Querri

Customer churn rarely happens all at once. It builds quietly across your data. Usage starts to slip. Support tickets increase. Engagement slows down. Renewal gets closer. The challenge is not a lack of signals. It’s that they’re spread across different systems and hard to see together. In this step-by-step tutorial, you’ll learn how to bring those signals into one place and identify at-risk accounts before renewal, using a simple, repeatable workflow. We walk through how to: Combine CRM, product usage, support, and engagement data Analyze 30, 60, and 90-day trends across accounts Surface early warning signals tied to churn risk Build a prioritized list of accounts that need attention now This is a practical workflow Customer Success teams can use to move from reactive reporting to proactive retention. 📺 YouTube: https://lnkd.in/eeeVek2s 👉 Follow along with the full playbook (includes sample data): https://lnkd.in/epKrkhSc 👉 Try it yourself with a free Querri account: https://querri.com If you are leading Customer Success or supporting retention, this is a practical way to get ahead of churn and have more meaningful conversations with your customers before renewal. #CustomerSuccess #customerSupport #Data #AI #AIDataAnalytics

We’ve been having a lot of conversations lately with software teams exploring AI analytics. Not “thinking about it” in theory Actually trying to build it into their product. And honestly, it makes sense. You already have something incredibly valuable: your customers’ data. The next step is helping them get more out of it. Not just dashboards. Answers. Context. Direction. That’s where things get interesting. Because what starts as “let’s add a simple AI layer” quickly turns into something much bigger: • A chat interface that actually understands your schema • Reliable query generation and validation • Dashboards, exports, permissions, scheduling • Infrastructure that works across every customer’s dataset On a whiteboard, it looks manageable. In practice, it’s a full product. We’re seeing a consistent pattern: Teams want to deliver this experience But they also don’t want to pull engineers away from the core product that differentiates them So the question becomes less about whether to offer AI analytics and more about how to do it in a way that actually makes sense for the business Our perspective is simple: You should own what makes your product unique And make sure your customers can fully use the data you already capture If that means building, great If that means partnering, that can be just as strategic Either way, the opportunity is real Your customers are already asking better questions They just need a better way to get answers If you’re thinking through this right now, we put together a deeper breakdown of what it actually takes to ship AI analytics well: https://lnkd.in/e767BTFC Curious how others are approaching this Are you building internally, partnering, or still exploring?

Most CS teams fall into one of two camps. They either pay $50K+ a year for a platform like Gainsight and wait six weeks for their first health score — or they rely on gut feel, a two-year-old spreadsheet, and whoever shouts loudest in the Friday standup. There's almost nothing in between. And the gap is quietly costing companies their renewals. The data you need to score account health already exists. It's sitting in your product database, your helpdesk, your CRM, your billing system. The problem isn't access. It's stitching it together every Monday without hiring a CS Ops lead or burning three months on a platform rollout. A few things worth knowing if you're building your first one: — Usage is almost always the strongest predictor of churn. Weight it around 40%. — Start with three signals you can collect cleanly, not eight you half-collect. — Your noisiest customers are often your healthiest ones. Support volume alone will mislead you. — A score that lives in a dashboard nobody opens is a vanity metric. Tie it to a CSM action, or don't bother. The good news: AI can do the stitching now. What used to need a CS Ops hire and a six-figure contract can run on CSV exports from tools you already own, refreshed every Monday before standup. Faster. More thorough. A fraction of the cost. We wrote up the practical version — the four signals that actually predict churn, the weights, what a working model looks like: https://lnkd.in/eXdvqjiJ If you had to build a health score tomorrow using only three signals, which three would you pick? #CustomerSuccess #Data #DataAnalytics #AIDataAnalyst

Your support team probably isn't drowning in tickets. It's drowning in patterns you can't see. The volume problem everyone talks about? It's rarely the problem. The real issue is that the same thing keeps happening, and you're watching it unfold in three different channels, across a dozen agents, buried in different ticket categories. Customer calls with the same problem but different labels. Docs that don't exist. Resolutions that stick for one agent and fail for another. The data is there. It's just spread across systems and buried under open tickets. We took a hard look at this and built a 13-minute video walkthrough on how to actually analyze your ticket data to find what's driving volume and cut repeat contacts. Not another dashboard. A workflow your team can reuse every week. If you follow along, you'll learn how to: - Identify the real drivers behind your ticket volume - Spot repeat contact patterns before they explode - Break down issues by channel, agent, category — see what actually correlates - Build a repeatable workflow your team can own Support teams are under real pressure. Too much of that pressure gets eaten by manual work instead of solving actual problems. The teams that improve fastest? They're the ones who can turn their data into something they can act on. Watch the video and grab the sample data. We included a step-by-step playbook too: https://lnkd.in/e6KmDjY9 #CustomerSupport #Data #AIDataAnalyst

