datawebotPrepare inputs for a pricing decision
Separate source prices from your pricing rules.
Keep the right fields.
Workflow field map| Field / step | Input | Rule |
|---|---|---|
| Offer | Seller, variant and unit basis | Marketplace listings are not always comparable. |
| Margin | Your authorized internal cost data | Not available from public product extraction. |
| Decision | Your rule or model output | Keep approval and publication separate. |
Match the offer before choosing a pricing rule.
Amazon, Walmart and Alibaba references help define the collection scope. The framework below is a workflow design; it does not describe those companies' private repricing algorithms.
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| Keep | Record | Decision |
|---|---|---|
| Amazon comparison | Exact marketplace, product variation, seller, condition and fulfillment | A product identity alone may not identify the offer being priced. |
| Walmart comparison | Exact product configuration, seller and delivery context | Do not compare a marketplace seller offer with a different configuration. |
| Alibaba comparison | Specification, quantity tier, minimum order and quote basis | A bulk unit quote is not directly comparable with a single-unit retail price. |
| Internal constraints | Your costs, fees, minimum margin and publication policy | Public product pages cannot supply your own costs or authorize a price change. |
Evaluate a proposed price against a floor.
Illustrative scenario in one currency: unit cost 20, fixed fees 2 and a 25% margin requirement, with no percentage fees or taxes. The floor is (20 + 2) / (1 − 0.25) = 29.333…, rounded upward to 29.34. These are invented scenario inputs, not retailer observations.
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| Keep | Record | Decision |
|---|---|---|
| Comparable competitor offer · 32.00 | Candidate 31.99 | Above the example floor; still requires match, freshness and publication review. |
| Comparable competitor offer · 28.00 | Candidate 27.99 | Below the example floor; reject or use an authorized exception process. |
| Bulk quote · 24.00 at 100 units | No retail candidate | Resolve quantity, logistics and specification differences first. |
| Unknown shipping or stale observation | Hold decision | Collect the missing context rather than inventing a delivered price. |
Content reviewed 2026-09-23.
Before you use the output
Amazon, Walmart and Alibaba are source references, not a promise of immediate execution. datawebot does not update prices or supply an automatic repricing engine.
Check one record first.
Start with the smallest useful scope. Compare the returned record with its source, confirm variant identity and inspect missing fields before applying the mapping to a larger dataset.
Keep extraction warnings and the retrieval timestamp with downstream output. When a required field is missing, leave it unresolved or return to the source; do not fill it from an assumption.