RETAIL + FOOD

Omnichannel Inventory & Markdown Optimization

Know which SKUs to mark down and which to move, before the season turns.

Retailers lose billions to overstock markdowns and regional stockouts. Merchandising teams can upload POS transaction logs, warehouse inventory levels, e-commerce return data, and store allocations. The platform calculates the complex intersections between local purchasing trends, seasonal shifts, and specific SKU velocities, so planners stop working from a static spreadsheet that was already stale when it was built.

Instead of guessing, planners can see exactly how a regional weather anomaly moves fast-fashion sales. Exploring predictive power scores lets inventory managers forecast which product lines need aggressive markdowns versus reallocation to a high-demand region, turning deterministic, citation-backed analysis into protected gross margin.

Inventory Finding Citations

¹ 44% of "overstock SKUs" showed "slow week-2 velocity"

² 37% of "markdowns" traced to "regional weather shift"

³ 31% of "stockouts" hit one "store cluster"

⁴ 26% of "returns" concentrated in "one size curve"

⁵ 21% of "aged stock" sat in "low-traffic stores"

⁶ 16% of "reallocations" beat "markdown margin"

⁷ 9% of "SKUs" drove "half of carrying cost"

Location Intelligence

Use Steeped AI's location intelligence to enrich store data with regional weather, local events, and neighborhood context, so localized SKU demand becomes predictable. Two stores running identical allocations stop looking like the same store.

Statistical Significance Testing

Steeped AI's automated significance testing proves whether a regional sales spike is a mathematically real trend or a temporary anomaly. Markdown decisions worth millions stop resting on one unusually warm week.

Data Preparation

Use Steeped AI's data preparation to reconcile POS exports, warehouse counts, and e-commerce returns into one clean SKU dataset, unifying size curves and product codes. Cross-channel comparison only means something once every system agrees on what a SKU is.

Inventory from 3 systems was merged into one schema

SKUs "BLK-M", "blk_med" and "Black/M" were unified

Returns were matched back to their originating orders

Removed column "legacy_bin" because 84% were blank

Duplicate transfer rows for one shipment were merged

generating new dataset

NEW DATA

Relational Metrics

Steeped AI's automated metric breakouts calculate the hidden intersections between pricing tiers and inventory velocity without a single manual pivot table. Thousands of hardcoded metrics run before a planner opens the platform.

Predictive Power Score

Steeped AI's regression predictive power identifies the early sales velocity metrics that most strongly forecast a future markdown. Two-variable regression runs across every valid relationship and returns one percentage, so buying decisions rank signals instead of debating them.

Expand

Rank Markdown Predictors

Slow Week-2 Velocity

44%

Size Curve Skew

35%

Low-Traffic Store

27%

Late Delivery

18%

Base Column

sku_signal ▾

Value Column

markdown_risk ▾

44%ofSlow Week 2=Markdown

predictive power

88%

markdown count

132

SKU count

300

see examples

Talk to Your Data

Steeped AI's talk to your data lets a merchandising lead ask which lines need reallocation rather than markdown and get ranked, cited findings straight from the transaction record. No exports, no waiting on an analyst.

PLAN

Talk to Your Inventory

markdown riskreallocation

Which lines should move, not discount?|

Find Insights

• 16% of reallocations beat "markdown margin"

• "slow week-2" predicts future markdowns

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.