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Customer Health Score takes multiple dimensions of customer data measures and classifies them into a single representation of green, yellow or red. It is a consolidation all the information the company has about the customer, from all probes, people and systems, past and current.

Companies use customer health to speed up and scale communication, prioritization, decision making and forecasting of their customer success operations.

The model is very simple to understand:

When 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 customer is marked as red, there is something wrong that requires immediate attention with either the value the customer is not getting or the engagement with the account. Action is required to address it. Customer health is also known as the one of the key components of an early warning system.

Now that we’ve established the purpose of customer health, there are two other considerations that we need to take 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 those reasons are?

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?

Expressiveness of Customer Health

Sometimes when companies introduce customer health model for their business they they might deliberate on the objective and the formals 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 and by knowing the reasons that attributes to the color classification the company has a clear path for action.

What are the measures that formulate Customer Health?

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 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?

I’ll follow up with specific measures per each category in a follow up blog soon.

When we tie it all together the health formula should be something like this:

  1. Green Customer – if All of the thresholds of usage, utilization, business results, engagement and advocacy are met.
  2. Red Customer – if the customer is flagged in At Least on of those categories. There could be a sharp decline is usage or the customer is a detractor or not paying their bills and so forth. I’m sure you get the point
  3. Yellow Customer – those who are not green nor red. 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 health formula we don’t only have the abiltiy to color each customer we can also communicate very clearly the reasons behind the health classification.

Rule Based Customer Health vs. Linear Customer Health

Using this 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 few known limitation 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 customer health is clear. Better, fast and more accurate decision making process and being proactive about customer operations.