User Predictive Spend 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
Using proprietary predictive models, the User Predictive Spend extension enables you to create audiences based on targeting conditions relating to known users, such as their likelihood to spend in specific categories, to attrit, and many additional factors. You can then use these audiences in your campaigns to provide effective offers to each user type.
Targeting conditions
User Predictive Spend targets users based on their likelihood to spend in certain categories, and on user behavior similar to specific use cases and actions.
| Condition | Description |
|---|---|
| Expected spend in category | Target users whose likelihood to spend in the specific category is either high, medium, or low |
| Expected top 3 high-spend subcategories | Target users who are likely to spend the most in the selected subcategories |
| Expected top 3 new subcategories for expansion |
Target users who are most likely to start spending in the selected subcategory |
| Expected new category for expansion |
Target users whose likelihood to expand to a selected category is high, medium, or low |
| Risk for attrition |
Target users whose likelihood to attrit is high, medium, or low |
| Likelihood to reactivate card |
Target users whose likelihood to reactivate their card is high, medium, or low |
| Likelihood to spend online | Target users whose likelihood to spend online is high, medium, or low |
| Similarity to a small business user | Target users whose similarity to a business user is high, medium, or low |
| 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 |
Use case examples
- Increase the value of users by cross-promoting with other products based on propensity models. Offer a new business card to users who are highly similar to a business user.
- Reduce churn and increase card usage by targeting users with a high risk for attrition with attractive offers based on the categories in which they are most likely to spend.
- Increase category diversity and drive card usage in specific categories by personalizing experiences with offers and incentives, targeting users based on the categories they are most likely to spend on or expand to.
Prerequisites and limitations
- To get User Predictive Spend, contact your account manager and find out if the extension is available in your geographic location.
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.