Apparel & Fashion Product Sales Volume Data
Verified Data Provider| # | product_title |
date |
daily_sales |
units_sold |
current_price |
full_price |
out_of_stock |
price_increase |
price_decrease |
new_product |
on_sale |
|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx |
| 2 | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx |
| 3 | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx |
| 4 | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx |
| 5 | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx |
| 6 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx |
| 7 | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
| 8 | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx |
| 9 | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx |
| 10 | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx |
| ... | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx |
Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
| String | Formatted name of Company | Company Name | |
| String | Product ID (if available) on product page | Product ID | |
|
product_title
|
Product title as displayed on the product page | ||
|
date
|
Date the data was collected | ||
|
daily_sales
|
Product daily sales (units sold x current_price) | ||
|
units_sold
|
Units sold on current date | ||
|
current_price
|
Listed current product price | ||
|
full_price
|
Listed full product price | ||
| String | Product page URL | Product URL | |
|
out_of_stock
|
True if item is not in stock, False if is in stock | ||
|
price_increase
|
True if price is increased on current date, False if not | ||
|
price_decrease
|
True if price decreased on current date, false if not | ||
|
new_product
|
True if product is new, False if not | ||
|
on_sale
|
True if product is discounted, False if not |
Description
Country Coverage
History
Volume
| 30,000 | + Apparel companies tracked |
Pricing
| License | Starts at |
|---|---|
| One-off purchase |
$10,000 / purchase |
| Monthly License | Not available |
| Yearly License |
$50,000 / year |
| Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Apparel & Fashion Product Sales Volume Data?
Particl provides product sales volume data of each SKU from Apparel & Fashion eCommerce sites.
What is Apparel & Fashion Product Sales Volume Data used for?
This product has 5 key use cases. Particl recommends using the data for Market Research, Pricing Optimization, Product Research, Ecommerce Intelligence, and Competitive Intelligence. Global businesses and organizations buy Ecommerce Data from Particl to fuel their analytics and enrichment.
Who can use Apparel & Fashion Product Sales Volume Data?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Ecommerce Data. Get in touch with Particl to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Apparel & Fashion Product Sales Volume Data go?
This product has 2 years of historical coverage. It can be delivered on a daily, weekly, monthly, quarterly, and yearly basis.
Which countries does Apparel & Fashion Product Sales Volume Data cover?
This product includes data covering 4 countries like USA, United Kingdom, Canada, and Australia. Particl is headquartered in United States of America.
How much does Apparel & Fashion Product Sales Volume Data cost?
Pricing for Apparel & Fashion Product Sales Volume Data starts at USD10,000 per purchase. Connect with Particl to get a quote and arrange custom pricing models based on your data requirements.
How can I get Apparel & Fashion Product Sales Volume Data?
Businesses can buy Ecommerce Data from Particl and get the data via S3 Bucket, Email, and UI Export. Depending on your data requirements and subscription budget, Particl can deliver this product in .csv and .xls format.
What is the data quality of Apparel & Fashion Product Sales Volume Data?
Particl has reported that this product has the following quality and accuracy assurances: 100% sku coverage per site. You can compare and assess the data quality of Particl using Datarade’s data marketplace. Particl has received 3 reviews from clients. Particl appears on selected Datarade top lists ranking the best data providers, including Who’s New on Datarade? June Edition.
What are similar products to Apparel & Fashion Product Sales Volume Data?
This product has 3 related products. These alternatives include Walmart Data Ecommerce Data Ecommerce Product Data Walmart API Walmart Product Datasets Easy to Integrate 50% Cost Saving Free Sample, Ecommerce Data & Ecommerce Product Data Global Digital Shelf Data Unlimited Free Data Points Track Competitors & Best Sellers GDPR Compliant, and AutoScraping’s 3M Cannabis Product Data from Global eCommerce Sites (Brands, Categories, Prices, Locations, Real-Time Updates). You can compare the best Ecommerce Data providers and products via Datarade’s data marketplace and get the right data for your use case.
