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Leveraging Tech & Data to Drive Customer Success
Leveraging the right tools and optimizing them to drive what really matters – ensuring customer success.
Tomas
June 10th, 2025
In partnership with
Featured Article
This isn’t the little leagues anymore – you’re managing complex clients. They demand personalization at scale, proactive value delivery, and zero room for blind spots. The old playbook? Broken.
The new one? It’s all about how you approach tech and data. Today, we’re looking at whether you’re leveraging the right tools and optimizing them to drive what really matters – ensuring customer success.
🔄 1. Match to your clients’ tools (not the other way around)
Forcing your tech stack on clients is going to kill trust. Don’t make them move from their comfort zone. Even if it means manual data entry across three platforms. Or juggling five different comms channels. Even if it slows your team down.
Here’s why.
Enterprise relationships hinge on smooth transitions. The more seamless it feels for your client, the easier that “yes” will be come renewal season.
Plus, you being willing to operate in their space signals respect. That goes a long way.
Simple rule: meet them where they are. Whether that means…
If they’re tracking projects on Jira, then that’s where you log in too.
Do they prefer SharePoint? Then send reports via – you guessed it – SharePoint.
Quick fires should go through Slack if that’s their thing – not email.
It sounds annoying and time-consuming. And, honestly, it is. But the trade-off – i.e. making things totally seamless for your customer – is worth it.
Enterprise clients expect you to speak THEIR language on THEIR tools. End of story.
📊 2. Data strategy = Revenue strategy
Garbage data? Congratulations, you’re flying yourself straight onto your customer’s list of budget cut items.
Instead:
Tie data DIRECTLY to business goals:
Map product usage to contract value (e.g., "If Module X isn’t used, renewal drops 30%").
Track feature adoption by department—not just "logins."
Correlate training completion with expansion opportunities.
Focus on Red Flag stats:
Product Usage
Support Ticket Trends
NPS + Executive Feedback
Training Impact
Remember: Enterprise clients won’t renew what they don’t use. Spot those gaps early so you can fix them before they’re a problem.
⚡️ 3. Automate the grunt work with AI
Deploying AI isn’t about reducing your workload. It’s about making space for your CSMs to focus on the strategic work. If you could release your top CSMs from 20 hours a week of admin work – how could they use that time on top value work instead?
Think: “AI for taking notes. Humans for taking action.”
Here’s what you should be using AI for right now:
Auto-summarizing customer calls and pulling action items
Generating churn risk scores from support interactions
Drafting QBR slides with usage insights
🎯 4. Measure what matters (ignore the rest)
The best use of your data is what it tells you about your customer success. If you track strategically, you’ll get the full picture – what customers are at risk of churn, where your best revenue opportunities are, and what needs to change to avoid losing clients.
Here’s the short of it.
First, focus on predictive metrics over reflective ones:
Feature adoption velocity (e.g., "Accounts using Feature Y within 30 days renew at 95%").
Support ticket sentiment shifts (spikes = early red flags).
Executive engagement drops (missing QBRs? Sound the alarm).
Then, ditch generic CRMs. Instead:
Implement Customer Lifecycle Management (CLM) platforms like Gainsight or Totango.
Track real health signals: adoption spikes, support ticket trends, executive engagement dips
Always come back to what’s actually moving your bottom line. Look for specific, tangible results and ROI (e.g. "AI flagged 12 at-risk accounts; we saved $2.8M in Q3”).
🧠 The golden rule of tech: Use it to support, not to replace.
Tech amplifies humans – but it cannot replace them.
Never forget that enterprise clients buy relationships. Not just solutions.
Use data to fuel those relationships. Your CLM platform isn’t the hero – your CSM armed with strategic insights is. So focus on developing a tech strategy that helps your people locate and augment those insights.
Your next step? Audit this:
Are you tracking behavioral data (usage, sentiment) or just vanity metrics (logins, "active users")?
Start there, then work your way through the rest.
Looking for more on leveraging metrics? You’re in luck. Next week, we’re focusing on how to continuously and strategically track CS metrics.
Tomas
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