Known-visitor web/app personalization
This guide describes the known-visitor web/app personalization use case pattern, which uses Adobe Journey Optimizer (AJO) and Adobe Real-Time Customer Data Platform (RT-CDP) to deliver personalized content to identified visitors across digital surfaces. 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.
Known-visitor web/app personalization is the primary personalization pattern for authenticated digital experiences. Unlike anonymous visitor personalization, which relies solely on in-session behavioral signals, this pattern leverages the full unified profile: historical behavioral data, segment membership, loyalty tier, purchase history, lifecycle stage, computed attributes, and propensity scores. It supports personalization across web pages (via AJO web channel), mobile in-app messages, and content cards.
Use case pattern
This section describes the core pattern and its execution plan.
Known-visitor web/app personalization
Deliver personalized content, offers, or promotions to an identified visitor based on real-time profile and segment membership across web, mobile in-app, and content card surfaces.
Execution plan: Audience Evaluation > Personalization Decisioning > Surface/Channel Configuration > Content Delivery > Impression Tracking > Reporting
Use case overview
Organizations with authenticated digital properties – e-commerce sites, banking portals, subscription services, loyalty programs, mobile apps – need to deliver personalized experiences that reflect each customer’s relationship with the brand. When a visitor logs in or is recognized through identity resolution, the platform can access their full unified profile and deliver content tailored to their specific attributes, behaviors, and preferences.
This pattern addresses the scenario where an identified visitor arrives on a web property or opens a mobile app, and the system must determine the optimal content, offer, or promotion to display based on real-time profile data and audience membership. The personalization decision happens at the edge in milliseconds, enabling sub-second content delivery without perceptible latency.
The pattern supports both deterministic personalization (where specific content maps to specific audience segments) and dynamic decisioning (where AJO Decisioning evaluates eligibility rules and ranking strategies to select the optimal content per profile). It spans multiple digital surfaces – web pages, mobile in-app messages, and content cards – enabling consistent personalization across the customer’s digital journey.
Key business objectives
The following business objectives are supported by this use case pattern.
Deliver personalized customer experiences
Tailor content, offers, and messaging to individual preferences, behaviors, and lifecycle stage. For more information, see Deliver personalized customer experiences.
KPIs: Engagement, Conversion Rates, Customer Satisfaction (CSAT)
Increase website engagement
Improve time on site, pages per session, and interaction with web content through relevant experiences. For more information, see Increase website engagement.
KPIs: Time On (web) Page, Engagement, Conversion Rates
Increase mobile app engagement
Drive daily active usage, feature adoption, and in-app conversions through personalized in-app experiences.
KPIs: Engagement, Retention, Conversion Rates
Example tactical use cases
The following are common tactical implementations of this pattern:
- Homepage hero personalization by loyalty tier or lifecycle stage – display different hero banners based on whether the customer is new, active, at-risk, or VIP
- Product recommendation carousel based on purchase history – surface relevant product suggestions using past purchase data and product affinity scores
- Personalized promotional banner by customer segment – show different promotions to high-value, at-risk, and new customer segments
- In-app message for mobile users based on feature adoption – guide users to underutilized features based on their usage patterns
- Content card with personalized offer on account dashboard – persistent, dismissible offers tailored to the customer’s profile
- Personalized pricing or discount display based on customer tier – show tier-specific pricing or exclusive discounts to loyalty program members
- Cross-sell recommendation widget based on owned products – suggest complementary products or services based on current portfolio
- Personalized navigation or content ordering based on interests – reorder content modules or navigation elements based on demonstrated preferences
Key performance indicators
The following KPIs help measure the effectiveness of this use case pattern.
Applications
The following applications are used in this use case pattern.
- Adobe Journey Optimizer (AJO) – Web channel configuration, in-app channel configuration, content card channel configuration, decisioning (offer selection and ranking), message authoring (personalized content creation), campaign execution, content experimentation, and reporting
- Adobe Real-Time Customer Data Platform (RT-CDP) – Audience evaluation (edge, streaming, and batch), real-time profile lookup via Edge Network, profile enrichment with computed attributes and propensity scores
- Adobe Experience Platform (AEP) – Profile store, identity service, Web SDK, Mobile SDK, datastream configuration, edge network delivery
Related documentation
The following resources provide additional detail on the technologies and configurations referenced in this guide.