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Envestnet | Yodlee's De-Identified Ecommerce Purchases Data | Row/A...
Envestnet | Yodlee's De-Identified Ecommerce Purchases Data | Row/Aggregate L...
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Envestnet | Yodlee's De-Identified Ecommerce Purchases Data | Row/Aggregate Level | USA Consumer Data covering 3600+ corporations | 90M+ Accounts product image in hero

Envestnet | Yodlee's De-Identified Ecommerce Purchases Data | Row/Aggregate Level | USA Consumer Data covering 3600+ corporations | 90M+ Accounts

Envestnet | Yodlee
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Data Samples
Sample Consumer Transactions.csv Agg Product Sample.csv
#
unique_mem_id
unique_bank_account_id
unique_bank_transaction_id
amount
currency
description
transaction_date
post_date
transaction_base_type
transaction_category_name
primary_merchant_name
secondary_merchant_name
city
state
zip_code
transaction_origin
factual_category
factual_id
file_created_date
optimized_transaction_date
yodlee_transaction_status
mcc_raw
mcc_inferred
swipe_date
panel_file_created_date
update_type
is_outlier
change_source
account_type
account_source_type
account_score
user_score
lag
is_duplicate
1 xxxxxxxxxx Xxxxxxxxx xxxxxx xxxxxxxxxx Xxxxx Xxxxxx Xxxxxxxxxx Xxxxxx Xxxxxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxxxxxx xxxxxxxxx Xxxxxxx xxxxxx Xxxxx xxxxxxxxxx xxxxxx Xxxxxxxxxx xxxxxx Xxxxx Xxxxxx xxxxx xxxxxxxx xxxxxxx Xxxxx Xxxxxxxx xxxxxxxxxx xxxxxx Xxxxxxxxx xxxxxx Xxxxxxxxx Xxxxxxxxx xxxxxxxxxx
2 Xxxxxx Xxxxx xxxxxx xxxxxxx xxxxxxx Xxxxx xxxxxx Xxxxxxxxxx xxxxxxxx xxxxxx Xxxxx Xxxxxxx xxxxxx Xxxxxxxx Xxxxxxx Xxxxx xxxxxx xxxxxxxxxx Xxxxx xxxxxxxxxx xxxxxxxxx Xxxxxxx xxxxxxxx xxxxxxxx Xxxxxxxxxx Xxxxxxxx Xxxxxxxx xxxxxxxxx Xxxxxxxxxx Xxxxxx Xxxxxxxxx xxxxx xxxxxxx xxxxxxxxx
3 Xxxxxx Xxxxxxx Xxxxxxxxx xxxxxxxxx xxxxxxxxx Xxxxx xxxxxxxx Xxxxxxx xxxxxxxxx Xxxxxxx xxxxx Xxxxxxx xxxxxxx Xxxxx xxxxxxxxxx Xxxxxxx Xxxxx xxxxxxxxxx Xxxxxx xxxxxx Xxxxxxxxx xxxxx Xxxxxxxxxx xxxxxx xxxxx xxxxxxxx Xxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxxxxxxx xxxxxxxx xxxxx Xxxxxx xxxxxxxxxx
4 xxxxxxxxx xxxxx xxxxx xxxxxxxx xxxxxx Xxxxxxxxxx xxxxxxxxxx Xxxxx xxxxxxx Xxxxxxxx Xxxxxxx xxxxx xxxxxxxx xxxxxxxxxx Xxxxxx xxxxxxxxx Xxxxx xxxxx xxxxxxxxx xxxxxxx Xxxxxxxxx Xxxxxxx xxxxxxxxxx Xxxxx xxxxxxxxx xxxxxxx Xxxxxx xxxxxxxxx xxxxx Xxxxxxx xxxxxxxxx Xxxxxxxx xxxxxxxx Xxxxxxxx
5 Xxxxxxxx xxxxxxxx xxxxxxxxx Xxxxxxx Xxxxxxxxx xxxxxxxx xxxxx Xxxxxxxxxx xxxxxxxxxx xxxxxx Xxxxx Xxxxxxx Xxxxx Xxxxxx Xxxxx Xxxxxxxxx xxxxxx xxxxxxxx Xxxxxxxxx Xxxxxx Xxxxxxxxxx Xxxxxx Xxxxx Xxxxxxx xxxxxxxxx Xxxxx xxxxx Xxxxxx xxxxxxxxx xxxxxxx xxxxxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxx
