Envestnet | Yodlee's De-Identified Ecommerce Purchases Data | Row/Aggregate Level | USA Consumer Data covering 3600+ corporations | 90M+ Accounts
# | 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 |
... | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx |
# | 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 |
Data Dictionary
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 |
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 |
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
Country Coverage
History
Volume
9 | + Years history |
3,000 | + merchants |
600 | + tickers |
23 | + million users |
48 | + million + accounts |
Pricing
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
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, ClearScore Dataset UK Consumer Transaction Data 1.4m users., and Consumer Edge Vision Europe Retail & In-Store Sales Data Austria, France, Germany, Italy, Spain, UK 6.7M Accounts, 5K Merchants, 600 Companies. 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.