Shopee Seller Price & Stock Shift Intelligence

Use this Actor to analyze shopee seller price and stock shift and return decision-ready structured signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Turn Shopee product and seller snapshots into price, discount, stock, sell-through and competitive-position change intelligence across monitored markets.

Why use this Actor

Shopee teams can extract product rows easily, but recurring decisions require knowing which sellers cut price, lose stock, accelerate sales, or gain competitive position between snapshots. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Current-versus-previous snapshot comparison with deterministic deltas.

• Confidence-scored GO/WARN/BLOCK decision contract.

• Agent-ready recommended actions instead of raw rows.

• Dataset-ID inputs for recurring Apify workflows.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• Shopee competitor price and promotion monitoring.
• Stockout and sell-through signal detection.
• Seller assortment and market expansion analysis.
• AI-agent ecommerce monitoring workflows.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.004 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Output

The Actor writes one decision-ready record to the default Apify Dataset. The record includes marketSignalScore, executiveDecision, decisionConfidenceScore, recommendedAction, baseline availability, change counts, ranked topSignals, and optional economic-impact fields when the user supplies valuation inputs. This stable Gen2/Gen4 contract is designed for dashboards, automations and AI-agent workflows.