Naveen

  1. Welcome Email 📌 Header Image: A warm, inviting banner with a "Welcome!" message. Subject: Welcome to [Your Brand] – Let’s Get Started! Message: Dear [First Name], Welcome to [Your Brand]! We’re thrilled to have you with us. Get ready for exclusive updates, special offers, and valuable content. [Start Exploring] (CTA Button) Best, [Your Brand] Team First, we have uploaded a welcome image. Then, we have updated the Message content Then, we have created a button to explore the amazon shopping world.

1. ONE-TO-ONE (1:1) Person-Passport 2. ONE-TO-MANY (1:M)  3. MANY-TO-MANY (M: M) 

                                Random Data Extensions: Used to randomly split data from a source data extension, often for A/B testing. Use Case: 1. A/B Testing Campaigns Scenario : A retail company wants to test two versions of an email to determine which performs better. Solution : Use a random data extension to split the audience into two equal groups for testing. Field Name Data Type Length Required Primary Key CustomerID Text 50 Yes Yes FullName Text 100 Yes No EmailAddress EmailAddress N/A Yes No TestGroup Text 10 Yes No CustomerID FullName EmailAddress TestGroup C001 John Doe john.doe@example.com A C002 Jane Smith jane.smith@example.com B Example1 We have a Data Extension, which has 8 records, We have to split these records in 2 Random Data Extension (50-50% Each) Example-2 We have a Data Extension, which has 8 records, We have to split these records in 4 Random Data Extension (25-25...

Filtered Data Extensions: - https://www.loom.com/share/d9b7ca63c2af4dd5b0137e557fcd306a Generated by applying filters to existing data extensions; they update dynamically based on predefined criteria. USE CASES 1. High-Value Customers Segmentation Scenario : A retail company wants to target customers who have spent more than $500 in the last six months. Solution : Use a filtered data extension to segment customers from a standard DE based on purchase amount and date criteria. Field Name Data Type Length Required Primary Key CustomerID Text 50 Yes Yes FullName Text 100 Yes No EmailAddress EmailAddress N/A Yes No PurchaseAmount Decimal N/A Yes No LastPurchaseDate Date N/A Yes No CustomerID FullName EmailAddress PurchaseAmount LastPurchaseDate CustomerID FullName EmailAddress PurchaseAmount LastPurchaseDate C001 John Doe john.doe@example.com 550.00 2024-12-15 C003 Alice Johnson alice.johnson@example.com 750.00 2024-11-20 Initially created a table for 4 records. After applying the filter...