Basic Assumptions 

• Youtube TV is a subscription based platform that provides video content from 70+ TV channels. It also contains documentaries and movies.
• Youtube TV  is an added feature on the Youtube Website/App and it is accessible via desktop and mobile.
• Performance metrics for Youtube TV are just for a single region. Let’s assume the US.

Important Metrics to Measure

• Daily Active Users: Are the number of users who use the Youtube App going down/going up/remaining constant week over week(WoW)? How do these numbers change by device type(mobile/desktop)?
• Average Session Time : How does the distribution of average time spent by a user on Youtube TV change WoW by device type?
• % Returning Users : What % of users are returning users? How does this percentage change WoW by device?
• Distribution of average watch type per user : Distribution of average watch time per user WoW by device type.

Description: Youtube TV is subscription service that enables users to view entertainment channels through youtube TV app. It does not require any new hardware and also is advertisement free. Available platform is tablet, smart TV, mobile. The product shows vidoes by genres and based on your past viewing history.

Goal: Since Youtube TV has been around for a while, the Goal of the app is to acquire new users and retain existing users. Since acquiring is more of a marketing issue, i will focus on retaining existing users.

Users: People who want to watch TV during commute, people who want to watch TV with family and do not want to buy cable/subscription services

User Journey: People who are watching TV during commute, want to watch something that ideally they can finish during their commute; people who are watching it in the evening/lesiure time want to watch something. The typical problem for a user is that there are too many choices and hard to find the content they want and discover new content

Metrics: Since it is a subscription service, there are no third party advt. and the stake holders are content producers and consumers.

We will focus on retaing the users, so we focus on mertrics for consumers

For the viewer; the metrics – total hours of tv watched ( => indicates they are finding relevant content);

# of daily active users (=> DAU should be counted only if the user has watched a TV for 10 minutes or more; > this is more of a hypothesis but some data should be analyzed if watching more than 10 minutes results in people completeing the episode/movie.

# of time spent in finding new content (what to do if users are sharing account – > may result in poor recommendations)

# of time the first recommendation screen resulted in a viewing session

In summary, to ensure that Youtube TV app is helping customers find the entertainment of their choice, I wold focus on DAU and total time spent on watching TV/user/session.

Description: Youtube TV is a prime monthly subscription based offering that offers live TV, DVR and on-demand video.

Goal: The product was launched in 2017 and the goal would likely be on retention followed by acquisition of new customers. The way to do this is to enhance the product portfolio with increased number of channels offered.

Scope: For the scope of this question, let us focus on non-operational goals (not acquiring new partners) but enhance the retention and acquisition and activation.

Users: Youtube TV is trying to replace cable TV and box setups. In US, the trend of “cutting the cord” seems to be increasing with customers opting for streaming services and/or online video offerings.

Since the subscription offers up to 6 profiles under single account, it is worth looking at account level statistics as a way of data grouping.

1. Highly Active Accounts: Accounts with combind usage of over 12 hours a day.

2. Moderately Active Accounts: Accounts with combind usage of over 8 hours a day.

3. Low Active Accounts: Accounts with combind usage of under 4 hours a day.

4. Inactive Accounts: Accounts with 0 hours usage.

Metrics That Matter:

Summary: Focusing on the retention metrics ahead of acquisition and activation will focus on fine tuning product offering so the churn rate is reduced.