How to Maintain Data Cleansing in Salesforce Marketing Cloud (SFMC)

Maintaining data cleanliness in SFMC (Salesforce Marketing Cloud) is critical for improving deliverability, reducing costs, and enhancing personalization. My approach to data cleansing in SFMC involves a multi-step strategy, using SQL Query Activities, Data Filters, Automations, and AMPscript where needed.

1. Data Cleaning Strategies in SFMC

A. Removing Duplicates

SFMC does not enforce unique keys, so it's important to remove duplicate records.

? Method: SQL Query to Deduplicate Data Use SQL Query Activity in Automation Studio to retain only the latest unique record:

SELECT EmailAddress, First_Name, Last_Name, MAX(LastModifiedDate) AS LatestUpdate  
FROM [Subscribers]  
GROUP BY EmailAddress, First_Name, Last_Name
        

?? Why? Ensures only the latest entry is retained per email address.

? Alternative: Data Filters

• Use Data Filters in SFMC to remove duplicates by setting criteria manually.
• Create a filtered Data Extension with unique records.

B. Handling Inactive & Invalid Contacts

Regularly removing bounced, unsubscribed, and inactive contacts helps improve email deliverability.

? Method: Querying Bounced & Unsubscribed Contacts

SELECT EmailAddress  
FROM _Bounce  
WHERE BounceType IN ('Hard bounce', 'Blocked')
        

? Method: Suppressing Unengaged Users Identify users who haven’t opened or clicked in the last 6 months:

SELECT EmailAddress  
FROM _Subscribers  
WHERE Status = 'Unsubscribed' OR EmailAddress IN  
(  
    SELECT DISTINCT EmailAddress  
    FROM _Sent  
    WHERE EventDate < DATEADD(MONTH, -6, GETDATE())  
)
        

?? Why? Helps remove unresponsive contacts and avoid being marked as spam.

? Method: Using Auto-Suppression Lists

• Configure Auto-Suppression Lists in Email Studio.
• Add hard bounces, unsubscribed, or complaint emails to prevent sending to bad addresses.

C. Standardizing Data Formats

Bad data formatting leads to segmentation issues. Use SQL queries to clean common data errors:

? Normalize Email Addresses (Lowercase & Trim)

SELECT LOWER(TRIM(EmailAddress)) AS EmailAddress  
FROM [Subscribers]
        

? Fix Country Code Issues Standardize country codes (e.g., US instead of United States):

SELECT  
    CASE  
        WHEN Country IN ('United States', 'USA', 'US') THEN 'US'  
        WHEN Country IN ('United Kingdom', 'UK') THEN 'UK'  
        ELSE Country  
    END AS Country  
FROM [Customers]
        

? Enforce Proper Casing (First & Last Names)

SELECT UPPER(LEFT(First_Name,1)) + LOWER(SUBSTRING(First_Name,2,LEN(First_Name))) AS First_Name  
FROM [Customers]
        

?? Why? Ensures consistency in personalization when using AMPscript in emails.

D. Managing Expired & Unused Data

Over time, Data Extensions (DEs) fill up with outdated records.

? Use Retention Policies

• Set Data Retention Policies when creating Data Extensions to automatically delete old records.
• Example: Keep data for 180 days, then auto-delete expired records.

? Schedule Cleanup Automations

• Run a scheduled SQL Query Automation to remove old records every month.
• Example: Delete data older than 1 year: DELETE FROM [Transactional_Logs] WHERE EventDate < DATEADD(YEAR, -1, GETDATE())

E. Validating & Enriching Data

Ensuring accurate and complete data helps with better segmentation and personalization.

? Use Data Validation Checks

• Check for missing emails: SELECT * FROM [Contacts] WHERE EmailAddress IS NULL
• Check for incorrect phone formats: SELECT * FROM [Leads] WHERE LEN(Phone_Number) < 10

? Enrich Data Using External APIs

• Use APIs to validate email addresses (e.g., ZeroBounce, NeverBounce).
• Append missing city, state, ZIP code using third-party data sources.

2. Automation Studio for Ongoing Data Cleansing

SFMC Automation Studio can automate regular data cleanup tasks:

A. Data Cleansing Automation Workflow

1?? SQL Query Activity – Identify duplicates, expired data, and invalid emails.

2?? Filter Activity – Segment unengaged subscribers.

3?? Script Activity (AMPscript) – Format and clean data.

4?? Data Extract & Delete Activity – Remove unwanted data.

5?? Notification Activity – Alert admins when cleanup is complete.

?? Best Practices for Data Cleansing in SFMC

? Automate Data Cleaning – Use Automation Studio to schedule regular cleanups.

? Use SQL Queries for Cleansing – Deduplicate, remove bounces, and standardize formats.

? Implement Data Retention Policies – Auto-delete old data from Data Extensions.

? Validate & Enrich Data – Ensure accurate email/phone details using external APIs.

? Use Suppression Lists – Prevent sending to bounced, unsubscribed, or spam-complaint contacts.