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Salesforce Marketing Cloud Documentation

website·Indexed 33 days ago

https://mateuszdabrowski.pl/

Technical guides and best practices for Salesforce Marketing Cloud Engagement (MCE) and Marketing Cloud Next (MCN). Includes comprehensive SQL tutorials for data segmentation, Server-Side JavaScript (SSJS) development, performance comparisons between SSJS and AMPScript, and configuration guides for IP warming, deliverability, and Business Unit architectures.

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What's inside Salesforce Marketing Cloud

  1. Overview of Salesforce Personalization

    Salesforce Personalization (formerly Einstein Personalization) is a real-time personalization platform built on the Data 360 platform. It is designed to work with the broader Salesforce ecosystem, leveraging Agentforce and the Lightning Platform to provide a no-code, drag-and-drop interface for web implementation. It is generally available as of Winter '25 and is primarily targeted at small to medium businesses that require integration with Salesforce CRM (which is required).

  2. Overview of MCP Open Time Email Campaigns

    Open Time Email (OTE) in Marketing Cloud Personalization (MCP) allows for always up-to-date, personalized recommendations in emails. Unlike standard emails where content is static upon sending, OTE works by serving a dynamic image. When the user opens the email, MCP generates an image based on the current 'Next Best Action' for that user.

    Key Constraints:

    • Image-Based: Recommendations are rendered as images. If an email client blocks images, the recommendation will not be visible.
    • Caching: Some email clients cache images, which may delay or prevent updates to the recommendation.
    • Apple Mail Privacy: This feature blocks the dynamic generation and falls back to a static recommendation generated at the time of sending.

  3. Overview of Salesforce Marketing Cloud Next (MCN)

    Salesforce Marketing Cloud Next (MCN) is a platform built on Data 360 (formerly Data Cloud) and Agentforce Marketing. Key configuration areas include IP Warming & Deliverability and Business Units.

  4. Overview of MCE MobileConnect Data Views

    MobileConnect Data Views are built-in backend tables in Marketing Cloud Engagement (MCE) used for SMS data analysis. They can be used in SQL queries for reporting or to populate Data Extensions. Most are read-only, with the exception of _MobileAddress, which can be updated via SSJS. Data is scoped to the specific Business Unit.

    Key characteristics include:

    • Data Scope: Covers all numbers on private short/long codes, but only opted-in numbers on shared short codes.
    • Data Types: Some views log events (e.g., _SMSMessageTracking, _SMSSubscriptionLog), while others show the current state of a mobile number (e.g., _MobileAddress, _UndeliverableSMS).
    • Retention: _SMSSubscriptionLog retains more than 6 months of data. _SMSMessageTracking has no guaranteed retention; data should be copied to a separate Data Extension via scheduled Automation or exported to an external warehouse to avoid loss and performance degradation.

  5. Overview of Marketing Cloud Engagement Code Resources

    Code Resources in Salesforce Marketing Cloud Engagement (MCE) allow for hosting JavaScript, CSS, JSON, RSS, Text, and XML files. They differ from other MCE tools in several key ways:

    • Dedicated URL: Unlike Content Blocks, they have their own URL and can be linked or called via API as standalone web assets.
    • No HTML Overhead: Unlike Cloud Pages, MCE does not automatically append HTML structures, providing full control over the output.
    • Programmatic Backend: They support SSJS and AMPScript, allowing for dynamic content generation based on MCE data or external API calls.
    • Cost Effective: There is no cost associated with loading a Code Resource, unlike Cloud Pages.

  6. Overview of the Salesforce Marketing product landscape

    The Salesforce Marketing ecosystem is divided into several solution areas:

    • Salesforce Marketing: The umbrella term for all marketing products, including automation, real-time personalization, intelligence, and loyalty management.
    • Agentforce Marketing: Products leveraging Agentforce AI capabilities. This includes the new Marketing Cloud product and 'Plus' versions of legacy products (Marketing Cloud Engagement Plus and Marketing Cloud Account Engagement Plus).
    • Marketing Cloud Next (MCN): A suite of new marketing products (automation, personalization, and intelligence) built natively on the Data 360 (formerly Data Cloud) platform.
    • Cross-Solution Platforms: Agentforce 360 and Data 360 serve as platform-wide technology supporting all other marketing products.

  7. Overview of MCP Serverside Code

    Serverside Code is the core engine of Marketing Cloud Personalization (MCP, formerly Interaction Studio) used in Web, Serverside, and Triggered Campaign Templates. It is written in TypeScript (or modern JavaScript) and is responsible for delivering data-driven personalizations by leveraging user profiles, behavioral data, company catalog data, and Einstein Recipe recommendations to produce a final personalization payload.

  8. Overview of available Serverside Code Decorators

    MCP Serverside Code uses TypeScript decorators to extend property behavior in the UI:

    • @options: Provides picklist values for strings, numbers, or complex properties.
    • @richText: Changes string input to a rich text editor.
    • @unit: Adds context/units to numeric inputs.
    • @range: Converts a date selection into a range selection.
    • @tabular: Changes the layout of complex objects.
    • @title / @subtitle: Customizes property labels and helper text.
    • @markdown: Adds rich text documentation.
    • @header / @headerSubtitle: Adds section-level headings.
    • @hidden / @shownIf: Controls input visibility.
    • @buttonGroup: Converts picklists to buttons.

  9. Overview of Marketing Cloud Connect (MCC)

    Marketing Cloud Connect (MCC) is an out-of-the-box integration suite that connects Salesforce CRM (Sales, Service, Health Cloud, etc.) with Marketing Cloud Engagement (MCE). It enables data synchronization, dedicated Journey Builder activities, AMPScript functions, and reporting feedback loops without requiring custom API calls that consume license limits.

  10. Overview of MCE Behavioral Triggers

    Behavioral Triggers automate win-back communications by pushing abandoned engagement data from an e-commerce site to the Einstein Backend and subsequently to MCE Data Extensions. This allows developers to trigger Customer Journeys and personalize emails using the Behavioral Trigger Content Block.

    Supported scenarios include:

    1. Abandoned Cart
    2. Abandoned Browse
    3. Abandoned Wishlist

  11. Analyze email opens using the _Open Data View

    The _Open Data View stores information about email open events, allowing you to track when and where a subscriber opened an email.
    SELECT
      opened.AccountID
    , opened.OYBAccountID
    , opened.JobID
    , opened.ListID
    , opened.BatchID
    , opened.SubscriberID
    , opened.SubscriberKey
    , opened.EventDate
    , opened.Domain
    , opened.IsUnique
    , opened.TriggererSendDefinitionObjectID
    , opened.TriggeredSendCustomerKey
FROM _Open AS opened

  12. Configure Einstein AI model training and data privacy

    The BU architecture determines how Einstein models are trained:

    • Single BU: A single configuration runs the model against the entire database. This is simpler to set up but does not allow for tuning by team, region, or other structures.
    • Multiple BUs: Each BU gets its own independently trained model per feature, tuned specifically to that BU's data. This requires more configuration as the model must be enabled for each BU.

    Data Privacy & Global Data Model:

    • Einstein Send Time Optimization (STO): By default, STO uses a global model trained on anonymized, aggregated data from multiple customers. To prevent your data from contributing to the global model (and lose access to pre-trained data), disable the Global Data Model toggle.
    • Engagement Frequency and Engagement Scoring: These features train only on the specific customer's data and do not require the Global Data Model toggle.