Demand-Aware Assortment and Feature Prioritization

Retail teams manage large catalogs with limited development and shelf space, leading to slow, intuition-driven choices and frequent overstock or missed sell-through. Product and R&D leaders lack a consistent way to compare the incremental value of a new feature or SKU versus expanding existing lines, creating costly experiments and long time-to-decision.

Build an operational pipeline that combines demand forecasting, causal models, optimization and human governance to create a prioritized backlog of SKUs and product features for development and replenishment.

• Data: POS and e-commerce sales, web and search analytics, returns, customer reviews, product attributes, price/promotions, supplier lead-times, and inventory snapshots.
• Models & tools: hierarchical time-series forecasting for SKU-level demand, causal uplift/cannibalization models for overlap effects, constrained optimization for assortment/mix decisions, and LLMs to surface feature signals from reviews and competitor catalogs.
• Workflow: generate weekly ranked recommendations with scenario simulations (margin, stockout risk, supplier constraints), run small-scale A/B or regional rollouts for top candidates, then feed results back to update models.
• Human-in-the-loop & governance: product managers and merchandisers review and adjust constraints (brand rules, sustainability, supplier limits) before approval; include explainability reports, performance SLAs, and drift monitoring for model outputs.

Teams typically reduce time-to-prioritize experiments and SKUs by 30-60% and cut excess inventory for tested assortments by 10-30%. Firms can expect assortment-level gross margin improvement of about 1-5 percentage points and sell-through rate uplifts in the high-single to low-double digits for prioritized lines, with smaller sample rollouts de-risking larger investments.