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 |
About Cardholder Spend Insights
Cardholder Spend Insights is a set of extensions designed for card issuers, to enable creating and targeting audiences in order to offer customers hyper-personalized experiences. The extensions use rich insights into users' past spending behavior and also leverage Mastercard's 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.