Datasets:

Octoparse
/

temu-ecommerce-pricing-workflow-sample

license: cc-by-nc-4.0
language:
  - en
task_categories:
  - tabular-classification
  - text-classification
tags:
  - b2b-data
  - enterprise-data
  - web-scraping
  - octoparse-data-service
  - production-ready
  - ecommerce
  - competitor-price-monitoring
  - pricing-intelligence
  - data-engineering
pretty_name: Temu E-commerce Pricing and SKU Variant Dataset
configs:
  - config_name: products
    data_files:
      - split: train
        path: products.parquet
  - config_name: skus
    data_files:
      - split: train
        path: skus.parquet

Temu E-commerce Pricing & SKU Variant Dataset

85 products · 382 SKUs · $2.14–$219.72 price range · avg 29.5% discount where measurable

A real, production-quality sample of Temu product listings and SKU-level pricing data, captured by Octoparse Managed Data Service via a managed anti-bot pipeline. Every row is real market data — no synthetic expansion, no mock prices.

Built for teams working on competitor price monitoring, dynamic pricing models, product matching, and e-commerce AI pipelines.

Enterprise Data Pipelines & Production-Grade Delivery

This dataset is a curated, static sample provided by Octoparse Managed Data Service. If your organization requires:

Real-time automated updates: Daily/Hourly feeds via API, Snowflake, AWS S3, or BigQuery

Custom schema alignment and production-grade anti-bot operations with 99.9% SLA-backed delivery

Compliance-aware data delivery workflows for public or properly authorized data, with support for GDPR/CCPA-aligned requirements

Request a Custom Data Pipeline Workshop on Octoparse Data Service

v2.0.0 — Complete rebuild. Previous version contained synthetic data; this release contains 100% real scraped data normalized into a clean two-table schema.

Why This Dataset

Most public e-commerce datasets are:

Stale — from Kaggle uploads years old, prices meaningless
Synthetic — generated to look like pricing data, not real market signals
Flat and noisy — video metadata and SKU variants collapsed into one bloated table

This dataset is different:

100% real market data from Temu's live catalogue
Normalized schema: products.parquet (SPU-level) + skus.parquet (variant-level), joined on spu_id
Anti-bot provenance: captured through Akamai Bot Manager bypass and TLS fingerprint randomization — the hard infrastructure problem solved for you
Pricing signal transparency: discount_pct computed where Temu surfaces both prices; sparsity documented, not hidden

Dataset Structure

temu-ecommerce-pricing-workflow-sample/
├── products.parquet        ← 85 rows, 5 columns (SPU-level)
├── skus.parquet            ← 382 rows, 14 columns (SKU-level)
├── schema.json             ← Machine-readable field definitions
├── data_dictionary.md      ← Business context for every field
├── workflow_stats.json     ← Pipeline provenance and anti-bot metadata
└── LICENSE                 ← CC BY-NC 4.0

`products.parquet` — SPU Level

Field	Type	Description
`spu_id`	string	Product ID (join key)
`product_url`	string	Temu product URL
`is_on_sale`	boolean	Currently purchasable
`not_on_sale_reason`	string	Reason if inactive (null for all active)
`goods_property_summary`	string	Semicolon-delimited property bag

`skus.parquet` — SKU Level

Field	Type	Description
`sku_id`	string	Variant ID
`spu_id`	string	FK → `products.spu_id`
`sku_main_pic`	string	CDN image URL
`sku_original_price`	float64	List price USD (null if Temu not showing strikethrough)
`sku_discounted_price`	float64	Current selling price USD
`discount_pct`	float64	`(original - discounted) / original`
`sale_attr_color`	string	Color variant
`sale_attr_size`	string	Size variant
`sale_attr_quantity`	string	Bundle/quantity variant
`sale_property_summary`	string	Pipe-delimited summary of all variant attrs

Quick Start

import pandas as pd

products = pd.read_parquet("hf://datasets/Octoparse/temu-ecommerce-pricing-workflow-sample/products.parquet")
skus     = pd.read_parquet("hf://datasets/Octoparse/temu-ecommerce-pricing-workflow-sample/skus.parquet")

# Join: full product + SKU view
df = skus.merge(products[["spu_id", "product_url", "goods_property_summary"]], on="spu_id")

# Products with highest discount (where data available)
top_discounts = (
    skus[skus["discount_pct"].notna()]
    .groupby("spu_id")["discount_pct"]
    .max()
    .sort_values(ascending=False)
    .head(10)
)

