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Unit Return Rate Sequential Input-Change Attribution
x402
Unit Return Rate Sequential Input-Change Attribution is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-27).
Performs sequential (waterfall) attribution analysis to decompose how changes in sold units and returned units drive a change in the unit return rate between two periods.
Price / call$0.01
Success—
StatusUnknown
How this service works
Unit return rate - Sequential input-change attribution: Sequential input-change attribution for Unit return rate. Supplied retail figures only. Local calculation and scenario analysis; no live inventory, account access, platform fee lookup or order execution. One of 25 profiles or calculations across 20 shared operations in this sector. 0.01 USDC includes one bounded sequential input-change attribution deliverable.
Output
Returns a sequential (waterfall) attribution breakdown showing the before and after unit return rates, the total change in return rate, and the individual contribution of each input change (sold units delta and returned units delta) in the order they are applied sequentially, so users can understand which factor drove how much of the movement.
Example use cases
Quarterly return rate driver breakdown
“Last quarter we had 8,000 sold units and 640 returns; this quarter we had 9,200 sold units and 828 returns. Do a sequential attribution waterfall to tell me how much of the return rate shift was driven by the change in units sold versus the change in number of returns.”
Diagnosing a spike in return rate
“Our return rate jumped — before it was 500 sold and 20 returned, now it's 480 sold and 30 returned. Break down that return rate change sequentially so I know whether to blame a drop in sales volume or an actual increase in returns.”
Scenario planning for sales volume impact
“If we keep returns flat at 50 units but grow sales from 1,000 to 1,250 units, how does that change our return rate and what's the attribution of that improvement to the volume increase alone?”
Technical details
Endpoint
POSThttps://market.datapackvibe.com/x402/assistant-sector-retail-unit-return-waterfall?utm_source=zero.xyzWhen to prefer
Use this endpoint when you need to isolate and quantify how much each individual input variable (sold units vs returned units) contributed to a change in the unit return rate between two time periods, using a sequential waterfall methodology. Prefer this over simple delta calculations when stakeholders need to understand cause-attribution rather than just the net change. It is specifically suited for retail scenarios where only aggregated sold and returned unit counts are available.
Side effects
- sold_units equal to zero causes division-by-zero in return rate calculation — likely returns an error or undefined result
- extremely large unit values near the 1 trillion maximum may cause precision issues
- missing required fields (sold_units or returned_units) will cause schema validation failure
- returned_units exceeding sold_units produces a return rate over 100%, which is mathematically valid but may be flagged
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| after | object | |
| before | object |
Tags
x402
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