6 Xxxxx Xxxxxxxxx xxxxxxxxxx xxxxxx xxxxxxxxx xxxxxxx Xxxxxxx Xxxxxxxxxx Xxxxxxxxxx Xxxxxxxx Xxxxxxxxx xxxxx Xxxxxxx xxxxxxxxxx Xxxxxxxxx Xxxxxxxx xxxxxxxxxx xxxxxxx Xxxxxxxx xxxxx Xxxxxx xxxxxx xxxxxxxx xxxxxxx Xxxxx Xxxxxxxxx Xxxxx Xxxxxxx Xxxxxxxx xxxxxxxxx xxxxxxxx xxxxx Xxxxxxxxxx Xxxxxxx
7 xxxxxxxxx xxxxxxx xxxxxxxxxx xxxxxx xxxxx Xxxxxxxxxx Xxxxxxxxx xxxxxxx Xxxxxx Xxxxx Xxxxxxxx xxxxxxxxx xxxxxxxx Xxxxxx xxxxxxxxxx xxxxxxxxx xxxxx Xxxxx xxxxxxx xxxxxxxxxx Xxxxxx Xxxxxxxxx xxxxxxx Xxxxxxxx xxxxx xxxxx Xxxxxxxxxx Xxxxxxx Xxxxxxxx Xxxxxxx xxxxx xxxxxxx Xxxxx xxxxxxxxxx
8 Xxxxxxxxxx xxxxxxx Xxxxx xxxxxxxxx xxxxxxxx Xxxxxxxx xxxxxxxx Xxxxxxx Xxxxxx Xxxxxxxxx Xxxxxxxx Xxxxxxxxxx Xxxxxxx Xxxxxx Xxxxxxxxxx xxxxxxxxxx xxxxxxxxxx Xxxxxxx Xxxxx Xxxxx Xxxxx Xxxxxxx xxxxx xxxxxxxxx xxxxxxx Xxxxxxx xxxxxx xxxxxxxxxx xxxxxxxxxx Xxxxxxx xxxxxxxxx Xxxxx xxxxxxx Xxxxxx
9 Xxxxx xxxxxxxxxx xxxxxxxxx Xxxxxxxxxx Xxxxxxxxx Xxxxxxxx xxxxxxxxx Xxxxxxx Xxxxxxx Xxxxx xxxxxxxxxx Xxxxxxxxx Xxxxxxx Xxxxxxx xxxxxxxx xxxxx Xxxxx Xxxxxxxx xxxxxxxx Xxxxxxxxx xxxxxxxxxx xxxxxxxxxx xxxxxxxxx xxxxxxxxx Xxxxxxx Xxxxxxx Xxxxxxx Xxxxxxx xxxxxxx Xxxxxxxxxx xxxxxxxx Xxxxx xxxxxxxxxx xxxxxxxxxx
10 xxxxxx xxxxxxxx Xxxxxxxx xxxxxx xxxxxxxx xxxxxx Xxxxxxxx xxxxxxxxx xxxxx Xxxxxxxxxx Xxxxxxxxx Xxxxxxx xxxxxxxx Xxxxxxx Xxxxxxxxxx Xxxxxxxxx xxxxxxxxxx xxxxxxx Xxxxxxxxx xxxxxxxxx xxxxxxxx xxxxxxxxx xxxxx Xxxxx Xxxxxxxx xxxxxxxxxx Xxxxxx Xxxxxxxxx Xxxxxxxxxx xxxxxxx Xxxxxxxxxx Xxxxxxxxx Xxxxxx xxxxxxxx
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Sign In To Preview Data
#
ticker
brand
quarter
start date
end date
users
txns
spends
1 xxxxxxxxxx Xxxxxxxxx xxxxxx xxxxxxxxxx Xxxxx Xxxxxx Xxxxxxxxxx Xxxxxx
2 Xxxxxxxxx Xxxxxxxxxx xxxxxxxxx Xxxxxxxxx xxxxxxxxx Xxxxxxx xxxxxx Xxxxx
3 xxxxxxxxxx xxxxxx Xxxxxxxxxx xxxxxx Xxxxx Xxxxxx xxxxx xxxxxxxx
4 xxxxxxx Xxxxx Xxxxxxxx xxxxxxxxxx xxxxxx Xxxxxxxxx xxxxxx Xxxxxxxxx
5 Xxxxxxxxx xxxxxxxxxx Xxxxxx Xxxxx xxxxxx xxxxxxx xxxxxxx Xxxxx
6 xxxxxx Xxxxxxxxxx xxxxxxxx xxxxxx Xxxxx Xxxxxxx xxxxxx Xxxxxxxx
7 Xxxxxxx Xxxxx xxxxxx xxxxxxxxxx Xxxxx xxxxxxxxxx xxxxxxxxx Xxxxxxx
8 xxxxxxxx xxxxxxxx Xxxxxxxxxx Xxxxxxxx Xxxxxxxx xxxxxxxxx Xxxxxxxxxx Xxxxxx
9 Xxxxxxxxx xxxxx xxxxxxx xxxxxxxxx Xxxxxx Xxxxxxx Xxxxxxxxx xxxxxxxxx
10 xxxxxxxxx Xxxxx xxxxxxxx Xxxxxxx xxxxxxxxx Xxxxxxx xxxxx Xxxxxxx
... xxxxxxx Xxxxx xxxxxxxxxx Xxxxxxx Xxxxx xxxxxxxxxx Xxxxxx xxxxxx
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Sample Consumer Transactions.csv Agg Product Sample.csv
Volume
3K
+ merchants
Data Quality
99%
% high precision tagging, 600 tickers
Avail. Formats
.sql and .txt
File
Coverage
1
Country
History
9
years

