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

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.

The future doesn’t belong to systems that store data and automate workflows—it belongs to those that synthesize information, surface insights, and drive action.

The days of bouncing between screens, hunting for information, and manually aligning teams? Numbered.

Every day, we are getting closer to a workplace where:

-Knowledge workers won’t be glorified data entry clerks. Technology will finally do the heavy lifting, freeing them to focus on strategic work. These people will be responsible for outcomes without being encumbered by the tedium. As a result, we will need fewer people to acquire and keep our customers.

- Every team continues to work on their screen of choice, but the data they have access to will be aggregated across every system. They may be on different screens, but everyone will be on the same page. The next era is about alignment, automation, and AI-driven decision-making.

- There will be a fundamental shift in the tech business model. Businesses won’t pay for ‘seats’—they’ll pay for intelligence. The old model of software—charging for logins, licenses, and user seats—is dying. No one wants to pay for access to another tool; they want outcomes, insights, and automation that drive real impact. The solutions that deliver intelligence over any interface will define the next era of technology.

The shift is happening—those who embrace it will lead, while those who resist will be left behind. The future belongs to businesses that trade inefficiency for intelligence, that replace busywork with impact, and that empower people to think, create, and drive outcomes—not just enter data. Innovation doesn’t wait.

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.