Adasight’s Post

Here’s something most dashboards don’t tell you: How much to care. You see a red arrow and the instinct is to fix it right away. But not every drop means something. A 2% dip in traffic isn’t the same as a 2% dip in inbound leads. One is fine and other is not. Execution is knowing which one actually needs attention and which one to let go.

Most tools stop after one data source. Clay doesn't. Waterfall enrichment combines data from several sources so you always get the most accurate, up-to-date information. In this clip from our HubSpot Data Hub vs Clay webinar, Diana Marcela Gonzalez shows how to use it in Clay to: 👉 Pull exact company revenue instead of vague ranges 👉 Auto-source new contacts with only a domain or LinkedIn URL 👉 Filter by title, location, and seniority just like ZoomInfo or Apollo (but smarter) 👀 Watch the full replay here: https://lnkd.in/eViRzaUg

What if your next big deal came with a built-in cheat sheet? 😉 A data room doesn’t just protect your documents — it reveals who’s most engaged, what they’re reading, and when they’re ready to move. 💡 Real-time analytics help your team focus on what matters most — turning insights into action (and prospects into partners!). 🔗 See how top sales teams use analytics to close faster → https://lnkd.in/gax_2ihE #SalesIntelligence #B2BSales #DealAnalytics #KytesApp

Here’s a pattern I see in almost every organization: Every new request = a new dashboard. Every slight variation = a new dashboard. Before long, you’ve got 400 dashboards… and nobody trusts them. 🔥 The smarter way is consolidation. 🔥 One semantic model as the single source of truth 🔥 Fewer dashboards, built smarter Users can create their own views without breaking governance ☑️ One client reduced 400 dashboards to under 40. ➡️ Adoption went up. Confusion went down. Dashboards aren’t the problem. The foundation is. Yes or No? Let's chat!

Dashboards don’t fix problems — people do. But people move faster when the system tells them what to do next. That’s the shift happening in leading IT organizations: visibility connected to workflow, so nothing stalls. Dashboards → Decisions → Results.

Dashboards don’t fix problems — people do. But people move faster when the system tells them what to do next. That’s the shift happening in leading IT organizations: visibility connected to workflow, so nothing stalls. Dashboards → Decisions → Results.

Most dashboards fail — not because they’re poorly built, but because they were never designed to drive decisions. They summarize what happened, not why it happened or what to do next. The best dashboards... - Are built around questions, not metrics. - Include thresholds or triggers that define when to act. - Integrate timing and interactions (not just static KPIs). Without that, you’re just watching the past in high-def. Great dashboards don’t just display data — they close the loop between measurement and behavior. 💭 Question: What’s the most useful dashboard you’ve ever seen — and why did it work?

I built a repeat-customer intelligence system that answers the question every retailer obsesses over: "Who becomes loyal and what makes them come back?" For this Month's Onyx Data DNA challenge, I designed RepeatIQ Commerce, a 5-page Power BI framework for retention, channel effectiveness, pricing, and discounts. What I built (and why it matters): 🔍 Cohort Retention Analysis Track 1st → 2nd → 5+ → 10+ purchases with a retention heatmap. Why it matters: Most teams know total buyers; few can show who returned and when. 💰 Discount Effectiveness Matrix Bubble map of discount % × repeat rate × order volume by code. Why it matters: Not all promos are equal; some build loyalty, others just give away margin. 📊 Revenue Waterfall Attribution MoM breakdown: Start → Volume Δ → Price Δ → Discount Δ → Refund Δ → End. Why it matters: "Revenue is down" isn't actionable. Knowing the specific driver is. 🌍 Country Pricing Intelligence ASP vs. Global with dynamic currency switching. Why it matters: Price gaps = hidden margin leakage or competitive risk. ⚠️ Refund Loss Decomposition Analyzed by channel × payment × product × reason code. Why it matters: Pinpoint exactly where dollars leak and why. Technical approach: → Star schema data model → Advanced DAX for cohort retention & lifecycle (2+/5+/10+) → Field parameters for dynamic metrics → MoM context with conditional formatting → ZoomCharts drill-downs for exploration The 5 pages: Executive Summary • Customer Intelligence • Product Performance • Country Analysis • Operations & Risk If you were building a retention dashboard, which metric would you prioritize first: cohorts, discounts, or pricing? Drop a comment. I'd love to hear what resonates 👇 Big thank you to the sponsors for this fantastic challenge: Onyx Data, ZoomCharts, EnterpriseDNA, BCS, The Chartered Institute for IT, Smart Frames UI, Data Career Jumpstart #DataStorytelling #CustomerRetention #dataDNA #ZoomCharts #PowerBI #DataVisualization #DataStorytelling #BusinessIntelligence #DataArcus

3 dashboard types – 1 foundation: the metrics map Most dashboards are built backward. Teams start with visuals – not with logic. But before you decide how to show something, you need to know what you’re actually showing and why. There are 3 types of #dashboards – and each one starts from a different question: Diagnostic: Why did something change? Operational: How is the process running? Exploratory: What patterns can we find? No matter which one you build, the foundation is the same: the metric map. It connects metrics into a structure of Result → Components → Drivers and makes your dashboards logical, not just visual. #metricsmap

🗺️I agree with Dmitry Nekrasov .  I’ve seen this pattern play out multiple times. Without using a structured approach you end up chasing disconnected KPIs. Having a clear "metrics map" (Result → Components → Drivers) solves a significant problem : dashboards that show many metrics but can't explain how they connect to outcomes. For reference: "Metrics map" is similar or relates to what other people call a "metric tree"/"measurement framework". If someone can’t trace a metric back to business results, it’s time to map before you visualize. I have seen people that think they need better dashboards that are more beautiful. In reality, they need better logic. Structure first, visuals second. Simple but not always done. We could take this a step further incorporating leading/lagging indicators with set thresholds for early warnings (and where relevant assigning ownership to each element in the metrics map). For instance, in a conversion funnel map, assign a team lead to monitor drivers like page load time (a leading indicator), with predefined health corridors—if it exceeds 3 seconds, it triggers an alert to prevent outcome dips in results like overall CR. This evolves the static map into a proactive steering tool, fostering accountability and faster decisions. #Analytics #CRO #DataStrategy #DashboardDesign #NorthStarMetric #GrowthAnalytics