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| Section | Objectives |
|---|---|
| Cross-Channel Journey Design | - Journey Builder configuration
|
| Marketing Cloud Core Architecture | - Salesforce Marketing Cloud ecosystem components
|
| Automation and Optimization | - Automation Studio workflows
|
| Email and Messaging Execution | - Multi-channel messaging
|
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NEW QUESTION # 98
What is prerequisite for email/web recommendations: select 2
Answer: A,B
Explanation:
Understanding Email/Web Recommendations:
* Purpose:Recommendations (in email or on a website) use data about customer behavior and product information to suggest products or content that a customer is likely to be interested in.
Prerequisites:
* A. Collect Tracking Code:This is crucial. The Collect Tracking Code is a JavaScript snippet that you place on your website. It tracks user behavior (page views, product views, purchases, etc.) and sends this data to Marketing Cloud. This behavioral data is essential for generating personalized recommendations.
* B. Catalog:A catalog is a structured file (usually CSV or XML) that contains information about your products or content (e.g., product ID, name, description, price, image URL). The catalog is imported into Marketing Cloud and provides the inventory from which recommendations are drawn.
Why A and B Are Essential:
* Data-Driven Recommendations:Recommendations rely on data. The Collect Tracking Code provides behavioral data, and the catalog provides product data.
* Personalization:Without these data sources, recommendations would be generic and not personalized to individual customers.
NEW QUESTION # 99
how many activities recommended in a journey canvas.(150-200)
Answer: C
NEW QUESTION # 100
Which two concepts are requirements that reed to be implemented for Einstein web and/or email recommendations in Marketing Cloud?
Choose 2 answers
Answer: D,E
NEW QUESTION # 101
What is true about Einstein engagement frequency (EEF). Select 3
Answer: A,B,D
Explanation:
Understanding Einstein Engagement Frequency (EEF):
Purpose: EEF is a Marketing Cloud Einstein feature that helps optimize email send frequency for each subscriber. It aims to reduce unsubscribes and improve engagement by sending the right number of emails to each person.
How it Works: EEF analyzes historical engagement data (opens, clicks, etc.) to predict the optimal send frequency for each subscriber.
Correct Statements:
A: EEF uses only commercial send data not transactional: EEF focuses on optimizing marketing email frequency. Transactional emails (order confirmations, password resets) are typically triggered by specific events and are not part of EEF's optimization.
B: The model uses data available only in your account: EEF's predictions are based on the engagement data within your specific Marketing Cloud account. It doesn't use data from other accounts or external sources.
C: Used to determine the correct no of mails to be sent so that there are fewer unsubscribes due to email fatigue: This is the core purpose of EEF. It aims to find the "sweet spot" for each subscriber - enough emails to keep them engaged, but not so many that they unsubscribe due to email fatigue.
Incorrect Statement:
D: It uses the last 90 days of engagement data: EEF uses the last 28 days of engagement data, not 90.
In Summary:
EEF uses your account's commercial email send data from the last 28 days to determine the optimal email frequency for each subscriber, reducing unsubscribes. A, B, and C are correct.
NEW QUESTION # 102
Which keywords does MobileConnect automatically recognize as STOP keywords?
Answer: C
NEW QUESTION # 103
......
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