Adobe AD0-E207 Exam Dumps

In Adobe Analytics, non-sequential segments allow for the inclusion of conditions that do not necessarily occur in a specific order. Since the specific product demo page and coupon page can be viewed in any order between the landing page and the order confirmation page, non-sequential containers are the appropriate choice. They allow for the creation of a segment where users must have started their visit from a campaign and completed an order, without enforcing a strict sequence for intermediate steps.

Business Requirement: Report on Facebook and Twitter activity separately in Marketing Channel reports.

Configuration Steps:

Identify Social Media Traffic: Create a rule to identify all social media traffic.

Split Facebook and Twitter Traffic: Create rules to distinguish traffic from Facebook and Twitter based on tracking codes.

Explanation:

First Rule for Social Media Traffic: This rule captures all social media traffic under a single category.

Second Rule to Split Traffic: Additional rules to distinguish between Facebook and Twitter based on specific tracking codes ('socialjb' for Facebook and 'sociaLtw' for Twitter).

Verification: According to Adobe's documentation on Marketing Channel Processing, using multiple rules to identify and then split traffic ensures detailed and accurate reporting (Adobe Analytics Marketing Channel Processing Rules Guide).

Introduction: The requirement is to use a value in both a prop (for pathing reports) and an eVar (for persistence) while minimizing the size of the server call.

Explanation of Methods:

A . A VISTA rule to copy the prop value to the eVar:

VISTA (Visitor Identification, Segmentation & Transformation Architecture) rules are server-side rules that can copy values between variables. However, they are complex, incur additional costs, and do not reduce server call size.

B . s.eVar1 = s.prop1:

Directly setting the eVar value to the prop value in the code is straightforward but does not minimize the server call size as both values are separately included in the request.

C . s.eVar1 = 'D=c1':

This method uses dynamic variable substitution, which reduces the server call size by referencing the prop value (c1) directly in the eVar without duplicating the data in the request.

Verification: Check the Adobe Analytics server call in the Network tab to confirm the reduced size.

D . A processing rule to copy the prop value to the eVar:

Processing rules can be used to copy values server-side, similar to VISTA rules but without the additional cost. However, this approach does not minimize the server call size.

Detailed Steps:

Dynamic Variable Substitution:

Set the eVar value to reference the prop value using the syntax s.eVar1 = 'D=c1'.

This tells Adobe Analytics to dynamically substitute the value of c1 (prop1) into eVar1 without sending redundant data.

Example:

s.prop1 = 'exampleValue';

s.eVar1 = 'D=c1';

Benefits:

Reduced Server Call Size: By using dynamic variable substitution, the server call payload is smaller, optimizing data transmission.

Efficient Data Handling: The value is captured once in the prop and referenced in the eVar, maintaining efficiency and persistence.

References:

Adobe Analytics Implementation Documentation: Dynamic Variable Substitution

Adobe Analytics Network Call Analysis Guide: Understanding Server Calls

By using s.eVar1 = 'D=c1', the Architect achieves the goal of collecting the value in both a prop and an eVar efficiently while minimizing the server call size.

Business Requirement: Ensure GDPR compliance for a new eVar tracking a site feature.

Additional Configuration:

Data Governance Labels: Essential for ensuring that the data collected complies with GDPR by appropriately labeling the data for privacy and security.

Explanation:

Data Governance Admin panel: Adding governance labels to the new eVar helps categorize and manage data according to GDPR compliance standards.

Verification: According to Adobe Analytics GDPR compliance documentation, adding governance labels in the Data Governance Admin panel is a critical step for ensuring data privacy and compliance (Adobe Analytics GDPR Compliance Guide).

Business Requirement: The company wants to ensure that the original search term ('Mobile') receives credit for the purchase, despite subsequent searches.

Understanding Merchandising eVars: Merchandising eVars are used to attribute success events (like purchases) to specific values captured earlier (like search terms).

Allocation and Expiration Settings:

Original Value (First) Allocation: This setting ensures that the first value captured (in this case, 'Mobile') remains attributed to the visitor, regardless of subsequent values.

Expiration Setting: Setting the expiration to the purchase event ensures that the value ('Mobile') remains active until the visitor makes a purchase.

Explanation:

Configure an eVar as Merchandising Variable: This allows tracking specific values like search terms in relation to product views and purchases.

Original Value (First) Allocation: Ensures that the initial search term ('Mobile') gets credit.

Expiration to Purchase Event: Keeps the eVar value until the purchase is made, ensuring accurate attribution.

Verification: According to Adobe Analytics documentation on Merchandising eVars, using Original Value allocation with appropriate expiration settings ensures correct attribution of original search terms to final purchases (Adobe Analytics Implementation Guide).