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What is Customer Health Score?

Customer Health Score 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 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 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 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 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?

 

Expressiveness of Customer Health

Sometimes when companies introduce a customer health model for their business they 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 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 one 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 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.

Rule 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. Better, fast and more accurate decision-making process to be proactive about customer operations.

If you find this topic relevant for you, I highly recommend that you check out our customer health webinar series video on-demand:

Part 1 “How to Build an Effective Customer Health Model” with Totango + iPerceptions [Watch]

Part 2 – “Measuring the Effectiveness of a Customer Health Model” with Totango + Feedvisor [Watch]

Please let me know in the comments what you think about the topic.

Guy

 

Guy Nirpaz

Guy Nirpaz is a Silicon Valley-based Israeli entrepreneur and CEO of Totango, a Customer Success software platform. A pioneer in the Customer Success field, Guy established the Customer Success Summit and is a well-regarded industry speaker and community contributor. Guy loves people and technology and has dedicated his career to improving the way in which business is done through innovation. Fun Facts: Guy moonlights as the lead guitarist in a rock band based out of his garage in Palo Alto and used to command a tank battalion...as well as having grown oranges.

  • Good summary, thanks for taking the time to make. Definitely seems to be a hot topic recently. One dimension that you might also want to consider is that customer health is also lifecycle-based – i.e. you might not expect customers to score 100 within 30 days of buying your service, but you might use different measures to understand whether you have a very young but unhealthy customer.

    • Mike – that’s a very good point. You are correct, there is not one size that fits all of customers.

      We use health profiles for that specific purpose – a profile matches health definition to a specific segment of customers. I’ll write about that and hierarchy health in the next few posts.

      Thanks for the compliment!

  • Dale Burnett

    Good overview. I like the concept of tracking key metrics over time … what is the WAU / MAU ratio tracking?

    • Thanks Dale. WAU – is weekly active users. MAU – monthly active users. WAU / DAU is the ratio between weekly users and monthly users. The closer the value is to 1 it means that user frequency is higher.

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