Assortment coverage CSV
textareaRequired input
Noderan MarketplaceData & Intelligence
Demo ReadyLive E2E verifiedDeterministic~10s
Merchandising Gap Analyzer
Profiles assortment rows against demand signals and flags category coverage gaps. Returns structured, review-ready output without triggering any external action.
Profiles assortment rows against demand signals and flags category coverage gaps.
Workflow summary
Profiles assortment rows against demand signals and flags category coverage gaps.
What is Merchandising Gap Analyzer?
Merchandising Gap Analyzer is a ready-to-run Noderan marketplace workflow for data & intelligence teams. It helps users move from a manual process to a repeatable automation with visible credit cost and app-based execution.
- Who is it for?
- Merchandising and buying teams
- What problem does it solve?
- Profiles assortment rows against demand signals and flags category coverage gaps.
- How does it work?
- Profiles assortment rows against demand signals and flags category coverage gaps.
- How does pricing work?
- 1 credit per run.
- What is the next action?
- Open the app marketplace to activate this workflow, or review credit pricing.
Merchandising Gap Analyzer FAQ
Short answers for activation, pricing, inputs, and execution review.
Profiles assortment rows against demand signals and flags category coverage gaps. Returns structured, review-ready output without triggering any external action.
Expected inputs
Demo input
Csv
item,status,score Trail shoes,ready,88 Winter boots,review,42 Sandals,ready,81View raw JSON
{
"csv": "item,status,score\nTrail shoes,ready,88\nWinter boots,review,42\nSandals,ready,81"
}Output highlights
- rowCount
- columnCount
- headers
- findings
- previewRows
Example result
Row Count
3Column Count
3Headers
itemstatusscore
Findings
Assortment rows include one under-covered category.Parsed rows are ready for operator review
Preview Rows
1 structured item
View raw JSON
{
"rowCount": 3,
"columnCount": 3,
"headers": [
"item",
"status",
"score"
],
"findings": [
"Assortment rows include one under-covered category.",
"Parsed rows are ready for operator review"
],
"previewRows": [
{
"item": "Example",
"status": "review",
"score": "72"
}
]
}Use cases
- Spot assortment gaps before the season locks.
How it works
- 1
Review the workflow and expected credit cost.
- 2
Connect the tools or inputs required for execution.
- 3
Run the workflow and inspect outputs in the app history.