Data Dictionary

[Sample] Sample Consumer Transactions.csv
Attribute Type Example Mapping
unique_mem_id
 Integer 794996014209149592499260
unique_bank_account_id
 Integer 206568081043093301913884
unique_bank_transaction_id
 Integer 13261778880509950775504945222
amount
 Float 6.82
currency
 String USD
description
 String HOBBYLOBBY 4141 MARTIN WAOLYMPIA WA~~XXXXX~~XXXXXX**...
transaction_date
 String 10/2/2019
post_date
 String 10/2/2019
transaction_base_type
 String debit
transaction_category_name
 String Entertainment/Recreation
primary_merchant_name
 String Hobby Lobby
secondary_merchant_name
city
state
zip_code
transaction_origin
 String Physical
factual_category
 String Businesses and Services,Home Improvement,Interior Design
factual_id
file_created_date
 String 10/3/2019
optimized_transaction_date
 String 10/2/2019
yodlee_transaction_status
 Boolean f
mcc_raw
 Integer 59450
mcc_inferred
 Integer 5945
swipe_date
panel_file_created_date
 String 12/15/2019
update_type
is_outlier
change_source
account_type
 Integer 1
account_source_type
 Integer 1
account_score
 Float 651.650587
user_score
 Float 46.270715
lag
 Integer 1
is_duplicate
 Integer 0
[Sample] Agg Product Sample.csv
Attribute Type Example Mapping
ticker
 String DIS
brand
 String The Walt Disney Company
quarter
 String 2023 Q1
start date
 String 10/1/2022
end date
 String 12/31/2022
users
 String 25,388
txns
 String 62,403
spends
 String 4,666,652
Product Attributes
Attribute Type Example Mapping
Ticker
 APL
Brand
 Apple
Quarter
 2023 Q1
Start Date
 10/1/22
End Date
 12/31/2022
users
 22,230
txns
 62,413
Spends
 4,66,523

