Querri

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

Customer Success leaders are under real pressure right now. Churn is a concern for many software companies, and every customer conversation matters more. But strong Customer Success is not just about preventing loss. It is about helping customers see value, make progress, and feel confident they made the right investment. That is why QBRs matter so much. When done well, they help teams prove impact, strengthen relationships, and create a clearer path to renewal and growth. The challenge is that building a strong QBR often takes too much manual work. Customer data is scattered across CRM records, product usage, support tickets, and billing systems. Pulling it together into a clear, compelling story can take hours. We just created a new playbook: How to Build a QBR Deck from Live Customer Data in Minutes It walks through how to: • Combine CRM, usage, support, and billing data into one view • Surface meaningful trends and customer signals • Build a clear, data-backed narrative • Generate a polished QBR deck—ready to present We also included sample data, and you can follow along with a free Querri account. If your team wants to create QBRs that are faster to build, more meaningful to customers, and stronger for long-term retention, this is a practical place to start: https://lnkd.in/ebYmuSMS #CustomerSuccess #DataAnalytics #QBR #AIDataAnalyst

Most client-ready marketing reports don’t come down to effort. They come down to how quickly you can turn data into a clear story. Here’s a simple workflow to build client-ready marketing reports in minutes: Step 1: Bring your data together Pull in your core sources (ads, CRM, analytics). Even if they live in different places, start by getting them into one place. Step 2: Clean and structure your data (just ask) Instead of manually fixing spreadsheets, you can simply ask: → “Clean this dataset and standardize dates and campaign names” → “Remove duplicates and fix inconsistent formats” The goal is the same—reliable data—but without the manual prep work. Step 3: Combine sources for context Join campaign data with conversions, revenue, or pipeline so you’re not just reporting on activity—but outcomes. Step 4: Identify the story Look at trends over time, top-performing channels, and what changed week-over-week or month-over-month. Step 5: Turn insights into visuals Build charts that answer real questions: What’s driving performance? Where are we improving? What needs attention? Step 6: Package it for your audience Structure your report so it’s easy to follow: → Key metrics → What changed → Why it matters → What to do next We turned this into a step-by-step playbook (with free sample data) so you can follow along: 👉 https://lnkd.in/euy4QtFT Here's also a full tutorial video walking you through each step in detail: 📺 https://lnkd.in/ezyjWv3K And if you want to try it yourself, Querri has a forever-free tier, so you can build this kind of report without setting anything up. The real shift isn’t just speed. It’s being able to go from question → analysis → report without getting stuck in the middle. #MarketingData #DataAnalytics #AIDataAnalyst #MarketingReports

Thank you, Charleston AMA - American Marketing Association, for the shout-out and for supporting local Charleston tech companies! We appreciate you 🙌. #Marketing #AIforMarketing #MarketingReporting

Marketing measurement has never been more important. But for a lot of teams… it’s also never been more frustrating. There’s no shortage of data. The challenge is turning that data into something you can actually use. → What’s working vs. what just looks good on a dashboard? → Where are we actually driving pipeline or revenue? → What should we do differently next week? That’s where effective analysis matters. Not more reports. Not more tools. Better ways to connect, understand, and act on your data. We’re excited to be included in this list of marketing measurement tools: 👉 https://lnkd.in/eKu8kyaz What stands out across the category: Most tools either: • track performance without helping you interpret it • or require a lot of setup before you can trust the results The gap is turning data into decisions. That’s exactly where modern workflows are evolving—toward tools that take you from messy data → analysis → insight → action in one place, without requiring a full data team At Querri, we’ve focused on making that process approachable for business users—so marketers can explore, analyze, and understand their data without getting stuck in manual work or complex tools. If you’re evaluating measurement tools this year, ask: Does this help me clearly understand what’s working—and what to do next? Because that’s what actually moves marketing forward. #Marketing #Data #AI #AIDataAnalyst #DataAnalytics