Offering discounts is one of the quickest ways to attract potential customers and encourage purchases. While some customers prefer to maintain a premium shopping experience, others are more responsive to discounted offers. Identifying and targeting users with an affinity for discounted products is valuable for marketers, as it allows them to personalize campaigns that drive conversions without sacrificing margins.
Discount Affinity uses a multi-class classification model to segment users based on their sensitivity to discounts, classifying them into three groups: High, Medium, and Low affinity. This model helps you tailor promotional offers, such as sending heavy discounts to high-affinity users or avoiding discounts for low-affinity users likely to buy at full price.
The Discount Affinity segment aims to determine a user's likelihood of engaging with discounted products by analyzing their past interactions and purchasing behavior.
Model Details
The Discount Affinity model runs on a behavior-based classification algorithm. The following details explain how the model operates and how frequently user segments are updated:
Model type: Multi-class classification
Segment update frequency: Daily
Data span: Last 6 months
Calculation time: 30 days
Data Used
The model evaluates a user's interactions with discounted products using data from both web behavior and CRM sources, where available. All session-based clickstream events that include product discount information are considered, such as:
Product page views
Add-to-cart events
Purchases
CRM purchase data is included only if combined with web session data, as in-session behavior forms the basis of this segmentation. CRM-only setups are not used for this segment.
Discount Affinity compares two product prices: the original price and the price after discount. On web, send unit_price (the original price) and unit_sale_price (the price after discount) in the Insider Object. In the mobile app SDKs, set price and salePrice on the product object. If the sale price is missing or both prices are identical, no discount can be detected. See Product Object for the parameter details.
Key Features
Past user interactions with discounted items
Purchase behavior during promotions or seasonal campaigns
Browsing patterns indicating price sensitivity (e.g., visiting sale sections or filtering by price)
Segment Breakdown
Users are segmented into the following categories:
High Discount Affinity (Discount Seekers): These users consistently engage with discounted products, browsing sale items, adding them to their carts, and making purchases during sales events. For example, a user who frequently visits the sale section, browses discounted products, and makes purchases during promotional periods.
Medium Discount Affinity: Users in this category show moderate engagement with discounted products. They might occasionally purchase during sales, but don't rely heavily on discounts. For example, a user who typically purchases full-price items but uses discounts for higher-value products.
Low Discount Affinity (Premium Shoppers): These users rarely interact with discounts and are more inclined to buy full-priced or newly released products. For example, a user regularly purchases from the new arrivals section and does not engage during sales campaigns.
By default, new users with no prior interaction data are assigned to the Medium affinity group. As their behavior develops, they are reclassified accordingly.
Use Cases
This segmentation enables targeted promotional strategies, such as:
Offering exclusive discount codes to high-affinity users
Preserving brand value by limiting discounts for low-affinity users
Testing discount elasticity across user groups to maximize ROI