Software is no longer the end product—intelligence is

We’re making it easier than ever for teams to tap into the power of customer conversations. With this integration, Sturdy’s AI-driven insights—trained to spot key behaviors and trends unique to your business—are now right where your team works. That means more proactive decisions, better collaboration, and a serious productivity boost.

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Here’s how Sturdy works with Slack.

• Get the right insights, right in Slack. Sturdy delivers AI-powered Signals where your team already works, flagging risks, expansion opportunities, and other key moments in real-time. No more digging through conversations—just actionable insights when you need them.
  
  
• Stay on top of every conversation. If your team works asynchronously in Slack channels, it’s easy for important feedback to get lost. Sturdy keeps you ahead by surfacing critical insights before they slip through the cracks.
  
  
• Act fast, not after the fact. Whether it’s a service risk, a feature request, or a potential upsell, Sturdy helps teams spot and respond to what matters—without disrupting their workflow.
  
  

Seamless sync with your tools. Sturdy doesn’t just stop at Slack. Insights discovered in customer Slack channels automatically flow into Jira, CSPs, CRMs, and other systems, ensuring the right teams get the right info—without extra work.

He doesn’t talk much, but when he does, you’d better listen.

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Quote from C-3PO, Star Wars: A New Hope

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A few days ago, I spoke to a business leader, and they asked, "How would Sturdy work for customers who never contact us?"

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"Do you know who those customers are?"

"No idea."

"Would you like to?"

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“Dark Customers.” It is almost impossible to source this list. Your customer might be dark to five silos, and bright in just one. 

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(By the way, there is a little-known filter in the Accounts page of Sturdy that lets you sort by “Last Inbound.”  Check it out. You can see the last time any customer sent you an inbound message.)

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Let’s be fair. In a recurring-revenue business, a lack of inbound contact isn’t necessarily bad. Sometimes your customers don’t feel the need to chat with you, but they like you just the same. 

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But, here’s the cool thought. What should happen when a Dark Customer suddenly reaches out? 

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For example, Acme Corp sends an email to your CS team for the first time in 18 months. What needs to happen next?

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I would want to know. So, we’re working on that. Naming such a signal is a bit tricky, if you have ideas, let us know.

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I've seen a slew of new AI companies doubling down on analyzing usage data as the silver bullet for predicting churn. It’s an attractive idea—track how often customers log in and how many features they use, and you’ll magically, often with some proprietary algorithm, you'll know who’s at risk and who’s primed for expansion.

That’s not how reality works.

Usage data alone is riddled with false positives, often creating a distorted view of account "health." A customer heavily engaging with your product isn’t necessarily satisfied—they might be struggling and frustrated. A drop in product usage doesn’t automatically signal churn risk—perhaps the customer has completed implementation and is now deriving value without needing to log in frequently.

🚨 High Usage ≠ HappinessCustomers with high usage might actually be frustrated and, therefore, a risk. Why are they opening support tickets and emailing their CSMs?Are they engaging because they love the product—or because they can’t figure something out? What are they saying? What’s the context?

⚠️ Low Usage ≠ Churn RiskThe modern technology landscape isn’t about engagement for engagement’s sake—it’s about delivering value with minimal friction. ✔️If your product makes life easier, customers shouldn’t need to use it constantly.

✔️Instead of measuring time spent, measure outcomes.

✔️Instead of chasing logins, track behaviors.This requires context—something raw usage data doesn't provide.

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📉 Usage ≠ RenewalsIn SaaS, high usage doesn’t guarantee a renewal.Renewals are driven by:

✔️ Perceived value (or lack thereof)

✔️ ROI & business impact

✔️ Alignment with evolving needs

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To truly predict and drive retention, track the right contextual signals like:

✔️ Contract issues

✔️ Bi-directional responsiveness and closed-loop resolutions

✔️ Budget and procurement discussions

✔️ Expansion/contraction language

✔️Change order requests

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Look for specific context beyond sentiment.

🔍 No Context, Limited InsightsUsage data doesn’t explain why something is happening. Why did usage drop?

⁉️ Did the customer stop needing what you sold them, or are they trialing a competitor?

⁉️ Have users given up on your solution and found a workaround?

⁉️ Is usage dropping in specific customer segments (e.g., corporate accounts)?

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You won’t find these answers in product telemetry alone.

Companies that get this wrong focus heavily on usage metrics and then wonder why their churn predictions fail.

The ones that get it right combine usage data with contextual signals—the insights that explain the "why."

Real-world signals tell you how customers feel and what they need, not just which buttons they click and how often.

If your account management strategy is built purely on tracking usage and opinions, you’re looking at a puzzle with half the pieces missing.