What is the most suitable method for Northern Trail Outfitters to compile email metrics over six months and link it to specific subscribers?

What is the most suitable method for Northern Trail Outfitters to compile email metrics over six months and link it to specific subscribers?

• A. List Filter
• B. Scheduled Automation
• C. SQL Query
• D. Standard Tracking Extract

Answer: (C)

The most suitable method for compiling email metrics over six months and linking them to specific subscribers is by using SQL Query. SQL Queries in Salesforce Marketing Cloud allow for complex data manipulation and retrieval from data extensions. They can be used to aggregate and analyze data over a specified time period, such as six months, and can join multiple data sources to bring together metrics related to both email performance and subscriber information. With SQL, Northern Trail Outfitters can efficiently filter and summarize metrics such as open rates, click-through rates, and other engagement metrics, while specifically targeting the subscribers associated with those metrics. This allows for a robust analysis of how different segments of subscribers engaged over time, enabling more precise marketing strategies. Other options may not provide the same level of granularity or capability. For instance, a List Filter is generally used for segmenting lists based on specific criteria but does not perform aggregation or analyze metrics over time. Scheduled Automation could help to automatically extract or manipulate data, but it lacks the comprehensive analytical capabilities that SQL queries offer. Finally, Standard Tracking Extract would allow for the extraction of tracking data; however, it would not facilitate the same level of custom analysis or linking to specific subscribers as SQL Queries can do. Thus, for this particular need regarding email metrics and

The most suitable method for compiling email metrics over six months and linking them to specific subscribers is by using SQL Query. SQL Queries in Salesforce Marketing Cloud allow for complex data manipulation and retrieval from data extensions. They can be used to aggregate and analyze data over a specified time period, such as six months, and can join multiple data sources to bring together metrics related to both email performance and subscriber information.

With SQL, Northern Trail Outfitters can efficiently filter and summarize metrics such as open rates, click-through rates, and other engagement metrics, while specifically targeting the subscribers associated with those metrics. This allows for a robust analysis of how different segments of subscribers engaged over time, enabling more precise marketing strategies.

Other options may not provide the same level of granularity or capability. For instance, a List Filter is generally used for segmenting lists based on specific criteria but does not perform aggregation or analyze metrics over time. Scheduled Automation could help to automatically extract or manipulate data, but it lacks the comprehensive analytical capabilities that SQL queries offer. Finally, Standard Tracking Extract would allow for the extraction of tracking data; however, it would not facilitate the same level of custom analysis or linking to specific subscribers as SQL Queries can do. Thus, for this particular need regarding email metrics and