
Online Purchase Data | Aggregated Transaction Patterns for In-Person and Online
# | raw_total_spend |
raw_num_transactions |
raw_num_customers |
placekey |
median_spend_per_transaction |
median_spend_per_customer |
spend_per_ transaction_percentiles |
spend_by_day |
spend_per_transaction_by_day |
spend_by_day_of_week |
spend_pct_change_ vs_prev_month |
spend_pct_change_ vs_prev_year |
online_transactions |
online_spend |
transaction_intermediary |
spend_by_ transaction_intermediary |
bucketed_customer_frequency |
mean_spend_per_customer_ by_frequency |
bucketed_customer_incomes |
mean_spend_per_customer_ by_income |
customer_home_city |
||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx |
2 | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx |
3 | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx |
4 | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx |
5 | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx |
6 | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx |
7 | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx |
8 | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx |
9 | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx |
10 | 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 | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
String | ["Target"] | Brand Name | |
raw_total_spend
|
76050.12 | ||
raw_num_transactions
|
1521 | ||
raw_num_customers
|
435 | ||
placekey
|
[email protected] | ||
String | ["SG_BRAND_59dcabd7cd2395a2"] | Brand ID | |
median_spend_per_transaction
|
50.00 | ||
median_spend_per_customer
|
174.83 | ||
spend_per_ transaction_percentiles
|
{“25”: 23.11, “75”: 80.99} | ||
spend_by_day
|
[2535.34, 5214.11, … ] | ||
spend_per_transaction_by_day
|
[20.33, 70.22, … ] | ||
spend_by_day_of_week
|
{“Monday”: 10864.11, “Tuesday”: 15200.10, … } | ||
spend_pct_change_ vs_prev_month
|
5 | ||
spend_pct_change_ vs_prev_year
|
-10 | ||
online_transactions
|
310 | ||
online_spend
|
7512.22 | ||
transaction_intermediary
|
{“No Intermediary”: 900, "Apple Pay": 215, "DoorDash": 15... | ||
spend_by_ transaction_intermediary
|
{“No Intermediary”: 10400.12, "Apple Pay": 2015.00, "Door... | ||
bucketed_customer_frequency
|
{ "1": 500, "2": 302, "3": 101, "4": 20, "5-10": 90, ">10... | ||
mean_spend_per_customer_ by_frequency
|
{ "1": 10000.10, "2": 31000.32, "3": 999.01, "4": 200, "5... | ||
bucketed_customer_incomes
|
{“<25k”: 135, “25-45k”: 225, “45-60k”: 500, “60-75k”: 252... | ||
mean_spend_per_customer_ by_income
|
{“<25k”: 1700.10, “25-45k”: 2221.51, “45-60k”: 5000.00, “... | ||
customer_home_city
|
{“Palo Alto, CA”: 22, “Redwood City, CA”: 308, “Mountain ... |
Description
Geography
History
Volume
1,100 | Brands |
400,000 | POI |
Pricing
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Online Purchase Data Aggregated Transaction Patterns for In-Person and Online?
SafeGraph Spend is an aggregated transaction dataset of consumer data containing spending behavior at individual points of interest (with online purchase data). This dataset includes transaction consumer data at individual POIs in the US based on aggregated debit card and credit card transactions.
What is Online Purchase Data Aggregated Transaction Patterns for In-Person and Online used for?
This product has 5 key use cases. SafeGraph recommends using the data for purchase behavior analytics, Consumer Trend Analysis, Retail Analytics, Consumer Data Enrichment, and Ecommerce Data Enrichment. Global businesses and organizations buy Purchase Intent Data from SafeGraph to fuel their analytics and enrichment.
Who can use Online Purchase Data Aggregated Transaction Patterns for In-Person and Online?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Purchase Intent Data. Get in touch with SafeGraph to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Online Purchase Data Aggregated Transaction Patterns for In-Person and Online go?
This Tabular Data has 2 years of historical coverage. It can be delivered on a monthly basis.
Which countries does Online Purchase Data Aggregated Transaction Patterns for In-Person and Online cover?
This product includes data covering 1 country like USA. SafeGraph is headquartered in United States of America.
How much does Online Purchase Data Aggregated Transaction Patterns for In-Person and Online cost?
Pricing information for Online Purchase Data Aggregated Transaction Patterns for In-Person and Online is available by getting in contact with SafeGraph. Connect with SafeGraph to get a quote and arrange custom pricing models based on your data requirements.
How can I get Online Purchase Data Aggregated Transaction Patterns for In-Person and Online?
Businesses can buy Purchase Intent Data from SafeGraph and get the data via S3 Bucket, UI Export, and REST API. Depending on your data requirements and subscription budget, SafeGraph can deliver this product in .csv format.
What is the data quality of Online Purchase Data Aggregated Transaction Patterns for In-Person and Online?
SafeGraph has reported that this product has the following quality and accuracy assurances: 100% fill rates. You can compare and assess the data quality of SafeGraph using Datarade’s data marketplace. SafeGraph has received 17 reviews from clients. SafeGraph appears on selected Datarade top lists ranking the best data providers, including Best Data Providers For Location-Based Marketing and Top 10 POI Data Providers & APIs.
What are similar products to Online Purchase Data Aggregated Transaction Patterns for In-Person and Online?
This Tabular Data has 3 related products. These alternatives include Consumer Data Aggregated Spend Patterns Retail Transactions, Location & Territory Data Geospatial, Sentiment (Reviews), Footfall, Business Listings & Store Location 200 Million+ POIs Mapped, and Versium REACH - Consumer Lifestyle and Interest (Investing, Health and Fitness, Purchase Data, etc) Append B2C, USA, GDPR and CCPA Compliant. You can compare the best Purchase Intent Data providers and products via Datarade’s data marketplace and get the right data for your use case.