Stable
📈 Live history is activating for this object — the automated backfill is in progress.
⌁ mcp.call("adw-204") v1.0 ← All products
Retail/Logistics · Index Paid
Shelf-Share Velocity
Enable retailers and CPG brands to benchmark their product distribution efficiency against market leaders to identify untapped shelf-space opportunities.
- Refresh
- daily
- History
- single vintage
- Plan
- Paid
Forward outlook
Prediction
- Horizon
- —
- Recommended use
- Enable retailers and CPG brands to benchmark their product distribution efficiency against market leaders to identify untapped shelf-space opportunities.
Methodology
How it's built
Aggregate point-of-sale transaction volumes and inventory turnover rates by SKU and store location, then normalize against total category sales to derive a relative distribution velocity score.
NielsenIQIRIor Statista retail sales data
Version 1.0 · validated to beat a naive baseline · benchmark: RetailVelocity
Live response
The object an agent receives
One call returns the answer with its reasoning attached — the live Intelligence Object for ADW-204.
GET /v1/intelligence/adw-204
{} IOM schema
The agent-callable contract
Every product conforms to the Intelligence Object Model — typed, versioned, and discoverable.
- product_id
- entity
- score
- trend
- confidence
- top_drivers
- prediction_horizon
- recommended_use
- methodology_version
- freshness
- coverage
- source_lineage
- allowed_use
MCP tool: adw.adw_204
Access options
Consume it your way
-
Dashboard
Read the score + drivers in the console.
-
REST API
/v1/intelligence/adw-204
-
MCP tool
adw.adw_204
-
Marketplace
Discoverable by any MCP agent via the MCP registry.
-
White-label
Embed under your own brand (Platinum).
Plan requirement
Depth scales with the plan
- Free Sample object — current score only
- Gold Full drivers + history + confidence
- Platinum White-label + bulk + SLA
Related products
Browse all Retail/Location · Score Paid
Global Real Estate Affordability Score
How affordable is real estate vs income?
Method: 100-(Norm_Price_Income+Norm_Rent_Income)
monthly · next ~30d score 42
View object → Retail/Marketing · metric Paid
Promo-Elasticity Gap
Identify underperforming promotional investments to reallocate trade spend toward high-elasticity categories for maximum revenue lift.
Method: Calculate price elasticity of demand using historical sales volume and discount depth data, then compute the gap between actual promo ROI and the theoretical maximum ROI to generate a prioritization s
daily · next ~24h
View object → Retail/Location · Index Paid
Demographic Affinity / Site-Selection
Identify high-potential zip codes for new retail locations by matching store concepts to local population characteristics.
Method: Aggregate Census demographic variables (income, age, household size) with proprietary spending propensity scores to generate a site-fit affinity index.
daily · next ~24h
View object →