Our historical sales data is signalling shifts that we don’t fully understand yet, so the priority is a diagnostic analysis that tells us not just what changed, but why it changed. The raw tables cover order details, customer attributes, product SKUs and daily revenue going back three years. Primary questions on the table: • How have sales trends evolved month-to-month and season-to-season? • Which customer segments are driving (or dragging) revenue, and how has their purchasing behaviour shifted? • Which products or product groups are over- or under-performing once promotions, returns and stock-outs are factored in? A clean, reproducible workflow in SQL, Python (Pandas, NumPy, Sci-Py) or R is essential so the team can rerun the analysis after future data drops. Visual explanations—preferably in Tableau, Power BI or Matplotlib/Seaborn—should accompany every finding, making it easy for non-technical stakeholders to see root causes at a glance. Deliverables 1. Processed dataset with all transformation scripts documented. 2. Diagnostic report (PDF or slide deck) covering sales trends over time, customer purchasing behaviour and product performance analysis, each supported by clear visuals and commentary on underlying drivers. 3. Interactive dashboard or notebook that lets us filter by date range, customer segment and product line to test scenarios ourselves. 4. Brief hand-off call or video walkthrough so our analysts can maintain and extend the work. Accuracy, transparency and clear reasoning trump flashy charts; every number must reconcile with the source data. If that aligns with your approach, I’m ready to provide sample extracts and schema details right away.