Customer Health Score (also known as Customer Health) takes multiple dimensions of customer data metrics and classifies them into a single representation of green, yellow or red. It is a consolidation of all the information the company has about the customer, from all probes, people and systems, past and current.
Companies measure customer health to speed up and scale communication, prioritization, decision making and forecasting of their customer success operations. The scorecard model is simple to understand:
When a customer is green, the customer is getting value from the products and services, the engagement is effective and the company should continue to manage the customer in a similar way.
On the other hand, once a customer is marked as red, there is something wrong that requires immediate attention. Either the customer is not getting value or there is poor engagement with the account. Action is required to address it. Customer health is also known as one of the key components of an early warning system.
Customer Health Diagnostics
Now that we’ve established the purpose of customer health, there are 2 other considerations that we took into account when we designed our own customer success model. I call the first one “expressiveness of customer health”. Put differently, when something is wrong and the customer is marked as yellow or red – why is that? Is there a single reason, multiple reasons, and what are those reasons?
The second consideration that ties into the expressiveness of customer health answers what are the metrics/measures that should be included in the customer health and what is the best way to formalize those into green, yellow or red?
The expressiveness of Customer Health
Sometimes when companies introduce a customer health model for their business they might deliberate on the objective and the formulas for health forever – the classical analysis paralysis syndrome. To make it clear, here’s what we’re trying to solve for:
1. What makes a customer green?
2. What makes a customer red?
3. Yellow – if the customer is not green and not red they are yellow, and in this case, we want to answer, why are they not green, what are the gaps?
Health score must be actionable. By knowing the reasons that attribute to the color classification, the company has a clear path for action.
What are Customer Health measures?
What I have found most effective is to group the measures into categories. The most common health categories I suggest to start with are:
1. Product Usage and Adoption – what are the volume and depth of use?
2. License Utilization – how much of the sold licenses are actually being utilized?
3. Business Results – is the customer getting the value they signed up for?
4. Engagement – support, billing, marketing, customer success engagements – how are those going?
5. Advocacy – is this customer referenceable, advocate?
When we tie it all together the health formula should be something like this:
Green Customer – All of the thresholds of usage, utilization, business results, engagement, and advocacy are met.
Red Customer – The customer is flagged in at least one of those categories. There could be a sharp decline in usage or the customer is a detractor or not paying their bills and so forth. I’m sure you get the point.
Yellow Customer – They only meet some of the green criteria but not all of them. So there is clear room for improvement, but on the other hand, nothing is burning (yet).
With this model of a health formula, we not only have the ability to color each customer, we can also communicate very clearly the reasons behind the health classification.
Rules-Based Customer Health vs. Linear Customer Health
Using logical conditions is also known as Rule-Based Customer Health. Most people start with customer health using a spreadsheet. They use the excel formulas to summarize the metrics across a row and come up with a number. This is known as linear based health. Linear health has a few known limitations compared to rule-based customer health:
Masking – It is very easy to understand customers that score 100 (all good) or 0 (all bad), but it becomes very difficult to look at a customer that scores 30 to 70 to really understand the reasons behind that.
Difficult to Change – formula change in linear health is very difficult to do and in many cases creates confusion
The intuitive value of rule-based customer health is clear. The better, fast and more accurate decision-making process to be proactive about customer operations.