Continuously Measuring and Optimizing Customer Success Metrics

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Firstly, my apologies for going offline for the last two weeks. I had to step away unexpectedly to care for a sick family member, and I appreciate your understanding.

Now that I’m back, let's get right to it.

The last thing you need is another damn dashboard. If you feel like you’re drowning in health scores and insights, but you’re still getting blindsided with churning customers – I have a solution…

You’re focusing on the wrong metrics. 

Tracking metrics in the rearview mirror doesn’t help you see the road ahead. Instead, you need to start looking at the insights that actually help you predict and forecast. 

I’ve compared and researched CS tracking strategies from over 200 elite teams, and this week, I’m sharing the most important metric insights I’ve learned.

⚖️ 1. Don’t ignore the context behind the numbers. 

Forget vanity metrics. Instead, get clear about the customer behavior that helps you flag churn risk from a mile away. 

The three behavioural metrics that matter:

1. Feature adoption velocity (e.g., “Accounts that hit X workflows in 60 days renew 2.3x more”)

2. Stakeholder decay: Track engagement breadth (how many departments are active?)—not just your champion’s logins.

3.  Support ticket sentiment: Use AI to flag frustration spikes before escalations.

Don’t forget – health scores without human intuition only tell half the story. 

Keep your CSMs in the drivers seat, and let them override algorithms.
→ Example: If a Fortune 500 exec hints at budget cuts, manually flag that client as “red” — even if usage looks fine

🛠️ 2. Not everything carries the same importance

Don’t overcomplicate. Don’t get distracted. Health dashboards stop working when there’s too much to see and nothing to gain. 

Instead, weigh your stats according to priority. 

Think… 

• 50% → Product adoption (usage depth – not logins)  

• 30% → Relationship heat (exec engagement, QBR attendance)  

• 20% → Support trauma (ticket trends, sentiment) 

Hint-hint: If your “healthy” accounts aren’t using core features, your score is broken.

🤖 3. Automate wisely

AI is like a fantastic intern. Put onto the right tasks, it can slingshot your whole team forward. Thrown into the deep end without context, it will sink your whole operation. 

Build in AI where it supports your people:

• Predictive churn alerts: Have your ML model spot silent disengagement (e.g., “CEO stopped opening emails + support tickets down 40% = hidden risk”).

• Auto-playbooks: Trigger “save” tactics when scores dip (e.g., schedule executive rescue call).

AI alongside human oversight can cut churn up 22% faster according to recent insights. But hear me when I say… 

Never try to replace your people with AI. Avoid full dependency on the newest tools. And always keep human veto power.

🔧 4. Scrub your data clean

Bad data = confident wrong decisions

Ever tried to make a cake with expired ingredients? Yeah. This is the same. 

But that doesn’t mean you need to do a total overhaul all at once. Start small. Focus on tracking 3 metrics perfectly instead of half-assing 20. 

And it doesn’t have to look fancy if the insights are good. A manual Excel sheet with clean data beats Gainsight full of garbage any day. 

The 90-day cleanup:

1. Kill duplicate accounts (Salesforce hygiene sprint).

2. Align definitions: Does “active user” mean logged in or completed a job?

3. Pipe product usage data hourly – not weekly snapshots.

You’ll know your metrics are good when they force your people into action.

If a number doesn’t change what your CSMs do tomorrow, scrap it. Find a metric that sparks a response. 

This week, try something new: Audit one “green” account with low feature adoption. Call them. Ask why. Figure out what’s actually going on under the surface. You might be surprised by what you find.

P.S. Wondering how we turn this up a notch? Next week, we’re looking into how to bring everyone and all the tools together for a holistic, seamless customer experience.

Tomas

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