Building an Optimized Data Model and Maximizing Automation Studio in Salesforce Marketing Cloud

Last Updated on November 24, 2024

Data and automation are two pillars that define the effectiveness of Salesforce Marketing Cloud (SFMC). A well-optimized data model provides the foundation for efficient segmentation, personalization, and reporting, while automation ensures these processes scale effortlessly as your campaigns and datasets grow. However, many SFMC users face challenges when working with synchronized data extensions, juggling complex queries, and managing multiple automation workflows. The result? Slow performance, missed opportunities, and campaigns that fail to meet their potential.

This article dives into strategies for building a streamlined, flattened data model, complemented by best practices for leveraging Automation Studio and optimizing SQL Query Activities. Together, these approaches can transform your SFMC implementation into a well-oiled machine capable of meeting the demands of modern marketing.

The Case for Data Model Optimization

In SFMC, data often starts in synchronized data extensions, which reflect the normalized structure of Sales or Service Cloud. While this is a logical design for a CRM, it presents challenges when managing large-scale marketing activities. Queries become more complex, processing time increases, and creating meaningful segments requires extensive joins and transformations.

Flattening a data model—consolidating key fields from various objects into a single, actionable data extension—addresses these issues. This structure offers simplicity, speed, and scalability, making it ideal for use cases like personalized campaigns or automation-driven workflows.

For instance, a retail marketer might flatten data from standard Salesforce objects like Contact, Opportunity, and Account into a unified view that includes subscriber information, sales activity, and account details. Such a structure allows for faster segmentation and real-time decision-making.

Building a Flattened Data Model

Creating a flattened data model begins with a clear understanding of your objectives. Define the fields you need, such as demographics, purchase history, or engagement metrics, based on the campaigns you plan to run. Use SQL queries to combine relevant data from synchronized data extensions.

Here is an example SQL query using standard Salesforce objects and fields:

SELECT 
    c.ContactID AS SubscriberKey,
    c.FirstName,
    c.LastName,
    c.Email,
    a.AccountName,
    o.LastModifiedDate AS LastOpportunityDate,
    o.Amount AS TotalOpportunityValue
FROM 
    Contact_Salesforce c
LEFT JOIN 
    Opportunity_Salesforce o ON c.ContactID = o.ContactID
LEFT JOIN 
    Account_Salesforce a ON c.AccountID = a.AccountID
WHERE 
    c.IsActive = 1

This query combines data from Contact_Salesforce, Opportunity_Salesforce, and Account_Salesforce, resulting in a consolidated, sendable data extension with critical fields for segmentation and personalization. Automations can then refresh this data extension at regular intervals to ensure it remains accurate and up-to-date.

Optimizing SQL Query Activities

SQL Query Activities in SFMC can time out if they exceed 30 minutes, making efficiency a key priority. Best practices for optimization include:

  • Use Primary Keys: SFMC automatically indexes primary keys, sendable relationship fields, and frequently queried fields. Carefully select primary keys to reduce processing overhead.
  • Optimize Field Lengths and Data Types: Define field lengths based on actual needs (e.g., limiting state codes to two characters). Avoid data types like nvarchar(max) or varchar(max) to improve performance.
  • Limit Data Quantity: Query only the necessary data. For instance, if you need activity from the past 24 hours, avoid querying 30 days’ worth of data.
  • Employ Searchable Arguments (SARGable Queries): Use operators that allow indexes to improve query speed.
  • Stage Data: Break complex queries into smaller stages to avoid timeouts.

By implementing these practices, you ensure smoother operations and faster query execution, enabling your workflows to keep up with marketing demands.

Scaling with Automation Studio

Automation Studio is the linchpin of scalability in SFMC. It enables you to handle repetitive tasks like data synchronization, email sends, and data transformation, freeing up resources for strategy and creativity.

Linking Automation to Your Data Model is essential. For example, automations can be scheduled to run every 15 minutes, querying synchronized extensions to populate your flattened data extension. This approach bridges the gap between real-time data needs and the inherent delay in synchronized data extensions.

Use Case: Personalized Offers for High-Value Customers

Consider a retailer targeting high-value customers—those with an opportunity value exceeding $10,000 and a last modified date within the past 30 days. With a flattened data model, these criteria are pre-calculated and stored in the data extension. Automation Studio ensures this data remains current by running queries on a schedule. The result? Marketers can create and execute campaigns almost instantly.

Use Case: Enhancing Data Visibility Across Campaigns

Another challenge marketers face is ensuring consistent visibility of customer data across campaigns. A financial services company running campaigns for credit cards, loans, and savings accounts may struggle to manage segmentation without a centralized view of customer engagement and preferences.

A flattened data model addresses this issue by centralizing customer attributes like LastEmailInteraction, PreferredProductCategory, and LastApplicationStatus. Automation Studio ensures continuous updates, reflecting changes from synchronized extensions. This not only streamlines segmentation but also provides a single source of truth for campaign analysis.

Best Practices for Automation and Data Models

To maximize your efforts:

  • Schedule regular updates with Automation Studio to ensure segmentation and decisions rely on accurate, current data.
  • Monitor query and automation performance, optimizing SQL scripts where necessary.
  • Validate data accuracy with checks for duplicates and record counts.
  • Centralize data extensions for multi-business unit setups to maintain consistency.

Building an optimized data model and scaling it with Automation Studio are interdependent strategies that unlock the full potential of Salesforce Marketing Cloud. Together, they form the foundation for seamless segmentation, real-time personalization, and scalable workflows. By implementing these approaches, you can eliminate inefficiencies, improve performance, and deliver exceptional customer experiences.

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