Analytics and Reporting in Support Software

2 weeks of analysis → 2 hours. The AI trio every support leader needs: 🔍 Perplexity: 𝗠𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗧𝗿𝘂𝘁𝗵 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 Preparing for my upcoming Support Driven Expo talk on AXIS? Perplexity gives me: → Cited deflection rate data I can trust on stage → Industry benchmarks with actual sources → No more "according to some study" embarrassments ⚡Dust: 𝗠𝘆 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗦𝘂𝗽𝗲𝗿𝗽𝗼𝘄𝗲𝗿 Lemuel Chan and I built a "Support Analyst" assistant that's saving us WEEKS of manual work. Here's the magic: • Connected to our Snowflake data warehouse • Analyzes Front support inbox data (minus sensitive info) • Trained on our specific support operations Now I can ask: "𝘞𝘩𝘢𝘵'𝘴 𝘵𝘩𝘦 𝘴𝘦𝘯𝘵𝘪𝘮𝘦𝘯𝘵 𝘵𝘳𝘦𝘯𝘥 𝘧𝘰𝘳 𝘌𝘯𝘵𝘦𝘳𝘱𝘳𝘪𝘴𝘦 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘪𝘯 𝘘1?" "𝘚𝘩𝘰𝘸 𝘮𝘦 𝘤𝘩𝘶𝘳𝘯 𝘪𝘯𝘥𝘪𝘤𝘢𝘵𝘰𝘳𝘴 𝘣𝘺 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘪𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯 𝘱𝘢𝘵𝘵𝘦𝘳𝘯𝘴" "𝘎𝘳𝘢𝘱𝘩 𝘰𝘶𝘳 𝘷𝘰𝘭𝘶𝘮𝘦 𝘵𝘳𝘦𝘯𝘥𝘴 𝘣𝘺 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘧𝘦𝘢𝘵𝘶𝘳𝘦" Real example: Just used it for our FY27 forecasting. What used to take 2 weeks of spreadsheet hell? Done in 2 hours. 💟 Front:  𝗠𝘆 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗖𝗼𝗺𝗺𝗮𝗻𝗱 𝗖𝗲𝗻𝘁𝗲𝗿 While Perplexity researches and Dust analyzes, Front AI orchestrates everything: → Topics auto-categorize conversations from real patterns → Smart CSAT predicts satisfaction without surveys → Copilot drafts responses with actual context, not generic templates → Complete control over when and how AI assists The beauty? Front AI learns from what's actually happening in our inbox, not from generic training data. 𝗧𝗵𝗲 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗿𝗶𝗼: Morning research: Perplexity tells me deflection rates are averaging 40% industry-wide Afternoon analysis: Dust shows me we're at 30% but our AXIS scores are higher Real-time execution: Front AI ensures every interaction maintains quality while we scale Strategic insight: We're deflecting the RIGHT tickets, not just more tickets 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝘂𝗻𝗹𝗼𝗰𝗸𝘀: ✅ Zero dependency on data team for routine analysis ✅ Real-time VOC without waiting for quarterly reports ✅ Product influence backed by actual customer patterns ✅ AI assistance with human control at every step The formula is simple: 𝘌𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 (𝘗𝘦𝘳𝘱𝘭𝘦𝘹𝘪𝘵𝘺) + 𝘐𝘯𝘵𝘦𝘳𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 (𝘋𝘶𝘴𝘵) + 𝘊𝘰𝘯𝘵𝘦𝘹𝘵𝘶𝘢𝘭 𝘈𝘐 (𝘍𝘳𝘰𝘯𝘵) = 𝘚𝘶𝘱𝘱𝘰𝘳𝘵 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱 𝘴𝘶𝘱𝘦𝘳𝘱𝘰𝘸𝘦𝘳 While others guess, we know. While others wait for reports, we act. While others lose control to AI, we orchestrate it. 𝗪𝗵𝗮𝘁 𝗔𝗜 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀?

Love me some Pre-Built Reports!! Help Desk Analyze Tab 🔥 📊 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐢𝐭? The Help Desk Analyze Tab gives support team leaders a comprehensive overview of their Help Desk’s performance. By providing access to key metrics and trends, this tab enables data-driven planning and decision-making. 📈 𝐖𝐡𝐲 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫? Having a 12-month view of key data empowers support leaders to: ↳ Identify trends to evaluate the effectiveness of past strategies. ↳ Plan resources strategically, ensuring better alignment with business goals. ↳ Make informed decisions, optimizing processes to improve customer satisfaction and team efficiency. ↳ Drive success by turning insights into actionable strategies. ⚙️ 𝐇𝐨𝐰 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐰𝐨𝐫𝐤? ➡️ Analyze Tab Access ↳ Navigate to Workspaces > Help Desk > Analyze Tab. ➡️ Review Key Metrics (365-day historical view): ↳ Tickets created. ↳ First reply averages. ↳ First response SLA completion. ↳ Messages received. ↳ Messages received by time of month. ↳ Next response SLA completion. ↳ Tickets closed. ↳ Time to close average. ↳ Time to close SLA completion. ↳ Average CES feedback. ➡️ AI Insights ↳ Click Actions > View > AI Insights on an individual report to generate detailed insights. ➡️ Filter Reports ↳ Use quick filters on the dashboard to refine your view of the data. 👥 𝐖𝐡𝐨 𝐠𝐞𝐭𝐬 𝐢𝐭? ↳ Available to users with a Service Hub Pro or Service Hub Enterprise seat.

🚀 From Data to Decisions: Technical Support Insights with Power BI One of the things I love about data analytics is its ability to transform raw numbers into actionable insights. Over the years, I’ve worked on multiple IT ticketing dashboards, helping teams track support tickets, monitor agent performance, and improve resolution times. In my previous organization, I worked extensively with BigQuery and various visualization tools to automate ticketing dashboards, eliminating manual reporting efforts and ensuring teams had real-time insights at their fingertips. That experience played a key role in shaping my latest project: developing a Power BI dashboard to analyze IT support ticket trends. 🔑 Key Insights from This Project: 📌 Ticket Volume Trends – Analyzed peak hours and ticket distribution across different sources and countries, providing clarity on workload patterns. 📌 Resolution Efficiency – Measured resolution times across ticket sources, identifying opportunities to enhance response speed and optimize workflows. 📌 Agent Workload Balance – Assessed ticket distribution among agents to ensure an even workload and improve overall efficiency in handling support requests. A huge thank you to Anh Leimer and Hien Tran for your invaluable feedback and support throughout this project. It made a world of difference! 🙌 And a special shoutout to Injae Park for sharing the amazing IT Service Ticket Overview dashboard. I loved the design and took inspiration from it for my Resolved Summary Page. ✨ Every project, every tool, and every challenge has been a stepping stone. Whether it was working with BigQuery and Looker Studio in my previous role or diving deep into Power BI now, the goal remains the same --> turning data into insights that drive real change. Have you worked on a dashboard or automation project that made a big impact? Let’s share insights! Interactive Dashboard Link: https://lnkd.in/gpAKgCec #DataAnalytics #PowerBI #DashboardDesign #Automation #BusinessIntelligence #ProblemSolving #Efficiency #DataVisualization