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The 5 Core Metrics Every CS Leader Needs To Use

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As a leader in Customer Success, deciding on the best ways to deliver impact in your organization is key. Assessing the current situation, evaluating available data, and choosing the right metrics to report on are crucial. You don’t want to dump mountains of data you’ve been collecting in front of busy executives who don’t have time to go through it all. Instead, you want to choose a few key metrics that will both showcase your performance and be useful in making accurate projections.

Of course, every business is unique and the exact metrics you need will change depending on your business model; nevertheless, there are a few universally useful metrics that will help any business predict its overall performance.

As professionals with years of experience in Customer Success and Co-Founders of Velaris, we have found that capturing data, ensuring it is accessible, and acting on it effectively are some of the first challenges a new leader will solve. So let’s start with the 5 metrics at the core of Customer Success.

Net Revenue Retention (NRR)

NRR is the sum total of all active subscriptions and expansions (upgrades, upsells and cross-sells) after deducting downgrades and cancellations. It is usually calculated on a monthly basis.

So why is it important? High NRR proves that customers are happy with the product and finding value in it. This is especially important for start-ups because it can indicate product-market fit - which is crucial for securing funding.

So what numbers should you be aiming for as Head of CS? It depends on your business model. Experts say that for Enterprise SaaS (OKTA), 125% is a great metric, whereas Bottom-Up SaaS (Slack) should strive for at least 100%. As Customer Success is about retaining customers and making their experience better, a good NRR rate indicates a successful strategy.

Other fundamental questions to consider are: Where is this data situated? How would you calculate your NRR so you can report on it to key stakeholders?

Time to Value (TTV)

Time to Value measures how long it would take a new customer to start experiencing the full benefits (i.e. the value) of your product. This metric is important because lower time to value means a lower risk of churn. The faster your customers understand how they benefit from your product, the less likely they are to give up on it.

Beware - the most important part of this metric is defining what “value” means for your product and your customer. Reducing TTV without a clear-cut definition is risky because you might end up leading your customers down the wrong path. After defining the value, you can start finding ways of decreasing TTV and improve customer experience.

There are a few methods of reducing the time to value, such as reducing onboarding time. The learning curve of the product will depend on its complexity, but you can always try to provide quicker routes to gaining value from your product. Alongside that, take a look at the adoption rates of new features. If your data shows a delay in adopting a few features, it might mean that the features are unnecessary or too complex.

Start by gathering data with a goal to ensure it is accessible and actionable, followed by the formulation and execution of your strategy with the ability to then evaluate the results. How would you do this in your organization?

Gross Renewal Rate (GRR)

GRR is the percentage of contracts that have been renewed in a defined period. Unlike NRR it doesn’t consider expansion revenue, so it’s a direct indicator of the impact of churn.

GRR can never be above 100%, but the higher the number, the better for your business. This metric is a great indicator of customer success as it shows how often users abandon your product or downgrade to fewer features.

This metric is important to executives because consistently tracking over time helps improve the accuracy of revenue predictions. And predictability is key to for any business.

Managing and often reducing churn is a key goal as a CS leader, and your approach to identifying and mitigating potential risk will be key to increasing your GRR.

Expansion Revenue

Expansion revenue is the revenue generated by existing customers who have signed up for additional products. This metric is a great indicator of retention because the more customers spend on your product, the less likely they are to churn. They become more invested in your product because of the extra expense they have put into it.

One of the most meaningful strategies for growing expansion revenue is making the process of upgrading as easy as possible. Your users should not struggle with every upgrade; moreover, access to extra features should be offered to your customers. However, to figure out when and how to suggest upgrades to your user base, analytics including the measurement of product usage can be very insightful and support your expansion strategy.

Health Score

The health score of a customer is directly correlated with the chance of renewing or churning your product subscription. By defining the scope and the weights of various metrics and data points, you can accurately calculate the health score that is more suitable for your business, and understand the health of your business at a glance.

The exact variable you should track will differ by industry, but there are a few we believe are important to have a comprehensive health score:

  1. Product Usage: For example customers with low product usage are often at a high risk of churning.
  2. Net promoter score: It may be difficult to collect enough feedback to calculate objective scores because customers tend not to leave positive reviews when they are satisfied; whereas, they do often share negative experiences.
  3. Pulse: Data can tell us a lot. However, the ability to assess risk based on interactions with your team are essential as their experience and knowledge can uncover things the data cannot.

You can add more variables to the health score depending on your experience and industry, but we recommend that you don’t overcomplicate it.

We know that gathering and monitoring all this data can be painful. Having to manually wade through various tools to scrape these numbers together is frustrating and that is before you start to action your insights and demonstrate results. This is why we have built a tool that can help.

Contact me on or through LinkedIn if you think it’s worth a chat!

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