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 in three different ways:
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.
PipeCandy Prospector:
A web application you can login to, slice and dice the data based on attributes that matter to you. Search based on attributes or company names, build lists, download the list as a .CSV file or export to Hubspot/Salesforce or any CRM of your choice.
PipeCandy Firehose:
Our industry standard API or flat-files with blocks of attributes that you can pick and choose from. Build a set of net-new companies or bring your own data and enrich with attributes offered by PipeCandy.
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.
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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 listing has a single pricing dimension. You get Product Access measured in Units at no cost. This is a free sample of US fashion and apparel domain data with attributes. Because it is offered at no charge, there are no tiers, usage add-ons, or scaling options to weigh. Access grants you the sample dataset covering fashion and apparel eCommerce domains and their associated attributes. There is one option, so pricing does not vary by quantity, term, or feature set.
Top-of-mind questions for buyers
One unit grants you access to the free sample dataset of US fashion and apparel eCommerce domains and their attributes. It is a limited preview, not the full catalog of hundreds of thousands of eCommerce companies. Access is granted per subscriber at no charge.
Each domain can include attributes such as web sales estimates, shipping volume, revenue range, web traffic, and technology usage. The sample covers fashion and apparel eCommerce companies in the US. It shows the format and fields you would receive, drawn from a larger dataset.
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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.
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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 | No 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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