Most Customer Success teams know what a “healthy” customer looks like. The problem is actually seeing it clearly, in time to act. Signals are everywhere: Product usage Support tickets Engagement Renewal timelines But they’re scattered across systems, and by the time they come together, it’s often too late. That’s why many teams either: • Rely on gut feel • Or spend hours trying to build a health score that no one fully trusts We put together a playbook on how to build and monitor a customer health score without a dedicated CS platform. Not a theoretical model An actual workflow using real data across sources It walks through how to: • Combine usage, support, CRM, and billing data into one view • Create a weighted health score based on real signals • Track changes over time so risk shows up early • Share a live dashboard the whole team can use Because being proactive in Customer Success is not about having more data It is about actually being able to use it in time If you are trying to move your team from reactive to proactive, this is a practical place to start: https://lnkd.in/ezuggCMg #CustomerSuccess #AI #DataAnalysis #AIDataAnalytics

Most teams don’t struggle with data because they lack it. They struggle because it takes too long to do anything useful with it. By the time a report is ready The moment has already passed By the time insights are shared The decision has already been made That’s the real problem. Looking across recent G2 reviews, a clear pattern shows up: Teams are not just getting answers faster They are changing how they work with data altogether → Bringing all their data into one place they can actually work from → Asking questions in plain language and getting real analysis back → Moving from raw spreadsheets to insights in minutes → Understanding the “why” behind the numbers, not just the totals → Building workflows they can reuse instead of starting from scratch One review said it best: “It feels like I have a partner in data analysis.” That’s the shift. Not more dashboards Not more tools Just faster clarity And workflows that actually compound over time Because when your analysis is: • real-time • repeatable • easy to build on You move faster You make better decisions You stay focused on what actually grows your business That’s where the advantage comes from Curious if others are seeing this shift too 👇 #AIDataAnalytics #G2 #Data #AIDataAnalyst

A lot of QBRs are built under pressure. Not because Customer Success teams do not care But because the process makes it hard to do them well What usually happens: Data gets pulled from 4 to 6 different systems CRM, product usage, support, billing It gets cleaned up in spreadsheets And turned into slides that mostly explain what already happened By the time the deck is ready There is not much time left to think about the customer The best QBRs feel different. They are not just summaries They are conversations They show progress Highlight what matters And make it clear what to do next We put together a step-by-step walkthrough showing how to build a QBR deck directly from live customer data. Not a template An actual workflow you can follow In the video, you will see how to: • Bring CRM, usage, support, and billing data into one view • Ask questions that surface real signals • Turn that into a clear story • Generate a QBR deck you can use right away Watch the full tutorial here: 📺 https://lnkd.in/e9WprYjN If you want to try it yourself, we also created a full playbook with sample data and free access: 📘https://lnkd.in/ebYmuSMS What has made the biggest difference in your QBRs? What changed things for your team? #CustomerSuccess #QBRs #AI #DataAnalytics #AIDataAnalyst

Software companies are going through a lot right now. Growth is harder. Budgets are tighter. Expectations are higher. Customer Success can’t afford to be reactive anymore. The problem is, most risk doesn’t show up during a QBR. It builds quietly over time: Usage starts to slip Support tickets creep up Engagement slows down Renewal gets closer The signals are there. They’re just spread across different systems. We put together a playbook on how to identify at-risk accounts before renewal using cross-source analysis. It walks through how to: • Combine CRM, product usage, support, and engagement data • Spot 30, 60, and 90-day trends that actually matter • Flag early warning signs across accounts • Rank accounts by real risk, not gut feel We also included: • Free sample data so you can try it yourself • A free Querri account to run the workflow If you’re trying to walk into QBRs with answers instead of surprises, this is a practical place to start. 🔗 https://lnkd.in/epKrkhSc Curious if others are seeing the same shift right now? #CustomerSuccess #QBRs #DataAnalytics #AIDataAnalyst

Most RevOps teams rebuild the same report every week. Pull pipeline from the CRM. Reconcile marketing data. Join billing numbers. Clean everything in Excel. They get it done. But it takes hours. And it pulls them away from the actual work. The problem is not the data. It is how long it takes to turn it into something useful. We just put together a full step-by-step tutorial on how to automate your weekly RevOps executive report using live data. In this walkthrough, you will learn how to: • Combine CRM, marketing, and billing data into one view • Clean messy exports without manual work • Build a live pipeline and forecast report • Automate weekly reporting so it runs on its own • Deliver clear, leadership-ready insights 🎥 Watch the full tutorial here: https://lnkd.in/eETqVKP5 📊 Follow along with the full playbook (includes free access + sample data): https://lnkd.in/eeTzXrp4 RevOps should not be the team rebuilding reports. It should be the team answering: What changed Why it changed What to do next Curious how much time RevOps teams are spending on weekly reporting... #RevOps #ExecutiveReports #AI #AIDataAnalyst #DataAnalytics

Your CSMs probably already sense which accounts are at risk. The problem isn't their intuition. It's that the signals they're picking up on (a champion going quiet, tone shifting in QBRs, support tickets moving from "how do I?" to "can I export my data?") don't show up in any dashboard. We identified 5 churn signals in your team's notes, tickets, and conversations, not in your health score. Each one includes what it looks like in practice and a concrete action your team can take immediately. Retention is the growth strategy right now. The best thing CS leaders can do is give their teams the frameworks and tools to catch these signals before they become a cancellation. Swipe through below. And if it's useful, share it with your CS team. We built Querri to help CS teams surface exactly these kinds of insights, from the unstructured data that dashboards can't read. Learn more: https://lnkd.in/eRzKKbQy #CustomerSuccess #AIDataAnalyst #CSM #UnstructuredText