of orchestrating your proactive customer journeys at scale.
But for that to work, we, the builders, have to invest heavily in backend operations and data integrity. Good data doesn't just make dashboards prettier. It makes your AI smarter, your predictions more reliable, and your teams more confident. Data quality is the quiet moat most companies overlook.
Where Monocle Starts
At Monocle, we decided not to fake precision. We don't have a "health score." We have a health assessment focused only on what we can measure with confidence.
Our current focus is simple: Is the CSM doing the right work for the customer's current journey stage?
We track whether the right motions are being executed, whether those actions align with journey stage and product complexity, and whether the CSM's workflow reflects best practices.
It's not a replacement for a score. It's the groundwork for one that might actually mean something someday.
And yes, I've joked internally that if anyone ever wants to build a fully custom health score in Monocle, we'll throw in a free hour-long consultation just to warn them about the dragons that lie ahead. Because those dragons are real.
The Path Forward
This is just the beginning of how we plan to tackle the health problem. As Monocle scales, we'll start layering in contextual intelligence: product usage, sentiment analysis, and customer-validated value signals. We'll continue to focus on motion quality, the work that CSMs do every day, but pair it with richer context that shows why customers succeed.
And this is likely where we'll partner with the emerging startups working to turn noise into signal at scale: companies building the data pipelines, enrichment tools, and AI models that help translate customer behavior into structured understanding.
Because the future of Customer Success isn't just about smarter dashboards. It's about a connected ecosystem that finally lets us see the whole picture.
The Real Measure of Health
Customer health isn't a number. It's a relationship between actions, outcomes, and trust. Until AI can validate what "value" truly means for every customer, the most honest measure of health is simple: Are we doing the right work, at the right time, for the right reasons?
That's what Monocle measures today. Because before you can score health, you have to understand what makes it real.