Sold by: Mastercard
Deployed on AWS
Mastercard Audiences analyzes aggregated and anonymized consumer spending patterns to create standard or custom audience segments at the micro-geographic (i.e., Zip+4) level. Customers can apply Audiences insights to their data sets to make those insights actionable.
Overview
Mastercard Audiences
Data Product Overview
Mastercard Audiences analyzes aggregated and anonymized consumer spending patterns to create standard or custom audience segments at the micro-geographic (i.e., Zip+4) level. Our customers can apply Audiences insights to their data sets to make those insights actionable. There are two types of Mastercard Audiences:
- Mastercard Standard Audiences – Hundreds of off-the-shelf audiences based on aggregated and anonymized transaction data in different industries and seasonal shopping behavior. a. Some examples include:
- ZIP+4 - Online Food & Meal Delivery – Frequent Buyers
- ZIP+4 - Online Shoppers – High Spenders
- ZIP+4 - Likely to Be a Small Business – In Market
- ZIP+4 - Luxury Retailers – High Spenders
- ZIP+4 - Fast Fashion Apparel Buyers – High Spenders
- ZIP+4 - Big Ticket Shoppers (Online) – Frequent Buyers
- ZIP+4 - Affluent Shoppers
- ZIP+4 - Brick and Mortar Shoppers
- ZIP+4 - Luxury Travelers & Tourists
- Mastercard Custom Audiences – Built-to-order audiences designed to a client’s specifications to align directly with their advertising strategy and based on layering the following attributes:
- Transactions (e.g., amount, frequency, offline vs. online)
- Date and Time (e.g., date range, time of day, weekend vs. weekday)
- Geography (e.g., country, state/province, DMA, city, region)
- Industry / Merchants (e.g., Merchant Category Codes (MCCs), custom aggregate set of industry merchants)
Use Cases
Audiences customers can leverage aggregated and anonymized Audiences for:
- Customer Segmentation: Identify customer segments with a high statistical probability of making a purchase within their industries
- Tailored Marketing & Promotions: Customize creative messaging based on an understanding customer segment spend across categories and channels
- Ad Research/Ad Sales: Build look-alike models to extend campaign reach
- Analytics & Insights: Enhance their existing models by applying cross-category spend behavior
- Location-Based Automation: Personalize using geographic-based spend insights
Metadata
| Description | Value |
|---|---|
| Update Frequency | Monthly for standard audiences, custom segment refreshes are available upon request |
| KPI(s) | Audiences makes metrics such as Demi-decile, Propensity and Binary available for each ZIP+4. Note: Some scores may be omitted at Mastercard’s discretion if data cannot be sufficiently aggregated or anonymized |
| Demi-decile | Ranking of 1-20 based on spend, where #1 stands for top 1-5% of spenders, #2 stands for top 6-10% of spenders,…,#20 stands for bottom 95-100% of spenders |
| Propensity Models (forecast data) | A probability to spend 0-100 generated by predictive model score |
| Binary | A value of 0 or 1, where 1 indicates the top 20% of spenders or propensity scores and 0 the bottom 80% |
| Geographic coverage | Total USA, Zip, Zip+4 level reporting in the US |
| Time period coverage | Actuals: Standard Audiences are based on the past year, however three years of data is readily available for building custom audiences. Propensity model forecasts apply to the next 1 - 3 months |
| Data Source(s) | Mastercard Audiences are built by creating segments based on spending insights from anonymized and aggregated transaction data |
| Original Publisher of data | Mastercard |
| Data Set(s) Format(s) | CSV, Excel |
| Raw or scraped data | Aggregated and anonymized transaction data |
| Key Fields | Geography (Zip5), Micro-Geography (Zip+4), Audience Segment Score(s) aggregated to the Zip+4 level (e.g. Zip 10001-0001 scores high for High Spenders in Video/TV Streaming Services, Zip 10001-0002 scores low for Frequent Spenders of Online Food & Meal Delivery Services) |
| Key Words | Audiences, Segments, Shoppers, Consumer Insights, Online Shoppers, In-store Shoppers, Propensity Models, Consumer Behaviors, Seasonal Shoppers, Travel and Tourism Insights, Personas, Transaction, Spend, Credit Card, Marketing, Advertising, Digital Advertising, Customer Segmentation, Customer Targeting |
| Number of companies/brands covered | Mastercard Audiences covers aggregated and anonymized spend data in 19 Industries and 88 Sub-Industries |
| Data Channels | Aggregated and anonymized POS Transaction data, including both online and brick-and-mortar sales |
Tables
The three tables below illustrate various data file layouts. The first shows Demi-Decile Audiences (Actuals), the second shows Propensity Audiences (Forecast), and the third shows Audiences presented in a Binary Threshold format (applying to Demi-Decile or Propensity outputs for simplification).
