This guide provides a structured implementation framework for deploying Salesforce Personalization across your digital channels. It is intended for project managers, solution architects, and implementation engineers responsible for planning and executing a personalization rollout — whether targeting web properties, mobile applications or offline batch audiences.
What This Guide Covers
Salesforce Personalization is a multi-channel decisioning platform. This guide walks through each major implementation track, from establishing the data foundation to measuring engagement lift across all touchpoints.
- Data Capturing & Modeling — Configure Data 360 data streams, object mapping, and identity resolution to power all personalization channels.
- Web Implementation — Deploy real-time personalization on web properties using the SDK, sitemap, and Web Personalization Manager.
- Mobile Implementation — Integrate the Personalization SDK into iOS and Android apps to deliver in-app personalized experiences.
- Batch Personalization — Trigger offline personalization decisioning for email, direct mail, and other non-real-time channels.
- Personalization API — Query the decisioning engine directly via REST API for headless or custom front-end implementations.
Implementation Project Flow
flowchart TD
A([Start]) --> B[Define Use Cases & Success Metrics]
B --> C[Set Up Data 360 Foundation]
C --> D{Choose Channel}
D --> E[Web Implementation]
D --> F[Mobile Implementation]
D --> G[Batch Personalization]
D --> H[Personalization API]
E & F & G & H --> I[Configure Personalization Logic]
I --> J[Build Recommenders & Response Templates]
J --> K[Launch & Validate Experiences]
K --> L[Measure Performance & Attribution]
L --> M([Scale & Iterate])
Implementation Phases
Regardless of channel, every Salesforce Personalization deployment follows the same phased progression.
Phase 1 — Foundation
Before any personalization can be delivered, the underlying data infrastructure must be in place. This involves connecting behavioral and profile data sources to Data 360, mapping them to the appropriate Data Model Objects (DMOs), and configuring identity resolution so that anonymous and known user activity can be unified into a single profile.
A reliable data foundation ensures that every downstream decision is based on accurate, real-time information.
Phase 2 — Strategy & Configuration
With data flowing correctly, the next step is defining the logic that governs what each user sees. This includes building Recommenders that evaluate profile and item data to surface relevant content, creating Response Templates that define the format and fields returned by the decisioning engine, and setting up Personalization Points — the named slots where decisions are evaluated and delivered.
Targeting rules, audience segments, and A/B experiment configurations are all applied at this stage.
Phase 3 — Channel Delivery
Personalization is surfaced through the appropriate delivery mechanism for each channel.
- Web: Experiences are deployed using the Web Personalization Manager, or rendered client-side via direct API calls from custom front-end code.
- Mobile: Experiences are driven by SDK integrations within native iOS and Android applications, enabling in-app personalization at key user touchpoints.
- Batch: Audiences are generated and exported for activation in downstream systems such as Marketing Cloud, enabling personalized email and direct mail campaigns.
- Personalization API: Decisions can be requested directly via REST API, giving headless or custom implementations full control over how and where personalized content is rendered.
Phase 4 — Measurement & Iteration
Post-launch, performance is tracked through the Pipeline Intelligence dashboard, attribution models, and experiment analytics. Engagement lift, decision volume, and cohort performance data inform ongoing refinement of targeting rules and recommender configurations.
Data 360 as the Core Engine
All personalization channels share a common dependency on the Data 360 platform. Data 360 handles real-time data ingestion, identity resolution, profile unification and item catalog management. It serves as the single source of truth from which the decisioning engine evaluates eligibility and generates personalized outcomes.
Summary
A Salesforce Personalization implementation spans data ingestion, profile unification, decisioning logic, channel delivery and performance measurement. This guide covers each of these layers across all supported channels — web, mobile, batch and API — providing the technical and strategic detail needed to plan and execute a complete rollout.