Data Views in Salesforce Marketing Cloud

With the proliferation of digital channels, marketers have access to more data than ever, but this data is only as valuable as the insights it provides. This is where Salesforce Marketing Cloud (SFMC) comes into play. SFMC is a powerful platform that offers many tools for managing customer relationships, automating marketing campaigns, and analyzing data. 

One of the most potent features of SFMC is Data Views—a behind-the-scenes data structure that provides marketers with an invaluable resource for tracking and analyzing customer interactions.

According to a study, organizations that embrace a data-driven approach can outperform their competitors by 6% in profitability and 5% in productivity. Given these statistics, understanding and utilizing Data Views within Salesforce Marketing Cloud can significantly elevate your marketing strategy, allowing for more precise targeting, better customer experiences, and, ultimately, higher ROI.

What are Data Views in Salesforce Marketing Cloud?

Data Views in Salesforce Marketing Cloud are system-generated tables that store tracking data related to your marketing activities. These views are not visible through the standard interface but are accessible through SQL queries within Automation Studio or Query Activities. Each Data View table contains information about a specific type of activity, such as email sends, opens, clicks, and bounces.

For marketers and data analysts, Data Views provide a treasure trove of information that can be used to analyze campaign performance, segment audiences, and refine targeting strategies. Unlike data extensions, which are user-defined and can be customized to store any type of data, Data Views are predefined by Salesforce. They are automatically populated with data based on your contacts’ interactions with your marketing efforts.

Data Retention

One important aspect to consider when working with Data Views is data retention. Salesforce Marketing Cloud retains data in these views for a limited period, which varies depending on the type of Data View. For example, email tracking data is typically retained for 180 days, while other data might be retained for shorter or longer periods. Understanding these retention policies is crucial for planning your data analysis and reporting strategies, as historical data older than the retention period will be automatically purged.

To ensure you don’t lose valuable data, export it to a data extension or an external database for long-term storage. Automation Studio can be configured to regularly extract data from these views and store it elsewhere, giving you access to a more extensive history of your marketing activities.

Usage

The primary use of Data Views is to provide marketers and analysts with detailed insights into the performance of their campaigns. By querying these views, you can generate custom reports beyond the standard out-of-the-box reports provided by Salesforce Marketing Cloud. For example, you can analyze how different segments of your audience respond to various types of content, identify the best times to send emails based on historical open rates or track the performance of automated journeys.

Additionally, Data Views can be used to create complex audience segments based on past behavior. For instance, you can make a segment of users who have opened an email in the past 30 days but have yet to click on any links. This allows for more targeted follow-up campaigns, significantly improving engagement and conversion rates.

Examples of Data Views

Here are some examples of commonly used Data Views in Salesforce Marketing Cloud:

1. Sent Data View: This view contains information about emails sent, including the subscriber key, send time, and job ID.
2. Open Data View: Tracks email opens, providing details such as opening time, subscriber key, and job ID.
3. Click Data View: Records clicks on links within emails, including the URL clicked, subscriber key, and job ID.
4. Bounce Data View: This view contains information about emails that were not delivered, including the bounce reason, subscriber key, and job ID.
5. Unsubscribe Data View: Tracks unsubscribes, including the time of unsubscribe, subscriber key, and job ID.

Each of these views provides specific insights that can be used to fine-tune your marketing efforts. For example, analyzing the Bounce Data View can help you identify and address issues with email deliverability. In contrast, the Click Data View can help you understand which content resonates most with your audience.

Also Read – Salesforce Marketing Cloud Certification Path

How Can I Enable Data Views in My Salesforce Marketing Cloud Account?

Data Views are a built-in feature of Salesforce Marketing Cloud and do not require any special setup to enable. However, to access and use Data Views, you must have a basic understanding of SQL (Structured Query Language). To extract data from these views, you can create SQL queries within the Query Activity in Automation Studio.

Here’s a simple step-by-step guide to accessing Data Views:

1. Navigate to Automation Studio: Log in to Salesforce Marketing Cloud and navigate to Automation Studio.
2. Create a New Query Activity: Select “Activities” and click “Create New Query Activity.”

Write Your SQL Query: Use SQL to select data from the desired Data View. For example, to retrieve all email opens, you could use a query like:

SELECT * 

FROM _Open

WHERE EventDate >= GETDATE() – 30

1. Save and Run the Query: Save your query and either run it immediately or schedule it to run at regular intervals.

Once the query is executed, the results can be stored in a data extension, which can be further analyzed or exported.

