Leading and lagging indicators of customer success

There was a recent discussion over on the Customer Success LinkedIn group about defining leading and lagging indicators for customer success.

Here’s my take on which is which, through the prism of churn risk.

Indicators or outcome measures?

Before we get to indicators, let’s start with defining the outcome measures.  These are the standard business metrics that are used to measure success. They sometimes get confused with leading indicators.

Some examples:

  • Logo and revenue renewal rates
  • Churn rate
  • Period-over-period revenue growth per customer
  • Lifetime value

Therefore, indicators aren’t financial metrics so much as they are operational measures.  And the best indicators are the ones you can link to the outcomes you care about.

For example, take your churn events: can you unpack your churn outcomes to spot the leading and lagging indicators in retrospect?

Lagging indicators

If we’re looking for churn indicators, think of lagging indicators as the evidence of customer risk that could turn into a bad outcome in the near term.

Some examples:

  • Account escalation
  • Low license utilization
  • Negative feedback / surveys near a renewal date
  • Refund requests / discount requests
  • Account downsell

Leading indicators

Think of leading indicators as the earliest signs of a customer struggling to achieve value and success.

It’s easiest to conceive of early indicators when the customer relationship itself is early:

  • Slow time to first value
  • Slow initial adoption
  • Negative feedback and/or low survey scores
  • High volumes of support tickets (depending on what’s in them)

Others indicators can be signs of churn risk even when the relationship is otherwise stable:

  • Declining adoption
  • Negative feedback and surveys
  • Lack of engagement


There are plenty of indicators you can pay attention to; too many, in fact.  So the goal is to focus on a subset.  

Start with just one outcome that matters most.  For example, “flat or reduced renewals”.

Unpack that outcome to spot the indicators.  Get good at monitoring them, and responding to them reliably.

Once you’ve established some focus, you face a choice. Stay the course or introduce additional outcome measures with their indicators? Regardless, start simple.

6 ways to increase revenue with product usage data

TLDR: Analyzing product usage data can lead to several revenue generating opportunities. While most SaaS companies have some form of usage measurement, few have achieved a complete view of usage.

Let’s review all of the revenue levers at stake.

One: increase revenue by increasing trial conversions

Most trials convert because the user achieves rapid time-to-value, such as minutes or hours from the time you provision their trial account.

The conversion rate drops off significantly when provisioned users don’t actually engage with your product until days into the trial. In fact, most provisioned users who don’t access your application in the first few days probably don’t convert ever. So, usage data gives you an “early warning” about provisioned users who aren’t active in the first hours; you can react by stimulating their access with emails, phone calls and such.

As you master usage analytics, you’ll also discover the features that are most correlated to conversion. You can then engage trial users with a purpose: nurture them to experience the “best” features for conversion.

To summarize, the blueprint for successful conversion is about getting users to achieve rapid time-to-value, and to specifically experience the features that lead to conversion.

Two: increase revenue by spotting up-sell opportunities

Highly engaged users tend to adopt your application faster than others. In fact, the velocity of adoption is a critical measure for your business. These users are open to expansion revenue propositions and should receive attention when exhibiting “high velocity” behavior.

Even in the “average” user segment, progress toward some license limit should be the trigger to capture expansion revenue. Some examples:

  • users who have used 80% of their licensed seats
  • users who have consumed 80% of their capacity (as expressed in data volume or some other metering method)

The other important consideration is to present the up-sell opportunity at the earliest moment of need. Too often, vendors use an artificial event such as a renewal date to propose a higher-priced offering. But what if the customer had a need months ago? Selling at the moment of need is the best way to maximize this revenue stream. Pay attention to velocity and license limits as a result.

Three: reduce churn by spotting declining usage

Just as increasing adoption is a very strong positive signal, decreasing adoption is a strong negative signal. One could even argue that if usage is not growing, trouble lies ahead.

When a customer is at-risk as evidenced in usage data, the time to intervene is now. Whatever is causing customer dissatisfaction needs immediate attention. This “moment of opportunity” shouldn’t be confused with the renewals cycle. Customers can and do “churn” before their contract expires, by ceasing their usage. You’re highly unlikely to receive a renewal if you address their dissatisfaction at renewal time instead of the time they began to disengage.

Four: increase revenue by finding and mobilizing evangelists

Evangelists are a far more effective means to acquire new customers than any other means thanks to the power of recommendation and social proof. The challenge is that evangelists are often hard to spot. You have to find, enroll and activate them instead of waiting for them to naturally behave this way.

Usage data is a goldmine here. The heaviest adopters, and users of your most differentiated features, are hidden in your usage data. The programs you have to activate evangelists are super-charged when you’re identifying all of the potential recruits in usage data.

Five: increase revenue by improving usability

If we believe that adoption begets customer retention, then barriers to adoption must be systematically found and fixed. Usage data enables you to spot areas in your application that have usability issues.

Some examples of how to find usability issues in usage data:

  • No actions beyond login. Is the user having trouble getting started? Does the product have a good “first-use” mode?
  • Patterns of navigation that suggest users are getting “stuck”: a user is on the cusp of performing a task, then goes to a product help page, then goes back to the task and never successfully completes it
  • Users abandoning a guided process partway through, such a wizard. Is the process designed in a way that they can’t, or don’t want, to complete it?

Six: increase revenue by increasing prices

When you understand the correlation between usage and retention, you understand the sources of value creation in your product. Did you price your product on this basis? In other words, are you leaving money on the table by not linking your price to the features that generate value? Most organizations set prices for many other reasons than their unique selling points.

Where to go from here: getting and using product usage data

Now we have a framework for the value drivers of usage data. And they can add up to major revenue lift.