1688 Supplier & Product Analyzer

Analyze data from any 1688 scraper, product export, or private Apify Dataset. Turn raw 1688 product and supplier records into evidence-linked supplier diligence, MOQ cash exposure, fulfillment proxies, supply-trend evidence, cross-border unit economics, and a ranked sourcing shortlist.

Unofficial tool. This Actor is not affiliated with, sponsored by, or endorsed by Alibaba, 1688, or any source or destination marketplace.

Quick start

• No data yet: select demo to see a synthetic report without a paid report event.
• Have a scraper Dataset: paste its ID into sourceDatasetIds.
• Have JSON or spreadsheet exports: paste normalized records into uploadedRecords.

No 1688 account, password, session, or Cookie is requested by this analyzer.

Ready-made public examples

• Audit a 1688 Supplier Before Ordering compares supplier evidence, fulfillment proxies, red flags, and verification actions.
• Calculate Amazon FBA Margin from 1688 Products models MOQ cash exposure, landed cost, fees, advertising, returns, and baseline margin.
• Rank 1688 Products for Sourcing ranks products using demand proxies, supplier evidence, MOQ fit, modeled margins, and repeated-snapshot trend evidence.

All three examples use synthetic records. Duplicate an example and replace the sample input with data you are authorized to use.

Why this is different

Most 1688 scraper Actors sell collection. This 1688 supplier analyzer sells the decision layer after collection:

• compare suppliers without presenting a platform badge as proof of legitimacy;
• expose missing identity, repeat-order, delivery, certification, and factory evidence;
• apply MOQ and quantity pricing instead of assuming the lowest displayed price;
• model first-order cash exposure and landed margin under conservative, baseline, and optimistic assumptions;
• require repeated dated snapshots before calling a product a trend; and
• rank product candidates with source evidence and concrete next actions.

It does not claim that 1688 is freely scrapeable and it does not request or publish a 1688 Cookie.

1688 scraper data in, sourcing decisions out

Use the output of an existing 1688 product scraper instead of rebuilding your collection workflow. The analyzer recognizes common product, price-tier, MOQ, supplier, order, review, logistics, stock, certification, and snapshot fields. It then produces a comparable report rather than another raw product list.

Private-data-safe delivery

Choose either:

1. paste authorized JSON into uploadedRecords; or
2. provide sourceDatasetIds from collection tasks in your own Apify account.

Only explicitly supplied datasets are read. A customer who already uses a 1688 scraper can pass its Dataset ID directly into this Actor. Private collection, field mapping, scheduled maintenance, and an SLA can be scoped separately without putting credentials in this public Actor.

Reports and pricing

Report	Output	Price
Supplier diligence	Confidence score, red flags, fulfillment proxies, evidence, verification checklist	$1.99
MOQ & landed cost	MOQ-adjusted quantity tier, inventory cash, three cost/margin scenarios	$1.99
Product selection	Ranked shortlist, demand proxies, trend evidence, supplier and margin fit	$2.49
Full sourcing intelligence	Every module in one report	$4.99

The synthetic demo does not trigger a paid report event. Platform usage is included in paid report prices.

Modes

• demo: run a safe synthetic example without uploading data
• supplierDiligence: supplier evidence, red flags, fulfillment proxies, and verification actions
• landedCost: MOQ cash exposure and three-scenario unit economics
• productSelection: sourcing shortlist and supply-side trend evidence
• fullReport: all modules

Example using an upstream private Dataset

{
  "mode": "fullReport",
  "sourceDatasetIds": ["YOUR_PRIVATE_1688_DATASET_ID"],
  "targetMarketplace": "amazonFba",
  "destinationCountry": "United States",
  "destinationCurrency": "USD",
  "exchangeRateCnyToTarget": 0.14,
  "purchaseQuantity": 200,
  "targetSalePrice": 29.99,
  "targetMarginRate": 30,
  "inventoryBudget": 5000,
  "internationalFreightPerKg": 4,
  "dutyRate": 5,
  "taxRate": 0,
  "platformFeeRate": 15,
  "paymentFeeRate": 3,
  "adCostRate": 12,
  "returnRate": 5,
  "returnLossPerReturnedUnit": 8,
  "fulfillmentFeePerUnit": 4
}

Accepted records

The normalizer recognizes common flat and nested aliases for:

• product identity: offerId, productId, url, title
• pricing: price, priceCny, quantityPrices, priceTiers
• order requirements: moq, minOrder, minimumOrderQuantity
• supplier identity: supplierName, companyName, supplierId, location, business type, platform years, badges, and verification tags
• operating evidence: logistics/service scores, lead time, on-time rate, stock, service protections, return policy, factory area, and staff count
• transaction proxies: monthly sales, orders, sold count, reviews, positive rate, and repurchase rate
• time series: observedAt, scrapedAt, collectedAt, or timestamp
• product weight in kg, g, or lb

Fields can be flat or nested under common product, pricing, metrics, supplier, seller, shop, company, or factory objects.

What the scores mean

The supplier score is a transparent relative evidence score. It combines identity, transaction, operating, buyer-protection, and data-completeness signals from the supplied records. It is not legal identity verification, a factory audit, a credit check, or a performance guarantee.

The product shortlist combines:

• supply-side demand proxies;
• supplier evidence;
• configured unit economics;
• MOQ and inventory-budget fit;
• data completeness; and
• time-series evidence.

A single listing snapshot is never labeled as trend proof. At least two dated snapshots of the same offer are required.

Financial and sourcing boundaries

The output is a screening model, not tax, customs, legal, compliance, investment, or procurement advice. Before ordering:

• verify the legal contracting entity and bank details independently;
• order and inspect representative samples;
• obtain a written quote, specification, production capacity, lead-time, quality control, packaging, remedy, and payment terms;
• verify certificates with the issuer and confirm the exact covered model;
• confirm destination-market safety, labeling, intellectual-property, customs classification, duty, tax, and marketplace requirements; and
• replace estimated weight, freight, advertising, returns, fees, and exchange rates with current quotes and measured data.

Only process data you are authorized to use and follow platform terms, privacy, intellectual-property, trade, and data-protection rules.

Local validation

$python3 -m unittest discover -s tests -v