Three Mistakes CS Leaders Make When Defining Success Metrics

Most CS teams I've seen are measuring the wrong things. Not because the leaders don't care, but because they defaulted to whatever was available when the team was built — and never revisited it.

That's a problem. And it shows up in the same three ways, almost every time.

Tracking Activity as if It Were Outcomes

QBRs held. Calls logged. Emails sent. Health scores updated. These are inputs, not results. Somewhere along the way, CS orgs started treating effort as a proxy for value — and it's quietly wrecking their credibility with the rest of the business.

I get why it happens. Activity is easy to track, easy to report, and feels like proof of work. But when you're in a board meeting and someone asks "what impact is CS having on revenue?" — "we held 47 QBRs this quarter" is not an answer.

If your metrics dashboard is full of things your team did rather than outcomes your customers experienced, you're measuring the wrong layer entirely.

Using Churn Rate as Your North Star

Churn rate is a useful metric. It's also a post-mortem.

By the time churn hits the dashboard, you've already lost. The decision to leave was made weeks — sometimes months — earlier. Using churn rate as a primary success metric is like judging a doctor's performance by counting deaths. Technically accurate. Completely useless for preventing the next one.

The metrics CS leaders should care about are leading indicators: product engagement trends, time-to-value in the first 90 days, support ticket frequency, feature adoption curves. These tell you something is going wrong before the customer has made up their mind.

I've seen teams that barely talked about churn — because their leading indicators were tight enough to catch risk early. That's the goal.

Making the Metric the Target

This one gets me every time.

You roll out NPS as your CS success metric. You tie it to comp. Six months later, CSMs are cherry-picking who to survey, timing requests after a win, and quietly skipping customers they know are unhappy. The number goes up. Nothing changes.

This is Goodhart's Law: once a measure becomes a target, it stops being a good measure. It happens with NPS, CSAT, health scores, and renewal rates when teams push multi-year contracts to inflate the number.

The fix isn't to find a metric that can't be gamed — any metric can. The fix is to use a portfolio of metrics that are hard to manipulate in combination, and to pressure-test them regularly. Ask: if this number went up but everything else stayed flat, would we actually be in better shape? If the answer is no, you're measuring the wrong thing.

The Real Problem

All three mistakes come from the same place: defining success by what's easy rather than what's real.

CS is still a relatively young function in most SaaS companies, and a lot of teams inherited their metrics from Sales (activity-focused) or Finance (lagging outcomes). Neither model fits.

The best CS leaders I know define metrics around one question: is the customer getting the value they paid for? Everything else is a proxy. Some proxies are good. Most aren't.

Start there.