AD0-E207 Adobe Analytics Architect Master Questions and Answers

 Business Requirement: The company wants to track shares of blog posts to social networks and report on the total number of shares and the specific networks used.

 Variables and Metrics Needed:

eVar for "Blog Post Title": Captures the title of the blog post being shared.

eVar for "Blog Share Social Network": Captures the social network to which the post is shared.

Event for "Blog Shares": Captures the number of times a post is shared.

 Explanation:

eVar for "Blog Post Title": This variable allows reporting on the specific blog posts being shared.

eVar for "Blog Share Social Network": This variable tracks which social networks the posts are shared to, enabling detailed reporting.

Event for "Blog Shares": This metric captures the total number of shares, providing a quantifiable measure of sharing activity.

 Verification: According to Adobe Analytics best practices for tracking social sharing, using specific eVars for post titles and social networks along with an event for total shares ensures comprehensive reporting (Adobe Analytics Implementation Guide).

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.

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.

 Business Requirement: The company wants to track successful and unsuccessful form submissions and identify the types of forms being submitted.

 Variables and Metrics Needed:

Event for "Forms Submitted with Errors": Captures instances where forms are submitted but contain errors.

eVar for "Form Type": Identifies the type of form being submitted, which is crucial for analyzing different form performance.

Event for "Forms Submitted Successfully": Captures instances where forms are successfully submitted.

 Explanation:

Event for "Forms Submitted with Errors": Essential for understanding and improving form completion processes by identifying where users encounter issues.

eVar for "Form Type": Provides detailed breakdowns of form performance by type, aiding in targeted improvements.

Event for "Forms Submitted Successfully": Measures the completion rate of forms, which is critical for business metrics.

 Verification: According to Adobe Analytics best practices, using specific events for tracking successful and error submissions alongside eVars for categorization provides comprehensive insights (Adobe Analytics Implementation Guide).

To meet the specified business requirements, the query string parameters in the network request must include:

events=event1,event2: This captures both newsletter sign-up attempts (event1) and successful sign-ups (event2).

vl=architectsite.com: This identifies the URL.

pageName=homepage: This tracks the user journey through the site by page name.

ch=home: This captures the site section where the successful newsletter sign-ups occur.

The combination of these parameters ensures that all the required data points are collected and reported accurately.

 Business Requirement: Track the login status of visitors on a secure website.

 Potential Login Statuses:

"Unregistered / Logged Out": Tracks users who have not registered or have logged out.

"Anonymous": Generally used for visitors who browse without logging in, but does not apply in a secure login-required context.

"Registered / Logged Out": Important for tracking users who have accounts but are currently logged out.

"Registered / Logged In": Essential for tracking active, logged-in users.

"Incomplete Registration": Useful for identifying users who started but did not complete the registration process.

"Unregistered / Logged In": Typically not applicable as unregistered users cannot be logged in.

 Explanation:

"Unregistered / Logged Out": Helps in understanding the behavior of users who visit but have not completed registration or logged out.

"Registered / Logged In": Critical for identifying and analyzing the behavior of active users.

"Incomplete Registration": Helps in identifying and addressing barriers in the registration process.

 Verification: According to Adobe Analytics best practices, capturing various login statuses helps in comprehensive user behavior analysis (Adobe Analytics User Guide).

 Business Requirement: Use dynamic variables to collect the current page URL in eVar32.

 Method:

Dynamic Variable Syntax:D=cpreferences the current page URL.

 Explanation:

s.eVar32="D=cp": This syntax tells Adobe Analytics to dynamically populate eVar32 with the current page URL.

 Verification: According to Adobe Analytics dynamic variables documentation, usingD=cpcorrectly references and captures the current page URL in eVars (Adobe Analytics Dynamic Variables Guide).

When merchandising is enabled in Adobe Analytics, revenue attribution can vary based on the allocation method:

Linear Allocation: Distributes revenue equally across all instances of the product view and add-to-cart events. Thus, $100 would be attributed to bothmerch_category1andmerch_category2.

Most Recent Allocation: Attributes the revenue to the most recent instance of the variable. Thus, $100 would be attributed tomerch_category2.

This approach ensures that revenue attribution accurately reflects user interactions with products.

 Business Requirement: Disable Activity Map to prevent collecting Personal Identifiable Information (PII) in links.

 Method:

s.trackInlineStats: Setting this to false disables Activity Map tracking.

 Explanation:

s.trackInlineStats to false: This setting stops Activity Map from tracking link clicks and capturing PII in links.

 Verification: According to Adobe Analytics Activity Map documentation, disabling Activity Map by settings.trackInlineStatsto false prevents the collection of potentially sensitive data (Adobe Activity Map Documentation).

Overview of the Issue: The hourly unique visitor report being 3 hours behind indicates a delay in data processing within Adobe Analytics.

Potential Causes: The delay could be due to several factors such as increased data volume, server performance issues, or unexpected traffic spikes.

Explanation:

Increased unique variable values: This could slow down processing, but it typically affects data collection rather than causing such a significant delay.

Increased number of users running reports: This might slow down the user interface and report generation, but not data processing itself.

New variables enabled for report suite: This usually affects the data collection stage and can cause delays but would not typically result in a consistent 3-hour lag.

An unexpected traffic spike: A sudden increase in traffic can overload data processing servers, causing delays in reporting as the system tries to catch up with the increased data volume.

Verification: According to Adobe's documentation, data processing delays are often caused by unexpected traffic spikes that increase the volume of data beyond typical processing capacity (Adobe Analytics Documentation).

 Business Requirement: Report on the monetary value of a visitor's cart at the checkout page without using product IDs.

 Variable Configuration:

s.events: Captures the checkout event and the monetary value.

s.products: Lists the product names and their corresponding values.

 Explanation:

s.events = "scCheckout,event1": This sets the event to capture the checkout action and records the event value.

s.products = ";Novelty Mug;;;event1=8.00,;Coffee Beans;;;event1=10.00": This format lists the products by name and assigns their respective monetary values.

 Verification: According to Adobe Analytics product string documentation, the format used correctly attributes product names and values to the checkout event (Adobe Analytics Implementation Guide).

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.