User Spend History is an Experience OS extension powered by the data of Mastercard. It's part of the Cardholder Spend Insights extension set for card issuers.
About Cardholder Spend Insights
The User Spend History extension enables you to create audiences based on targeting conditions relating to known users, including their spending behavior in the past year and their card status:
- Across channels: Digital wallet, in-store, and online.
- In categories: For example, Children’s Apparel, Grocery Stores, and more.
- Card status: For example, the card has been acquired but hasn’t been activated, new cardholder, card has recently lapsed, and more.
Targeting conditions
User Spend History
Describes the user's general and cross-channel spending behavior.
Note that the condition This time last year means the following: The time period of the next 4 weeks, only from last year. So, for example, if today is December 1, 2023, the targeting is based on December 1-28, 2022.
| Condition | Description |
|---|---|
| Total spend ($) | Target users based on their total spend value in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Total number of transactions | Target users based on their number of transactions in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Monthly average spend ($) |
Target users based on the user average monthly spend across all cards in the past 12 months |
| Monthly average number of transactions |
Target users based on their total number of transactions in the past year divided by 12 (for new cardholders, by the number of months since their first transaction) |
| Digital wallet spend ($) |
Target users based on their total spend value using the digital wallet during either the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year). |
| Digital wallet total number of transactions |
Target users based on their number of transactions on a digital wallet in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Online spend ($) |
Target users based on their online total spend value in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Online total number of transactions | Target users based on their online number of transactions in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Recurring payments spend ($) | Target users based on their recurring payments value across their cards in the past 1, 3, 6, or 12 monthsor this time last year (the time period of the next 4 weeks, only last year) |
| Recurring payments total number of transactions | Target users based on their recurring payments number across their cards in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| In-store spend ($) | Target users based on their total in-store spend value in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| In-store total number of transactions | Target users based on their number of transactions in-store in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Time since last transaction | Target users based on the number of days since their last transaction |
| Time since first transaction | Target users based on the number of days since their first transaction |
| Time in early months on book (EMOB) | Target users based on the number of days into their first 6 months |
| Number of Active Months | Target users based on the number of months in the past year in which at least 1 transaction was made |
User Spend By Category
Note that the condition This time last year means the following: The time period of the next 4 weeks, only from last year. So, for example, if today is December 1, 2023, the targeting is based on December 1-28, 2022.
| Condition | Description |
|---|---|
| Top spend categories | Target users who spend the most in the selected categories over the past 12 months |
| Top activity categories | Target users who made the highest number of transactions in the selected categories over the past 12 months |
| Category spend diversity | Target users based on their spend diversity in the various categories: High (spend across a high number of categories), medium, or low (spend across a low number of categories) in the past 12 months |
| Spend on category ($) | Target users based on their total spend value in a specific subcategory in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Average Ticket Size | Target users based on their average transaction size in specific subcategory in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Total number of transactions | Target users based on their number of transactions in a specific subcategory in the past 1, 3, 6, or 12 months or this time last year (the time period of the next 4 weeks, only last year) |
| Category | Subcategory |
| Apparel & Accessories | Children's Apparel |
| Family Apparel | |
| Women's Apparel | |
| Men's Apparel | |