# Price spread per product (variant pricing range)
price_spread = (
    skus.groupby("spu_id")["sku_discounted_price"]
    .agg(["min", "max"])
    .assign(spread=lambda x: x["max"] - x["min"])
    .sort_values("spread", ascending=False)
)

# SKUs with strikethrough pricing (visible discount signal)
has_strikethrough = skus[skus["sku_original_price"].notna()].copy()
print(f"{len(has_strikethrough)} SKUs with measurable discount — avg {has_strikethrough['discount_pct'].mean():.1%}")

Dataset Stats

Metric	Value
Products (SPUs)	85
SKUs (variants)	382
Avg SKUs per product	4.5
SKUs with strikethrough price	120 (31.4%)
Avg discount (where measurable)	29.5%
Max discount	81.9%
Price range	$2.14 – $219.72
All products on sale	Yes (100%)

The Hard Problem: Why Temu Data is Difficult to Scrape

Temu runs one of the most aggressive anti-bot stacks in consumer e-commerce:

Akamai Bot Manager with TLS/JA3 fingerprinting — blocks standard HTTP clients instantly
Device fingerprinting — canvas, WebGL, and font enumeration checks
Dynamic price rendering — prices are injected via JavaScript after page load; HTML-only scrapers return empty price fields
Signed CDN URLs — image URLs rotate signatures; naive URL caching breaks within hours
Algorithmic discount visibility — Temu controls when strikethrough prices appear; the same SKU may show or hide the original price depending on session context, geo, and time

This dataset was captured through Octoparse's managed pipeline that handles all of the above. The sku_original_price sparsity you see (69% null) is a real market signal, not a data quality failure — it accurately reflects which SKUs Temu was displaying comparison pricing for at scrape time.

Pricing Signal Notes

Why is sku_original_price 69% null?

Temu uses algorithmic price display — the strikethrough "was" price is only rendered when Temu's system determines it increases conversion for that session. This means:

Null sku_original_price ≠ no discount
Null sku_original_price = Temu chose not to show the comparison at this moment
For true discount rate measurement, you need repeated captures at different times and sessions

For production-cadence pricing (hourly snapshots, session rotation), see our managed pipeline service.

Use Cases

1. Competitor Price Monitoring Pipeline Design

Use sku_discounted_price + discount_pct as features in a price alert system. The sale_attr_* columns let you track price moves at variant granularity (not just product level).

2. Product Matching & Deduplication

spu_id + sku_id + sku_main_pic give you the triplet needed for visual and text-based product matching across platforms.

3. Dynamic Pricing Model Training

The discount_pct distribution (mean 29.5%, max 81.9%) and the presence/absence of sku_original_price are themselves signals for modeling Temu's promotional logic.

4. E-commerce Schema Design

Use goods_property_summary to build a product taxonomy. The ~178 unique property keys across 85 products show the schema complexity of a live multi-category catalogue.

5. Anti-Bot Research

workflow_stats.json documents the specific bot-management systems encountered during collection, useful for anti-scraping research and pipeline architecture planning.

Data Limitations

Limitation	Detail
Price is a snapshot	No time-series in this sample. Temu prices change multiple times daily.
`sku_original_price` sparsity	69% null — intentional (see Pricing Signal Notes above)
`sale_attr_*` sparsity	Each SKU uses at most 1–2 of 7 attribute columns
Property key inconsistency	`goods_property_summary` has ~178 keys with typos/case variations
Sample size	85 products — sufficient for schema/pipeline design, not for statistical price benchmarking

Related Resources

Service page: Competitor Price Monitoring Service
Case study: Temu Pricing Data Pipeline Case Study
Data Service hub + sample library: Octoparse Data Service
Paired Kaggle dataset: temu-ecommerce-pricing-workflow-sample on Kaggle

Want Production-Scale Temu Data?

This is a sample dataset. If you need:

Hourly price capture across thousands of SKUs
Multi-category coverage (apparel, electronics, home, beauty)
Session-rotated scraping to expose hidden discount pricing
Delivery to Snowflake, BigQuery, or S3 in Parquet or JSONL

→ Talk to Octoparse Managed Data Service

We operate the anti-bot infrastructure so your team doesn't have to.

Citation

@dataset{octoparse_temu_pricing_2025,
  title        = {Temu E-commerce Pricing and SKU Variant Dataset},
  author       = {{Octoparse Managed Data Service}},
  year         = {2025},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/Octoparse/temu-ecommerce-pricing-workflow-sample},
  license      = {CC BY-NC 4.0},
  note         = {Real scraped Temu product and SKU pricing data, anti-bot pipeline provenance documented}
}

Built by Octoparse Managed Data Service · LinkedIn