Demi-Decile Audiences (Actuals)
| Geography (Zip5) | Micro-Geography (Zip+4) | Standard Audience 1 | … | Standard Audience n | | --- | --- | | 10001 | 0001 | 19 | … | 2 | | 10001 | 0002 | 7 | … | 14 | | 10001 | 0003 | 5 | … | 3 |
Propensity (Forecast)
| Geography (Zip5) | Micro-Geography (Zip+4) | Audience 1 Modeled Probability | … | Audience n Modeled Probability | | --- | --- | | 10001 | 0001 | 0.95 | … | 0.65 | | 10001 | 0002 | 0.46 | … | 0.43 | | 10001 | 0003 | 0.84 | … | 0.25 |
Binary Threshold Flag (Applied to Demi-Decile or Propensity outputs for simplification)
Geography (Zip5) | Micro-Geography (Zip+4) | Standard Audience 1 | … | Standard Audience n ----|----- 10001 | 0001 | 1 | … | 1 10001 | 0002 | 0 | … | 0 10001 | 0003 | 1 | … | 0
Dataset Specification
The datasets are updated monthly the week after calendar month close.
Pricing Information
Mastercard Audiences segments are available to purchase. Pease inquire with Mastercard at aws.sales@mastercard.com for pricing information.
Need Help?
For questions related to Amazon Data Exchange billing or invoicing, please reach out to your AWS Technical Account Manager or contact Amazon Data Exchange customer support directly.
For product-related questions, please reach out to Media_Delivery@mastercard.com .
About Your Company
Mastercard (NYSE: MA) Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
Refunds not applicable
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Additional details
You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
Mastercard Audiences | All historical revisions | All future revisions | Not included | Not included |
Similar products
Tealium connects customer data - spanning web, mobile, offline, and IoT devices - so brands can connect with their customers, generate insights, and drive growth.
Transform video content and audience data into actionable intelligence
Publicis Sapient’s Unified Audience Accelerator for Salesforce Data Cloud and AWS Data Clean Rooms (P360) allows businesses to seamlessly leverage data held in Salesforce Data Cloud in AWS Clean Rooms to collaborate with their partners without having to share the underlying data. PS360 empowers businesses to match and analyze datasets, gain rich audience insights, and unlock revenue opportunities using first-, second- and third-party data. In the new cookieless landscape, marketers using PS360 can enhance data insights, improve their return on ad spend and optimize performance measurement.
Unlock transformative audience insights and marketing automation with Mactores’ AI-Powered Audience Insights & Engagement Tools. Designed for Media, Entertainment, Gaming, and Telecom (TMEG) industries, our solution delivers AI-driven dashboards, predictive analytics, and advanced customer segmentation to supercharge engagement and retention.
Personalize campaigns with AI-generated content, optimize monetization strategies in real time, and enhance operational efficiency across platforms. From fraud detection and content localization to streaming optimization and in-game analytics, Mactores empowers data-driven decision-making at scale.
Harness the power of AI and ML to stay competitive in a fast-evolving digital landscape—future-proof your business with Mactores today.
Personalization Super App integrating AWS Personalize, SageMaker & Bedrock LLM to deliver privacy-conscious, AI-driven personalized customer experiences that scale.