Dataset Scanning & Metadata Inference: Scoop rapidly profiled the dataset, inferring schema, time range, product hierarchies, and surfaced dimensions such as model series, price tiers, and region. This accelerated context-building and allowed non-technical staff to see their data’s analytic potential instantly.

• Automatic Feature Enrichment: Leveraging agentic ML, Scoop synthesized new features by extracting product attributes from titles (screen size, GPU, CPU, etc.), creating robust price categorization and purchase timing attributes that empowered deeper segmentation beyond what was natively available.
• End-to-End KPI & Slide Generation: Drawing on organizational priorities, Scoop programmatically generated and visualized critical KPIs—revenue, units sold, average order value, and highest single order—segmented by month and distribution hub. It mapped shifts in average pricing, product category shares, top-selling configurations, and historical trends.
• Interactive Visual Exploration: Seamless drill-down tools enabled users to explore sales heatmaps, price sensitivity, and ordering behavior by customer type and channel, uncovering the interplay between product features and consumer choice.
• Agentic Predictive Modeling: Automated ML surfaced non-obvious relationships: how GPU or screen size defined price tiers, how time-of-day or month influenced bulk-versus-single-unit purchases, and how inventory mix should evolve to capitalize on demand spikes. The system provided explainable rules (e.g., bulk purchases for budget models in January, premium SKUs' solo buying patterns) and flagged their accuracy and exceptions.
• Narrative Synthesis & Actionable Reporting: Scoop's agentic AI orchestrated all the above into clear, decision-ready narratives, linking causality between supply chain performance, pricing levers, and consumer timing. Automated insights resolved which segments and configurations warranted aggressive stocking for peak retail cycles and where to adjust price strategies to maximize conversion.