Questions

 

Which of the following is a recommended best practice for when you run a Google Play store listing experiment?

 

• Run your experiments for at least seven days for better accuracy.
• Test three or more assets at once to understand their correlation.
• Limit your experiments to first-time installers only.
• Avoid revisiting previously tested assets once you see how they perform.

 

You can use acquisition and growth metrics from the Google Play store report to calculate which of the following?

• How many times your app was uninstalled.
• How much average revenue per app-related transaction.
• How many times your app crashed.
• How many daily users open your app.

 

Here’s why:

• Acquisition metrics focus on how users find and install your app. This can include data on the number of installs and where those installs came from (e.g., search, browsing, referrals).
• Growth metrics track user engagement and retention over time. This can include data on daily active users, retention rates, and churn.
• The number of daily users who open your app is a direct measure of user engagement and falls under growth metrics.

The other options are not directly calculated using acquisition and growth metrics:

• How many times your app was uninstalled: This is typically tracked separately as an uninstallation metric, not directly within acquisition or growth metrics.
• How much average revenue per app-related transaction: This involves revenue data, which is usually tracked under monetization metrics, not acquisition or growth metrics.
• How many times your app crashed: This is a technical performance metric, often tracked using crash reporting tools, and not directly derived from acquisition or growth metrics.

The reference to the correct answer can be found in the chapter “Track installs, uninstalls, trends, and engagement with Play Console statistics”. The chapter does discuss the importance of metrics like user acquisition and engagement throughout, which indirectly supports the idea that daily active users can be calculated using acquisition and growth metrics.

 

You want to monitor user acquisition data by country with the store analysis report. Which metric can help you find the information that you want?

• Active devices
• Compare to peers
• Average revenue per transaction
• First-time installers

 

First-time installers directly measures the number of new users who installed your app for the first time in a given period. This metric is crucial for understanding user acquisition trends and evaluating the effectiveness of your marketing campaigns or store listing optimizations in different countries.

Active devices measures the number of devices that have your app installed and have been active within a certain timeframe. While related to user acquisition, it doesn’t specifically focus on new users.

Compare to peers is a benchmarking feature that allows you to compare your app’s performance to similar apps in the same category. It doesn’t directly provide user acquisition data by country.

Average revenue per transaction is a monetization metric that focuses on the revenue generated per in-app purchase. It’s not directly related to user acquisition by country.

The reference to the correct answer can be found indirectly in the chapter “Optimize your store listing with experiments”. The chapter emphasizes the importance of understanding user acquisition and using metrics to measure app performance. This implies that first-time installers would be a relevant metric for monitoring user acquisition data by country.

 

Which of the following recommended best practices should you follow when running experiments for your app’s Google Play store listing?

• Use a maximum of five variants for each attribute that you evaluate.
• Only test for two elements and two attributes at a single time.
• Use a maximum of two elements for each attribute that you evaluate.
• Only test for one element and one attribute at a single time.

 

Here’s why:

• Isolation of Variables: Testing multiple elements or attributes simultaneously makes it difficult to determine which specific change caused the observed impact on performance. By isolating variables, you can clearly identify the cause-and-effect relationship between your changes and the experiment’s outcome.
• Clarity and Actionable Insights: Testing one element and one attribute at a time provides clear and actionable insights that allow you to make informed decisions about optimizing your store listing. You can confidently attribute changes in performance to specific modifications, enabling you to iterate and improve your listing effectively.
• Statistical Significance:  Testing multiple elements or attributes can dilute the statistical significance of your results, making it harder to draw meaningful conclusions. Focusing on a single change increases the likelihood of achieving statistically significant results.

The other options are not recommended best practices:

• Maximum of five variants: While you can test up to five variants, it’s generally recommended to start with fewer variations to maintain statistical power and avoid overwhelming users with too many choices.
• Testing two elements and two attributes: This increases complexity and makes it harder to isolate the impact of each change.
• Maximum of two elements: This limitation is unnecessary and might restrict your ability to explore different variations effectively.

The main chapter where the reference to the correct answer can be found is “Optimize your store listing with experiments”. The section on “Set up experiments” emphasizes the importance of focusing on specific elements and isolating the impact of changes when running experiments.

 

After five days of a store listing experiment, one of your variants outperforms the current version. Why should you continue to run your experiment for at least seven days even though you think that you have enough data now?

• You should run as many variants as possible before you make a decision.
• You must run the experiment for at least seven days to see a recommendation in the results column.
• You need to see an accurate reflection of weekday and weekend behavior.
• You must run the experiment for at least 7 days to see a recommendation in the metrics-used column.

 

Here’s why:

• User behavior can vary: People use apps differently on weekdays versus weekends. For example, a gaming app might see more engagement on weekends, while a productivity app might see more use during weekdays.
• Short experiments can be misleading: If you end the experiment after only five days, you might not capture a full week of user behavior. This could skew the results and lead you to make a decision based on incomplete data.
• Seven days is a good starting point: Running the experiment for at least seven days helps ensure that you capture a more representative sample of user behavior across the entire week.

The other options are incorrect:

• You should run as many variants as possible before you make a decision. While testing multiple variants can be helpful, it’s not the reason to continue the experiment for a full week.
• You must run the experiment for at least seven days to see a recommendation in the results column. The experiment will provide results even if it runs for less than seven days, but those results might not be reliable.
• You must run the experiment for at least 7 days to see a recommendation in the metrics-used column. The metrics used in the experiment are determined by you, not the experiment duration.

The main chapter where the reference to the correct answer can be found is “Optimize your store listing with experiments”. The section on “Set up experiments” mentions that experiments should run for at least seven days to get enough data for reliable results. This implies that a shorter duration might not capture the full range of user behavior, including weekday and weekend variations.

 

You see an increase in your app’s churn rate after a year during which it was steady. How can a custom store listing help you proactively avoid the loss of your users?

• You can present users with a single and stable message that appeals to a wide and diverse user base.
• You can present multiple non-specific messages to a wide range of users with a single description for all.
• You can target a wide range of users with a single store listing that’s easy to maintain and doesn’t need monitoring.
• You can target inactive users and explain the benefit of new features or recently added benefits.

 

Here’s why:

• Addressing Churn: Churn rate refers to the percentage of users who stop using your app within a given period. To combat churn, it’s essential to re-engage inactive users and remind them of your app’s value.
• Targeted Messaging: Custom store listings allow you to tailor your messaging to specific user segments. By creating a custom listing that highlights new features or benefits, you can directly address the needs and interests of inactive users, encouraging them to return to your app.
• Proactive Approach: Instead of waiting for users to churn, you can proactively use custom store listings to communicate updates and improvements, keeping your app relevant and engaging.

The other options are not as effective in addressing churn:

• Single, Stable Message: While a consistent message is important for branding, it might not be enough to re-engage inactive users who need specific reasons to return.
• Multiple Non-Specific Messages:  Non-specific messages lack the targeted appeal needed to address the diverse reasons why users might become inactive.
• Single Store Listing without Monitoring: A single store listing for all users doesn’t allow for personalized messaging or the flexibility to adapt to changing user needs.

The main chapter where the concept of custom store listings is discussed is “Create a Google Play Store listing”. While the text doesn’t explicitly mention using custom listings to address churn, it emphasizes the ability to tailor store listings to specific target audiences and use localized graphic assets and translations to provide a personalized experience for users. This implies that custom listings can be a powerful tool for re-engaging inactive users and reducing churn.