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Question 1 Describe which main KPI’s you think are the most important ones to track on a daily basis in a free-to-play mobile game. Question 2 Imagine a database table containing rows of events generated by a mobile app that help travellers keep track of connecting flights when they are out flying. The app is still under development and currently only tracks each time a user opens it. At this stage the only columns tracked are ts (UTC server timestamp for the event), userid , and country (will change depending on the user’s current location). As a first step in analyzing this app you want to investigate when and where in the world people first start using it. By using SQL (any dialect but preferably BigQuery) how would you extract the registration date and registration country for each user in this dataset? Either provide a query or describe how you would approach this. Question 3 One of the game teams you are working with ask you for help estimating how well their latest feature will work. The team is already done coding and could without much effort release it to all, or a subset, of players. ● The average number of new registrations in this game is 10 000 per day ● The server team provides a simple A/B-testing tool capable of assigning a percentage of new players into different treatment groups ● Since this is a brand new feature you have limited knowledge about its expected impact on metrics, but the team’s game designer is anticipating that 30% more players will be retained on day 14 after installing the game, and that the populations spending on in app-purchases will increase, when exposed to this new feature. a) How would you go about designing this test (e.g. number of groups / participants and so forth)? b) How would you evaluate the results of this test (e.g. target metrics, analytical approach, tools etc)? Question 4 The CEO of a free-to-play mobiles games company has asked you to help them build a model for predicting the lifetime value (LTV) for one of their games. The purpose with this model is to better help the user acquisition manager to balance the cost per install against estimated LTV so that the game is profitable over time. ● The game has been live for over 1.5 years and data has been tracked since launch. ● Historical data has been tracked in a BigQuery table. Each row contains a t imestamp, userid and an event identifier (either “start_app”, “level_played” or “purchase”). There is also a value column that indicates either the level played or dollar amount spent (depending on event). Describe which models you would experiment with and which one(s) you think will be performing the best based on the information provided above. Question 5: Imagine that you have access to a BigQuery table with several thousands of rows of AppStore review data for one sin gle app. The table consists of the following five columns: d ate, author, title, full_review and star_rating. Please describe which approach you would use to extract insights out of this data? Which tools, programming languages and text mining techniques would you apply?
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Business Intelligence Analyst

Interviewed at MAG Interactive

4.2★
Mar 8, 2017

Question 1 Describe which main KPI’s you think are the most important ones to track on a daily basis in a free-to-play mobile game. Question 2 Imagine a database table containing rows of events generated by a mobile app that help travellers keep track of connecting flights when they are out flying. The app is still under development and currently only tracks each time a user opens it. At this stage the only columns tracked are ts (UTC server timestamp for the event), userid , and country (will change depending on the user’s current location). As a first step in analyzing this app you want to investigate when and where in the world people first start using it. By using SQL (any dialect but preferably BigQuery) how would you extract the registration date and registration country for each user in this dataset? Either provide a query or describe how you would approach this. Question 3 One of the game teams you are working with ask you for help estimating how well their latest feature will work. The team is already done coding and could without much effort release it to all, or a subset, of players. ● The average number of new registrations in this game is 10 000 per day ● The server team provides a simple A/B-testing tool capable of assigning a percentage of new players into different treatment groups ● Since this is a brand new feature you have limited knowledge about its expected impact on metrics, but the team’s game designer is anticipating that 30% more players will be retained on day 14 after installing the game, and that the populations spending on in app-purchases will increase, when exposed to this new feature. a) How would you go about designing this test (e.g. number of groups / participants and so forth)? b) How would you evaluate the results of this test (e.g. target metrics, analytical approach, tools etc)? Question 4 The CEO of a free-to-play mobiles games company has asked you to help them build a model for predicting the lifetime value (LTV) for one of their games. The purpose with this model is to better help the user acquisition manager to balance the cost per install against estimated LTV so that the game is profitable over time. ● The game has been live for over 1.5 years and data has been tracked since launch. ● Historical data has been tracked in a BigQuery table. Each row contains a t imestamp, userid and an event identifier (either “start_app”, “level_played” or “purchase”). There is also a value column that indicates either the level played or dollar amount spent (depending on event). Describe which models you would experiment with and which one(s) you think will be performing the best based on the information provided above. Question 5: Imagine that you have access to a BigQuery table with several thousands of rows of AppStore review data for one sin gle app. The table consists of the following five columns: d ate, author, title, full_review and star_rating. Please describe which approach you would use to extract insights out of this data? Which tools, programming languages and text mining techniques would you apply?

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