What are the Main Differences Between Data Views and Data Extensions?

While both Data Views and data extensions store data in Salesforce Marketing Cloud, they serve different purposes and have some key differences:

1. Predefined vs. Custom: Data Views are predefined system tables that automatically capture tracking data, while data extensions are user-defined tables that can store any type of data.
2. Data Sources: Data Views primarily store data related to email interactions, such as sends, opens, and clicks. Data extensions can store various data, including customer attributes, preferences, and transactional information.
3. Data Access: Data Views are accessed through SQL queries in Automation Studio, while data extensions can be managed directly through the SFMC interface.
4. Data Retention: Data retention in Data Views is limited, while data in data extensions can be retained indefinitely as long as your account is active.

Understanding these differences is crucial for effective data management within the Salesforce Marketing Cloud. While Data Views are excellent for analyzing specific types of interaction data, data extensions offer greater flexibility for custom data storage and management.

Can Data Views Be Used for Real-Time Data Analysis?

Data Views in Salesforce Marketing Cloud are not designed for real-time data analysis. The data in these views is typically updated within minutes to an hour after the interaction occurs, making it suitable for near-real-time analysis rather than for scenarios that require instantaneous data processing.

For marketers who need real-time insights, other tools within Salesforce Marketing Cloud, such as Journey Builder and Einstein Analytics, might be more appropriate. These tools can provide immediate customer interaction feedback and enable real-time decision-making.

How Do Data Views Integrate with Automation Studio?

Automation Studio in Salesforce Marketing Cloud is a powerful tool for automating various marketing tasks, including data extraction, segmentation, and reporting. Data Views can be seamlessly integrated with Automation Studio through Query Activities.

Here’s how you can leverage Data Views within Automation Studio:

1. Data Extraction: Use Query Activities to extract specific data from Data Views and store it in data extensions for further analysis or reporting.
2. Audience Segmentation: Combine data from different Data Views to create dynamic segments based on customer behavior. For example, you could make a segment of users who clicked on a specific link but did not convert.
3. Automated Reporting: Set up scheduled Query Activities that automatically run at specific intervals, providing you with regular updates on campaign performance.

Integrating Data Views and Automation Studio allows for sophisticated data-driven marketing automation, helping you optimize your campaigns and improve ROI.

What Types of Data Are Typically Stored in Data Views?

Data Views in Salesforce Marketing Cloud store various data types, primarily related to email and other digital marketing interactions. Common types of data stored in Data Views include:

1. Interaction Data: Includes email sends, opens, clicks, bounces, and unsubscribes.
2. Subscriber Data: Contains information about the subscriber, such as the subscriber key and the date they were added to a list.
3. Campaign Data: Stores data related to specific marketing campaigns, such as job ID and send classification.
4. Behavioral Data: Tracks user behavior within emails, such as link clicks and conversion events.

This data is invaluable for understanding how your audience interacts with your content and optimizing your marketing strategies accordingly.

Conclusion

Data Views in Salesforce Marketing Cloud are a powerful feature that provides deep insights into your marketing activities. By leveraging these system-generated tables, you can track and analyze customer interactions, optimize your campaigns, and ultimately drive better results. Whether you want to improve email deliverability, create targeted segments, or automate reporting, Data Views offers the data and flexibility you need to succeed.

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FAQs

1. How long is data retained in Data Views?

Data retention in Data Views varies, with most email tracking data retained for 180 days.

2. How do I access Data Views in Salesforce Marketing Cloud?

Data Views can be accessed using SQL queries within Automation Studio’s Query Activity. You can write SQL queries to retrieve and analyze data from these views.

3. What’s the difference between Data Views and data extensions?

Data Views are predefined system tables that store interaction data, while data extensions are user-defined tables that can store any data type. Data Views are primarily used for tracking purposes, whereas data extensions offer more flexibility for custom data storage.

4. Can Data Views be used for real-time analytics?

Data Views are not designed for real-time analytics. They provide near-real-time data with updates within minutes to an hour after an interaction.

5. Can data be exported from Data Views for long-term storage?

You can use SQL queries in Automation Studio to extract data from Data Views and export it to data extensions or external databases for long-term storage.