Envestnet | Yodlee's De-Identified Restaurant and Food Delivery Transaction Data | Row/Aggregate Level | USA Consumer Data covering 3600+ corporations
# | 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations?
Envestnet® Yodlee®’s Restaurant and Food Delivery Transaction Data 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 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 Ecommerce Sales Data from Envestnet Yodlee to fuel their analytics and enrichment.
Who can use Envestnet Yodlee’s De-Identified Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Ecommerce Sales 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations cost?
Pricing information for Envestnet Yodlee’s De-Identified Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations?
Businesses can buy Ecommerce Sales 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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations?
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 Restaurant and Food Delivery Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations?
This product has 3 related products. These alternatives include Envestnet Yodlee’s De-Identified Electronrics Transaction Data Row/Aggregate Level USA Consumer Data covering 3600+ corporations 90M+ Accounts, GrabFood, GrabExpress Restaurant & Food Delivery Transaction Data E-Receipt Data South East Asia Granular & Aggregate Data avail., and PG Consumer Transaction Data 105M Transactions, $742M montly volume Sales Transaction Data perfect for Consumer Trend Analysis. You can compare the best Ecommerce Sales Data providers and products via Datarade’s data marketplace and get the right data for your use case.