Deployed on AWS
This dataset is a sample of 1000 US based eCommerce companies that fall under the umbrella of Fashion & Apparel – companies that sell clothing, cosmetics & beauty supplies, accessories, jewelry, shoes, watches, etc. You could get the entire universe of all eCommerce and D2C brands segregated on an extensive list of attributes including product type (physical or digital goods), presence of (or lack of) physical stores, their business model (B2B, B2C, or both), SKU type and more. Just ask us!
Overview
About Us:
PipeCandy is the industry standard data set for actionable insights on eCommerce and D2C brands. Our proprietary data includes 50+ attributes on over 1M+ eCommerce/D2C brands and is ever expanding. As a power-packed combination of machine learning algorithms and meticulous research, our data is the secret weapon in the arsenal of successful demand generation and analytics teams across business verticals.
Note: This dataset is a sample of 1000 US based eCommerce companies that fall under the umbrella of Fashion & Apparel – companies that sell clothing, cosmetics & beauty supplies, accessories, jewelry, shoes, watches, etc. You could get the entire universe of all eCommerce and D2C brands segregated on an extensive list of attributes including product type (physical or digital goods), presence of (or lack of) physical stores, their business model (B2B, B2C, or both), SKU type and more. Just ask us!
Key Data Attributes:
Traffic, Web Sales, Order Volume, Technologies used, Payment & Lending Partnerships, Logistics Partnerships, Conveniences they offer to shoppers (Free shipping, returns, delivery speed etc.), SKU type (Physical Goods vs. Digital Goods), Business Model (B2B vs. B2C), Revenue Model (Subscription vs. One-off), Channel Presence, Brand/Merchandise Strategy, etc.
Data Dimensions:
*Websales - Available as a range for last 12 months *Order volume - Available as a range for last 12 months *Refresh Frequency - Every month
An indicative set of companies included in this sample dataset:
Anthropologie, AMERICAN EAGLE, ASICS, allbirds, Glossier, David’s Bridal, Jimmy Jazz, BHLDN, AMORE PACIFIC, Kay, Zales, Yandy, Armani Exchange, HARRY'S, Aesop, Sally Beauty
Data Access:
You can access PipeCandy data :
Amazon ADX:
This is the most popular way of consuming our data for AWS users. We will share access to all of our data with you via Amazon Data Exchange.
Pricing Options:
Datasets via Amazon ADX: Datasets are priced based on the type of dataset. This dataset is a sample and hence is offered at zero cost. Our typical dataset pricing starts at $25000 per year.
Prospector: Pricing varies from $999 per year to $24,999 per year. For details visit https://pipecandy.com/pricing
Firehose: Pricing starts at $50,000
Sample Data & Getting Started:
Interested in learning more about our data sets? We are happy to set up a call to walk you through everything here as well as put together some relevant sample data for your review.
Details
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Delivery method
Deployed on AWS
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Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimensions summary
This is a free sample listing with a single pricing dimension: Product Access (Units). You subscribe at no cost to gain access to the product. There are no tiers, usage add-ons, or scaling charges. The sample gives you a look at US fashion and apparel domains and their attributes, such as eCommerce insights on clothing and fashion companies. Because pricing is free, there is nothing that scales by volume, users, or time. Access is granted through the single subscription dimension.
Top-of-mind questions for buyers
A unit grants you access to this sample listing. It is not a per-record or per-lead charge. The single unit unlocks the sample dataset of US fashion and apparel domains with their attributes. There is no separate counting of domains, users, or exports within this free access.
The sample gives you US fashion and apparel domains along with attributes such as eCommerce insights on clothing and fashion companies. It is a limited look at the dataset. It does not include the full company list, custom attributes, delivery frequency choices, or expanded market coverage described on the seller site.
No. Access is free through the single Product Access dimension. Nothing scales by volume, users, records, or time. Viewing more domains or attributes within the sample does not add charges. There are no tiers, overage fees, or usage add-ons in this listing.
pipecandy.com
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Vendor refund policy
PipeCandy offers sample data so that you are familiar and comfortable with the dataset. Once datasets are purchased, we do not offer refunds.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
US Fashion & Apparel Domains | All historical revisions | All future revisions | Not included | Not included |
Resources
Vendor resources
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This dataset is a sample of 1000 US based eCommerce companies that fall under the umbrella of Fashion & Apparel – companies that sell clothing, cosmetics & beauty supplies, accessories, jewelry, shoes, watches, etc. You could get the entire universe of all eCommerce and D2C brands segregated on an extensive list of attributes including product type (physical or digital goods), presence of (or lack of) physical stores, their business model (B2B, B2C, or both), SKU type and more. Just ask us!
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