Marketing Cloud Data Analysis: Queries & Visualizations

Introduction: Why Data Analysis Matters in Marketing Cloud

In today’s digital-first world, marketing is no longer driven by assumptions or gut feelings. It is powered by data. Salesforce Marketing Cloud (SFMC) generates vast amounts of customer data through email campaigns, journeys, mobile messaging, and integrations with CRM systems. However, raw data alone has little value unless it is analyzed, organized, and visualized in a meaningful way.

Marketing Cloud Data Analysis focuses on transforming scattered marketing data into actionable insights. SQL queries help marketers extract and prepare data, while visualizations turn numbers into stories that decision-makers can easily understand. Together, queries and visualizations form the backbone of data-driven marketing strategies in SFMC.

This blog explores how Marketing Cloud handles data, the role of SQL queries, and how visualizations help marketers measure performance, optimize campaigns, and improve customer engagement.

Understanding Marketing Cloud Data Analysis

Before diving into analysis, it is important to understand how data is structured in Marketing Cloud.

Data Extensions: The Foundation of Marketing Data

Data Extensions (DEs) are tables that store subscriber and campaign-related data in Marketing Cloud. They are similar to database tables, consisting of rows (records) and columns (fields). Data Extensions can store:

• Subscriber profile data (email, name, location)
• Behavioral data (opens, clicks, purchases)
• Journey-related data
• Custom business data from external systems

There are two main types of Data Extensions:

• Sendable Data Extensions, which are used for email sends
• Non-Sendable Data Extensions, used for reference or analytical purposes

A well-structured data model is essential for efficient querying and accurate analysis.

System Data Views

In addition to custom Data Extensions, Marketing Cloud provides Data Views, which store tracking and system-generated data. These views allow marketers to analyze campaign performance without manually storing tracking data.

Commonly used Data Views include:

• _Subscribers – subscriber status and attributes
• _Sent – email send details
• _Open – email opens
• _Click – link clicks
• _Bounce – bounced emails
• _Unsubscribe – unsubscribe activity

These views are read-only and can only be accessed through SQL queries in Automation Studio.

Role of SQL Queries in Marketing Cloud Data Analysis

SQL is the primary tool used for data analysis in Marketing Cloud. Through SQL queries, marketers can filter, join, and transform data to create meaningful datasets for targeting, reporting, and personalization.

Why SQL is Essential

Marketing Cloud does not provide advanced analytics out of the box for every use case. SQL allows you to:

• Segment audiences based on complex conditions
• Combine data from multiple Data Extensions
• Analyze engagement trends over time
• Prepare data for dashboards and reports

Even for non-technical marketers, basic SQL knowledge can unlock powerful insights.

Types of SQL Queries Used in Marketing Cloud

1. Select Queries

These queries retrieve data from one or more Data Extensions or Data Views. For example, selecting subscribers who opened an email in the last 7 days.

2. Join Queries

Join queries combine data from multiple tables. This is useful when linking subscriber data with engagement or purchase data.

3. Filtered Queries

Filtered queries narrow down records based on conditions such as date ranges, engagement status, or demographics.

4. Aggregation Queries

These queries use functions like COUNT, SUM, or AVG to generate metrics such as total opens, clicks, or conversions.

Automation Studio: Running Queries at Scale

Automation Studio is the workspace where SQL queries are created and executed. Queries can be run manually or scheduled as part of an automation.

Benefits of Using Automations

• Automatically refresh reporting data
• Maintain up-to-date segmentation lists
• Reduce manual effort and errors
• Enable near real-time decision-making

For example, a daily automation can refresh a Data Extension showing campaign performance metrics, which can then be visualized in a dashboard.

Data Preparation for Visualization

Raw data is rarely suitable for direct visualization. It often needs to be cleaned, structured, and aggregated.

Key Data Preparation Steps

• Removing duplicate records
• Standardizing date formats
• Aggregating data by day, week, or campaign
• Creating calculated fields like open rate or click-through rate

SQL queries play a critical role in preparing this analysis-ready data.

Importance of Visualizations in Marketing Cloud

While SQL queries provide numbers, visualizations provide clarity. Charts, graphs, and dashboards make it easier to understand trends, compare performance, and communicate insights to stakeholders.

Why Visualizations Matter

• Simplify complex datasets
• Highlight patterns and anomalies
• Enable faster decision-making
• Improve collaboration between marketing and leadership teams

A well-designed dashboard can replace dozens of spreadsheets.

Visualization Options for Marketing Cloud Data

Marketing Cloud does not have a built-in advanced visualization tool, but it integrates well with external platforms.

Common Visualization Tools

• Tableau – for advanced analytics and interactive dashboards
• Power BI – popular for business intelligence and reporting
• Google Looker Studio – simple and cost-effective dashboards
• Excel or Google Sheets – quick and basic reporting

Data extracted via SQL queries from Marketing Cloud can be exported or synchronized with these tools.

Key Metrics to Visualize

Effective marketing analysis focuses on the right metrics rather than overwhelming users with data.

Engagement Metrics

• Email open rate
• Click-through rate (CTR)
• Bounce rate
• Unsubscribe rate

Journey Performance Metrics

• Entry vs exit rates
• Conversion rates per journey step
• Drop-off points

Audience Metrics

• Active vs inactive subscribers
• Engagement frequency
• Lifecycle stage distribution

Visualizing these metrics over time helps marketers understand what works and what needs improvement.

Best Practices for Queries & Visualizations

Query Best Practices

• Use clear naming conventions for Data Extensions
• Avoid unnecessary fields to improve performance
• Test queries on small datasets
• Document query logic for future reference

Visualization Best Practices

• Choose the right chart type for the data
• Keep dashboards simple and focused
• Use consistent metrics and time frames
• Align visualizations with business goals

A combination of clean queries and thoughtful visuals leads to reliable insights.

Real-World Use Case Example

Imagine a marketing team running multiple email campaigns every week. Using SQL queries, they extract:

• Total sends, opens, and clicks per campaign
• Engagement trends over the last 30 days
• Subscribers who have not engaged in 90 days

This data is stored in a reporting Data Extension and visualized in a dashboard. The team quickly identifies which campaigns perform best, which audiences are disengaging, and where optimization is needed. This insight directly influences content strategy, send frequency, and audience targeting.

The Future of Data Analysis in Marketing Cloud

As marketing becomes more personalized, data analysis will play an even greater role. Automation, AI-driven insights, and predictive analytics are becoming increasingly important.

Future trends include:

• Real-time data analysis
• AI-powered engagement predictions
• Deeper CRM and data cloud integrations
• Self-service dashboards for marketers

Marketers who master queries and visualizations today will be better prepared for tomorrow’s data-driven landscape.

Conclusion

Marketing Cloud Data Analysis is not just about writing SQL queries or creating charts—it is about turning customer data into meaningful insights that drive smarter marketing decisions. Queries help structure and extract the right data, while visualizations transform that data into clear, actionable stories.

By understanding Marketing Cloud data architecture, using SQL effectively, and adopting best practices for visualization, marketers can move beyond basic reporting and truly unlock the power of their data. In a competitive digital environment, this analytical capability is no longer optional—it is essential.