What it is

Behavioral targeting uses Walmart first-party shopper signals to reach audiences based on how customers shop, browse, search, and engage across Walmart properties.

Unlike contextual targeting, behavioral targeting focuses on who the shopper is, not the page they are currently viewing.

Best used for:

Customer engagement

Re-engage shoppers based on previous purchases, browsing, or brand interactions.

Prospecting

Reach shoppers likely to purchase based on predictive and behavioral models.

Audience activation

Activate custom audiences, demographic segments, personas, and category shoppers.

Available audience categories

Brand audiences

Reach shoppers based on interactions with your brand using: historical, lapsed_buyers, and predictive audience models.

Important: Only available to advertisers or suppliers who own the brand.

Retail audiences

Reach shoppers based on Walmart retail-category behavior using historical and predictive category audiences.

Custom audiences

Activate rule-based, propensity, lookalike, advanced, and external audiences created through Audience APIs or Asset Library.

Demographic audiences

Reach Walmart shoppers based on demographic characteristics regardless of purchase or browsing behavior.

age
gender
household_income

Persona audiences

Reach curated machine-learning audience segments built around shared interests, lifestyles, and shopping behaviors.

auto
food_beverages
lifestage
lifestyle
shopping_habits

Audience type reference

Behavioral targeting attributes allow you to indicate the various tactics or specific targeting attributes applicable to each behavioral targeting audienceType.

brand audienceType attribute objects

object	Description
`historical`	Customers who have interacted with your brand recently Equivalent to "Recent buyers" in the UI
`lapsed_buyers`	Customers who interacted with your brand in the past but not recently
`predictive`	Uses machine learning to identify customers likely to purchase from your brand

object	Description
`rule_based`	Audiences built from purchase criteria, browse behavior, ad exposure, and more.
`propensity`	Targets high-intent customers likely to buy specific items or categories.
`brand_affinity`	Targets customers based on their demonstrated loyalty to your brand or competitor brands.
`lookalike`	Targets new customers with similar behavior to an existing seed audience.
`advanced`	Highly specific audience segments created by combining brand, category, persona or custom segments using Boolean operators AND, OR, and NOT.
`external`	Scintilla (formerly Luminate) audiences

object	Description
`age`	Audience segmented by age range.
`gender`	Audience segmented by gender.
`household_income`	Audience segmented by estimated household income.

object	Description
`auto`	Customers segmented by vehicle ownership, intent, or brand/model preferences.
`food_beverages`	Customers defined by food and beverage interests (e.g., cooking styles, dietary needs).
`lifestage`	Customers defined by key life events (e.g., recent movers, busy families).
`lifestyle`	Customers with shared interests/traits (e.g., games, cat parents, travelers).
`shopping_habits`	Customers with specific Walmart or seasonal buying habits (e.g., everyday savers, Back-to-School shoppers).

object	Description
`historical`	Targets users with recent purchases in a category
`predictive`	Used machine learning to identify customers actively researching or buying in a category. UI equivalent: In-market

How behavioral targeting works

Component	Purpose
`audienceType`	Defines the audience category
`attribute`	Defines the behavioral model
`id`	Identifies the specific target audience (See Targeting List endpoint)

This endpoint supports negative behavioral targeting.

Use NOT in an expression-based schema
Use exclude in a flat schema

Object structure: Behavioral targeting

Developers have two options when working with targeting data. Both schemas represent the same targeting intent but use two different structures.

Expression behavioral targeting schema

"targeting": { "and": [ { "and": [ { "behavioral": [ { "audienceType": "retail", "attribute": "historical", "id": 134 }, { "audienceType": "retail", "attribute": "historical", "id": 243234 } ] }, { "not": [ { "behavioral": [ { "audienceType": "retail", "attribute": "historical", "id": 134 }, { "audienceType": "retail", "attribute": "historical", "id": 243234 } ] } ] }, { "geoTargets": [ { "id": 123 }, { "id": 234 }, { "id": 235 } ] } ] }

Flat behavioral targeting schema

"targeting": { "behavioral": { "include": [ { "audienceType": "retail", "attribute": "historical", "id": 134 }, { "audienceType": "retail", "attribute": "historical", "id": 234 } ], "exclude": [ { "audienceType": "retail", "attribute": "historical", "id": 123 }, { "audienceType": "retail", "attribute": "historical", "id": 111 } ] }, "geoTargets": { "include": [{ "id": 123 }, { "id": 234 } ] }
}