| Miscellaneous Apparel | |
| Jewelry and Giftware | |
| Luggage and Leather Stores | |
| Sporting Goods / Apparel / Footwear | |
| Shoe Stores | |
| Automotive & Cars | Automotive New and Used Car Sales |
| Automotive Fuel | |
| Automotive Used Only Car Sales | |
| Automotive Retail | |
| Miscellaneous Vehicle Sales | |
| Education | College, University Education |
| Elementary, Middle, High Schools | |
| Miscellaneous Educational Services | |
| Electronics | Consumer Electronics / Appliances |
| Computer / Software Stores | |
| Camera / Photography Supplies | |
| Entertainment | Amusement, Recreation Activities |
| Casino and Gambling Activities | |
| Live Performances, Events, Exhibits | |
| Miscellaneous entertainment and recreation | |
| Movie and Other Theatrical | |
| Professional Sports Teams | |
| Video and Game Rentals | |
| Financial Services & Insurance | Financial Services |
| Insurance | |
| Grocery & Drug Stores | Beer / Wine / Liquor Stores |
| Drug Store Chains | |
| Grocery Stores | |
| Specialty Food Stores | |
| Health & Personal Care | Cosmetics and Beauty Services |
| Health / Beauty / Medical Supplies | |
| Home & Office | Home Furnishings / Furniture |
| Home Improvement Centers | |
| Office Supply Chains | |
| Media & Art | Arts and Craft Stores |
| Book Stores | |
| Music and Videos | |
| Newspapers and Magazines | |
| Giftware/Houseware/Card Shops | |
| Miscellaneous Publishing Industries | |
| Other | Advertising Services |
| Accounting and Legal Services | |
| Consumer Credit reporting | |
| Cleaning and exterminating Services | |
| Construction Services | |
| Courier Services | |
| Clothing, Uniform, Costume Rental | |
| Death Care Services | |
| Dry Cleaning, Laundry Services | |
| Employment, Consulting Agencies | |
| Equipment Rental | |
| Miscellaneous | |
| Information Retrieval Services | |
| Miscellaneous Administrative and Waste Disposal Services | |
| Manufacturing | |
| Miscellaneous Personal Services | |
| Miscellaneous Professional Services | |
| Maintenance and Repair Services | |
| Miscellaneous Technical Services | |
| Photofinishing Services | |
| Photography Services | |
| Public Administration | |
| Real Estate Services | |
| Software Production, Network Services and Data Processing | |
| Security, Surveillance Services | |
| Veterinary Services | |
| Warehouse | |
| Wholesale Trade | |
| Restaurants & Dining | Bars, Taverns & Nightclubs |
| Eating Places | |
| Retail Shopping | Agriculture/Forestry/Fishing/Hunting |
| Discount Department Stores | |
| Department Stores | |
| General Merchandise Stores | |
| Florists | |
| Pet Stores | |
| Toy Stores | |
| Wholesale Clubs | |
| Telecommunications & Cable | Communications, Telecommunications Equipment |
| Communications, Telecommunications, Cable Services | |
| Travel & Tourism | Accommodations |
| Travel Agencies and Tour Operators | |
| Airlines | |
| Bus | |
| Cruise Lines | |
| Vehicle Rental | |
| Railroad | |
| Other Transportation Services | |
| Taxi & Limosine | |
| Utilities | Utilities |
User Cards
Describes the cards portfolio of the issuer and the status of the card per user
| Condition | Description |
|---|---|
| Cardholder | Target users who owns a credit card, debit card, a premium card or a Business card |
| Card name For each card in the issuer’s portfolio |
Target users based on the status of their cards:
|
Use case examples
Cardholder insights are highly powerful for every life-cycle targeted campaigns across channels, for example:
- Personalize experiences and messaging to encourage digital card usage and spend, based on user historical spending behavior and channels of spending.
- Personalize off-platform messaging and experiences to encourage usage and reduce churn for users who have recently lapsed, based on historical spend across categories and channels.
- Increase category diversity and drive card usage in specific categories by personalizing experiences for offers and incentives, targeting users based on their spending categories.
Prerequisites and limitations
- To get User Spend History, contact your account manager.
About Cardholder Spend Insights
Cardholder Spend Insights is a set of extensions designed for card issuers, to create audiences based on targeting conditions that enable you to offer customers hyper-personalized experiences. The extensions use insights into users' past spending behavior based on rich data, and also leverage Mastercard propensity modeling techniques to predict future behavior.
Working with Mastercard data
The extensions use datasets generated by Mastercard (covering Mastercard cardholders), which are then provided to each issuer.
Each data set is aggregated at the user level and captures insights based on the user's activity in the past 12 months. The data is updated weekly.