Description

Envestnet®| Yodlee®'s Ecommerce Purchases Panels (Aggregate/Row) consist of de-identified U.S. consumer credit/debit/ACH transaction level data, offering a wide view of the U.S. consumer ecosystem in near real-time (T+1).
Envestnet®| Yodlee®'s Ecommerce Purchases Data (Aggregate/Row) Panels consist of de-identified, near-real time (T+1) USA credit/debit/ACH transaction level data – offering a wide view of the consumer activity ecosystem. The underlying data is sourced from end users leveraging the aggregation portion of the Envestnet®| Yodlee®'s financial technology platform. Envestnet | Yodlee Consumer Panels (Aggregate/Row) include data relating to millions of transactions, including ticket size and merchant location. The dataset includes de-identified credit/debit card and bank transactions (such as a payroll deposit, account transfer, or mortgage payment). Our coverage offers insights into areas such as consumer, TMT, energy, REITs, internet, utilities, ecommerce, MBS, CMBS, equities, credit, commodities, FX, and corporate activity. We apply rigorous data science practices to deliver key KPIs daily that are focused, relevant, and ready to put into production. We offer free trials. Our team is available to provide support for loading, validation, sample scripts, or other services you may need to generate insights from our data. Investors, corporate researchers, and corporates can use our data to answer some key business questions such as: - How much are consumers spending with specific merchants/brands and how is that changing over time? - Is the share of consumer spend at a specific merchant increasing or decreasing? - How are consumers reacting to new products or services launched by merchants? - For loyal customers, how is the share of spend changing over time? - What is the company’s market share in a region for similar customers? - Is the company’s loyal user base increasing or decreasing? - Is the lifetime customer value increasing or decreasing? Additional Use Cases: - Use spending data to analyze sales/revenue broadly (sector-wide) or granular (company-specific). Historically, our tracked consumer spend has correlated above 85% with company-reported data from thousands of firms. Users can sort and filter by many metrics and KPIs, such as sales and transaction growth rates and online or offline transactions, as well as view customer behavior within a geographic market at a state or city level. - Reveal cohort consumer behavior to decipher long-term behavioral consumer spending shifts. Measure market share, wallet share, loyalty, consumer lifetime value, retention, demographics, and more.) - Study the effects of inflation rates via such metrics as increased total spend, ticket size, and number of transactions. - Seek out alpha-generating signals or manage your business strategically with essential, aggregated transaction and spending data analytics. Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking 2. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC) 3. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence 4. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Country Coverage

North America (1)
United States of America

History

9 years of historical data

Volume

9 + Years history
3,000 + merchants
600 + tickers
23 + million users
48 + million + accounts

Pricing

Free sample available
Envestnet | Yodlee has not published pricing information for this product yet. You can request detailed pricing information below.
Request detailed pricing

Suitable Company Sizes

Small Business
Medium-sized Business
Enterprise

Quality

Self-reported by the provider
99%
% high precision tagging, 600 tickers

Delivery

Methods
S3 Bucket
Frequency
daily
weekly
monthly
Format
.sql
.txt

Use Cases

Alpha Generation
Credit Card Analytics
Consumer Trend Analysis
Consumer Profiling Revenue Forecasting

Categories

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Frequently asked questions

What is Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts?

Envestnet® Yodlee®’s Ecommerce Purchases Panels (Aggregate/Row) consist of de-identified U.S. consumer credit/debit/ACH transaction level data, offering a wide view of the U.S. consumer ecosystem in near real-time (T+1).

What is Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts used for?

This product has 5 key use cases. Envestnet Yodlee recommends using the data for Alpha Generation, Credit Card Analytics, Consumer Trend Analysis, Consumer Profiling, and Revenue Forecasting. Global businesses and organizations buy Credit Card Data from Envestnet Yodlee to fuel their analytics and enrichment.

Who can use Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts?

This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Credit Card Data. Get in touch with Envestnet Yodlee to see what their data can do for your business and find out which integrations they provide.

How far back does the data in Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts go?

This product has 9 years of historical coverage. It can be delivered on a daily, weekly, and monthly basis.

Which countries does Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts cover?

This product includes data covering 1 country like USA. Envestnet Yodlee is headquartered in United States of America.

How much does Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts cost?

Pricing information for Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts is available by getting in contact with Envestnet Yodlee. Connect with Envestnet Yodlee to get a quote and arrange custom pricing models based on your data requirements.

How can I get Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts?

Businesses can buy Credit Card Data from Envestnet Yodlee and get the data via S3 Bucket. Depending on your data requirements and subscription budget, Envestnet Yodlee can deliver this product in .sql and .txt format.

What is the data quality of Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts?

Envestnet Yodlee has reported that this product has the following quality and accuracy assurances: 99% % high precision tagging, 600 tickers. You can compare and assess the data quality of Envestnet Yodlee using Datarade’s data marketplace. Envestnet Yodlee appears on selected Datarade top lists ranking the best data providers, including Best Credit & Debit Card Transaction Data Providers: Q1 2023.

What are similar products to Envestnet Yodlee’s De-Identified Ecommerce Purchases Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts?

This product has 3 related products. These alternatives include Envestnet Yodlee’s De-Identified Spending Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts, Consumer Edge Vision Europe Retail & In-Store Sales Data Austria, France, Germany, Italy, Spain, UK 6.7M Accounts, 5K Merchants, 600 Companies, and Product and Price Data, Product Reviews Data from Google Shopping Ecommerce Data Real-Time API. You can compare the best Credit Card Data providers and products via Datarade’s data marketplace and get the right data for your use case.

Pricing available upon request

Envestnet | Yodlee

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