---
title: "Prepare inputs for a pricing decision"
url: https://www.datawebot.com/blog/dynamic-pricing-strategies-amazon-walmart-alibaba
description: "Separate source prices from your pricing rules. Amazon, Walmart and Alibaba are source references, not a promise of immediate execution. datawebot does not update prices or supply an automatic repricing engine."
updated: 2026-09-23
author: datawebot team
---

Extraction guide

# Prepare inputs for a pricing decision

Separate source prices from your pricing rules.

Updated September 23, 2026 · datawebot team

[← All guides](https://www.datawebot.com/blog)Field map ↓Try the workflow ↗

01 / INPUT**Source offer + internal cost and policy**

02 / YOUR WORKFLOW**Match → evaluate → review**

03 / OUTPUT**Proposed price with supporting inputs**

## 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.

| 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. |

[Amazon source scope ↗](https://www.datawebot.com/scraper/amazon) · [Walmart source scope ↗](https://www.datawebot.com/scraper/walmart) · [Alibaba.com source scope ↗](https://www.datawebot.com/scraper)

## 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.

| 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. |

[Preserve pricing evidence ↗](https://www.datawebot.com/blog/build-product-price-history-database)

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.

Working references [Product and variant schema ↗](https://www.datawebot.com/docs/data) · [Supported endpoints ↗](https://www.datawebot.com/scraper) · [Incomplete or unavailable results ↗](https://www.datawebot.com/docs/troubleshooting)

[Continue this workflow](https://www.datawebot.com/solutions/dynamic-pricing-optimization)