Sales & Customer Analytics Dashboard

Igniting Powerful Insights

• The Challenge Sales, order, and customer data were scattered across countries, categories, and sales teams with no unified reporting layer. Leadership could not easily answer basic questions which regions were underperforming, which customers were most valuable, or why the return rate sat at a notable 20.5% because the raw data lived in disconnected tables with no consistent definitions of revenue, orders, or customer segments.
• The Process Raw transactional data was ingested and cleaned in Power Query, then modelled into a star schema with a central Orders fact table linked to Customer, Product, Region, and Date dimension tables. Core DAX measures - Net Revenue, YTD Revenue, YoY Growth %, Average Order Value, and Return Rate % were built once and reused consistently across all five report pages, ensuring every number on every page told the same story.
• Complexity and Innovation Beyond standard KPI reporting, the solution layers in an RFM (Recency, Frequency, Monetary) scoring engine to classify customers into Champions, Loyal Customers, and other segments, alongside a Customer Lifetime Value (CLTV) model. A discounting-impact waterfall compares gross Revenue against Net Revenue by month, while a salesperson performance bubble chart correlates order volume against revenue to surface top and underperforming reps at a glance.
• Adaptive Learning for Enhanced Accuracy The model was refined iteratively: 2025 vs 2026 trend comparisons were added to validate seasonality, return-rate thresholds were recalibrated by category and segment as new data arrived, and drill-through paths from summary visuals to the Customer Details page were tuned based on how stakeholders actually explored the data — improving both accuracy and adoption over time.