Retail

Retail Analytics & AI for Availability, Margin, and Speed

Retail analytics delivers ROI when availability improves, discount leakage reduces, and daily exceptions are visible. We build forecasting, KPI governance, and operational reporting that teams actually use.

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Key problems

Stockouts + overstocks despite planning effort

Planning inputs and signals are fragmented, so inventory buffers don’t reflect real uncertainty.

Pricing and discounting without guardrails

Teams discount to hit targets, but lack visibility into discount depth/frequency and margin impact by SKU.

Slow operational reporting + metric disputes

Reports refresh late, and different teams calculate KPIs differently—leading to debate instead of action.

Fragmented channel and customer performance views

Store, marketplace, and D2C signals live in silos, making it hard to spot what’s driving growth.

What we deliver

Forecasting + inventory planning foundation

Clean demand signals, forecast accuracy tracking, and service-level buffers tied to inventory levers.

Pricing & promotions discipline (guardrails)

Competitor indices, price ladders, discount leakage reporting, and rules for acquisition vs retention offers.

Near real-time operations reporting (exceptions-first)

Sales vs target, stock cover, fill rate, late delivery exceptions—designed for daily intervention.

Governed KPI layer + adoption cadence

One semantic model, documented definitions, monitoring, and a weekly review cadence that drives usage.

Deliverables

Architecture + roadmap + MVP plan (what to build first, why, and how fast)

Curated models (product, inventory, orders, pricing/promo, store/region)

Role-based dashboards (Ops / Merch / Finance / Leadership)

Data quality + freshness monitoring and KPI governance approach

Frequently Asked Questions

How does AI improve forecasting?

AI analyzes seasonality and demand signals to transform uncertainty into optimized inventory buffers—ensuring product availability while preventing overstock.

What is pricing discipline analytics?

A system of dashboards and rules that shows discount depth/frequency, competitor indices, and margin impact by SKU and segment.

How do you prevent KPI mismatch across teams?

By implementing a governed semantic layer. This centralizes definitions and calculations into a “Single Source of Truth,” ensuring every department—from finance to sales—is looking at the same verified numbers.

Fastest retail MVP to launch?

Sales + inventory health + exception reporting (store/warehouse) with a KPI layer and a weekly operating cadence.

Typical timeline?

4–12 weeks depending on number of sources, refresh needs, and KPI alignment.