For Single Page Applications (SPAs), it is important to capture the URL changes correctly. The

s.pageURL variable should be mapped to dataLayer.url to ensure that Adobe Analytics captures the

correct URL each time the state changes in the SPA. This allows accurate tracking of user navigation

within the SPA framework.

Reference: Adobe Analytics SPA Implementation

To add a new enterprise user via the Adobe Admin Console for enterprises, the Architect should

follow these steps:

Input the user's email address: This is the primary identifier for the user in the Adobe Admin

Console.

Assign a product profile: Product profiles determine the level of access and permissions the user will

have within the specific Adobe products.

Assign the user to a user group: User groups help in managing users by grouping them based on their

roles or responsibilities, simplifying the process of assigning permissions and product profiles.

This method ensures that the user has the correct access and can utilize the Adobe products as

intended.

Reference: Adobe Admin Console User Management Guide

Business Requirement: Extract data for weekly updates to a Tableau Dashboard from Adobe

Analytics.

Recommended Method:

Deliver data via FTP through Data Warehouse: Provides a robust and automated method for

exporting large datasets on a scheduled basis.

Explanation:

Data Warehouse: Allows for the extraction of large and complex datasets suitable for integration with

external BI tools like Tableau.

FTP Delivery: Automates the data export process, ensuring timely updates to the Tableau Dashboard.

Verification: According to Adobe Analytics Data Warehouse documentation, using FTP delivery

through Data Warehouse is the preferred method for exporting large datasets for external use

(Adobe Analytics Data Warehouse Guide).

The issue in the original code lies in the incorrect placement of the event syntax. In Adobe Analytics,

when defining events in the product string, the correct format must be followed to ensure the event

is captured properly. Specifically, there needs to be a double semicolon (;;) before the event

definition.

The corrected code s.products = ";car stereo;1 ;820;;event100=23" ensures that the event is

recognized correctly by Adobe Analytics.

Reference: Adobe Analytics Product Variable 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.

Reference: Adobe Analytics VISTA Rule Guide.

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.

Reference: Adobe Analytics Implementation Documentation on setting variable values.

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.

Reference: Adobe Analytics Documentation on Dynamic Variable Substitution.

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.

Reference: Adobe Analytics Processing Rules Guide.

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.

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.

Segment IQ in Adobe Analytics is used to compare different segments of traffic to determine

the statistical significance of differences between metrics. A high differential score of 0.9 or greater

indicates a high statistical significance difference between the metrics of the two segments. This

means that the differences observed in metrics like Page Views/Visitors, Searches/Visitors,

Carts/Visitors, Cart Additions/Visitors, and Revenue/Visitors are not due to random chance but are

significant and meaningful.

Reference: Adobe Analytics Segment IQ

Business Requirement: Analyze all internal search terms, including those falling into the "(low

traffic)" category.

Extraction Methods:

Data Warehouse: Provides comprehensive data extraction capabilities, allowing for the retrieval of

detailed and granular data that might not be fully visible in standard reports.

Data Feeds: Offers raw data extraction capabilities, enabling the capture of all search terms without

the aggregation and sampling that can occur in standard reports.

Explanation:

Data Warehouse: Can export detailed data, bypassing the "(low traffic)" limitation by accessing the

raw, unsampled data.

Data Feeds: Provides a way to extract raw data directly from Adobe Analytics, ensuring that all search

terms, including those with low traffic, are included.

Verification: According to Adobe Analytics documentation, using Data Warehouse and Data Feeds are

recommended methods for extracting comprehensive datasets, including detailed search terms

(Adobe Analytics Data Warehouse Guide, Data Feeds Documentation).

Standard metrics in Adobe Analytics are those that come pre-configured within the platform and are

commonly used across various types of implementations. These include:

Purchase: Tracks the completion of a purchase transaction.

Orders: Records the number of orders placed.

These metrics are essential for eCommerce tracking and are part of the standard set of metrics

available in Adobe Analytics.

Reference: Adobe Analytics Standard Metrics

To capture and analyze the contribution of news sections to conversions, the following steps should

be taken:

Assign a custom eVar to capture the news section value: This allows for tracking which news sections

users engage with.

Enable linear attribution to the eVar: Linear attribution ensures that all news sections engaged with

during a session are credited proportionally for any conversions, providing a more balanced view of

their contribution.

Expire the eVar after the visit: This ensures that the attribution is relevant to the current session and

does not carry over to future sessions, maintaining accuracy in conversion analysis.

This approach provides a clear and comprehensive understanding of how different news sections

contribute to conversions.

Reference: Adobe Analytics Attribution Models 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).