Behavioral recommendation
This guide describes the behavioral recommendation use case pattern, which uses Adobe Journey Optimizer (AJO) Decisioning, Real-Time Customer Data Platform (RT-CDP), and Adobe Experience Platform (AEP) to deliver personalized recommendation experiences across web, mobile app, and email channels. It is designed for solution architects, marketing technologists, and implementation engineers who need to understand what this pattern does, the business objectives it supports, the tactical use cases it enables, and the Adobe applications involved.
Behavioral Recommendation generates item-level or content-level recommendations using behavioral signals – product views, purchases, content interactions, search queries – combined with AJO Decisioning selection strategies and ranking models. Unlike offer decisioning — which governs a bounded set of offers, promotions, or incentives using eligibility rules and business constraints — this pattern operates on large, continuously changing item catalogs (products, articles, videos) where selection is driven by behavioral affinity signals rather than governed eligibility.
Use case pattern
Behavioral Recommendation
Generate item-level or content-level recommendations based on behavioral signals, using AJO Decisioning selection strategies and ranking models to serve contextual content.
Execution plan: Behavioral Signal Ingestion > Decisioning Strategy Evaluation > Recommendation Delivery > Reporting
Use case overview
Organizations with product catalogs, content libraries, or media libraries need to surface the most relevant items to each visitor based on their behavioral history and in-session activity. Whether it is a “recommended for you” carousel on a homepage, a cross-sell widget on a product detail page, or product recommendations embedded in an email campaign, the underlying challenge is the same: match each visitor’s behavioral profile to the most relevant items from a catalog, then deliver those recommendations in the right channel at the right moment.
This pattern addresses that challenge by ingesting behavioral signals in real time via Web SDK or Mobile SDK, processing them through AJO Decisioning selection strategies that combine item attributes with behavioral context, and delivering the recommended items through web, in-app, or email channels. Ranking models can be formula-based (e.g., sort by category affinity score) or AI-ranked (e.g., personalized recommendation model). The pattern also handles cold-start scenarios for new visitors with no behavioral history by configuring fallback recommendations.
The target audience for this pattern includes ecommerce merchandising teams, content personalization teams, and digital experience teams seeking to improve engagement, conversion, and average order value through personalized recommendations driven by real user behavior.
Key business objectives
The following business objectives are supported by this use case pattern.
Drive cross-sell and upsell revenue
Promote complementary and premium products or services to existing customers based on behavior and purchase history.
KPIs: Upsell/Cross Sell %, Incremental Revenue, Customer Lifetime Value
Increase conversion rates
Improve the percentage of visitors and prospects who complete desired actions such as purchases, sign-ups, or form submissions.
KPIs: Conversion Rates, Lead Conversion, Cost Per Lead
Deliver personalized customer experiences
Tailor content, offers, and messaging to individual preferences, behaviors, and lifecycle stage.
KPIs: Engagement, Conversion Rates, Customer Satisfaction (CSAT)
Example tactical use cases
The following are common tactical implementations of this pattern:
- Product cross-sell widget on product detail page (“customers also bought”)
- “Recommended for you” carousel on homepage based on browse history
- Content recommendations on media site based on reading behavior
- “Recently viewed” combined with similar items widget
- Post-purchase complementary product recommendations
- Email product recommendations based on behavioral affinity
- Category-specific recommendations based on in-session browse behavior
- Search result re-ranking based on behavioral signals
Key performance indicators
The following KPIs help measure the effectiveness of behavioral recommendation implementations.
Applications
The following applications are used in this use case pattern.
- Adobe Journey Optimizer (AJO) Decisioning – Selection strategies, ranking models, item catalogs, and decision policies that evaluate behavioral signals and return the most relevant items for each visitor
- Adobe Real-Time Customer Data Platform (RT-CDP) – Behavioral profile data accumulation, audience evaluation for recommendation scoping, and computed attributes for behavioral affinity scoring
- Adobe Experience Platform (AEP) – Behavioral event ingestion via Web SDK and Mobile SDK, Edge Network processing, XDM schema management for event and catalog data
Related documentation
The following resources provide additional detail on the technologies and capabilities used in this pattern.