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Online Purchase Data | Aggregated Transaction Patterns fo...
Online Purchase Data | Aggregated Transaction Patterns for In-Person and Online
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Online Purchase Data | Aggregated Transaction Patterns for In-Person and Online

SafeGraph
Start icon5.0(17)Badge iconVerified Data Provider
#
brands
raw_total_spend
raw_num_transactions
raw_num_customers
placekey
safegraph_brand_ids
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
Request Data Sample
Volume
400K
POI
Data Quality
100%
fill rates
Avail. Format
.csv
File
Coverage
1
Country
History
2
years

Data Dictionary

Product Attributes
Attribute Type Example Mapping
brands
 String ["Target"] Brand Name
raw_total_spend
 76050.12
raw_num_transactions
1521
raw_num_customers
435
placekey
 [email protected]
safegraph_brand_ids
 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

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.
Dataset contains more than 400K POI, focusing on consumer behavior and transactions at major brands and chains. The data clarifies if the purchase was made in-store on online. Samples/Tables Included POI information and consumer data: -Location name -Address -Category -Total spend -Number of transactions -Number of unique customers Additional Information: All SafeGraph POI-based datasets utilize Placekey as the primary key and are formatted as delimited CSVs. SafeGraph updates the Spend dataset every month with the past month's openings and closings and maintains a persistent Placekey across releases. Our detailed SafeGraph Spend Schema(1) is available online. Additionally please refer to Places Data Manual(2) for more detailed field definitions and methodologies. Our Places Summary Statistics(3) is updated with every monthly release to reflect data coverage. Data Dictionary Our documentation site includes detailed information for all of our products, but this product listing specifically only includes the Places data set. (1) Spend Schema: https://docs.safegraph.com/docs/spend (2) Places Data Manual: https://docs.safegraph.com/docs/places-manual (3) Places Summary Statistics: https://docs.safegraph.com/docs/places-summary-statistics Correlations with Quarterly Revenue - When rolled up to the parent brand, SafeGraph Spend data can be compared against financial indicators of companies (eg. quarterly revenue). SafeGraph uses such tests as a benchmark even though the use cases of Spend are far more varied than aggregating by brand. Based on one such analysis, SafeGraph data track with quarterly revenue from major brands like McDonald's, Chipotle, and Target, including cases where companies report online sales separately than overall revenue (e.g., Chipotle).

Geography

North America (1)
United States of America

History

2 years of historical data

Volume

1,100 Brands
400,000 POI

Pricing

Free sample available
SafeGraph 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
100%
fill rates

Delivery

Methods
S3 Bucket
UI Export
REST API
Frequency
monthly
Format
.csv

Use Cases

Categories

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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.

Pricing available upon request
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