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Imagine you get user feedback to build a video scrubber on TikTok. What would you do?

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Clarifying Questions:
1. Define a video scrubber: The slider that allows a viewer to move the timer on the video so that they can get to specific parts of the video that interests them.
2. Is the feedback given for all videos or a specific section of videos (after a certain length of time) – let’s assume that this is for all videos more than 15 seconds.
3. Who provided the feedback? – Assume your biggest stakeholder.
4. US or global? – Global
5. Any constraints that I should know about? – nothing else
6. Why was this feedback given? - Assume as you see fit - Ok, here. I will assume so that it allows users to consume long form content quicker.

Goal of TikTok:
Inspire creativity and spread joy. This aligns with that as people want to get to certain parts of the video that bring them the most joy and inspiration.

Users:
1. Content consumers
2. Content creators
3. Advertisers

Let's focus on the Content consumers as this feature is mostly going to affect them.

Let's look at how this could help them and what we can do to improve the experience for the target group.

Video scrubbers help users consume long form content more easily.
It allows them to quickly get to the portion of the video they want to see for instant gratification as well as share the video with the details of the scrubbing to friends and share content that way.

Lastly, this also acts as a tool that could encourage consumers to watch more long form content on TikTok thereby increasing the engagement and Time on app.

At this time, this is more of a hypothesis - I'd still want to verify it - To ensure that we are building the right feature, I will run an A/B test to determine this is going to be a hit with the customers.

The A/B test will be with 2 test groups - test and control with the test group being shown a scrubber feature for a period of say 6 weeks and the control group will get the current experience.

The metrics that I would use to evaluate the A/B test are:

Activation/Acquisition:
1. Number of users that are using the scrubber for the first time

Engagement:
1. Number of videos that used a scrubber
2. Avg number of videos scrubbed per user
3. DAU/WAU/MAU
 

Based on the results, I will make a decision on whether I want to go ahead of rolling it out.
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Clarify question: 

horizontal time scroll of the video letting users to move forward or backward to a specific timestamp. Ideally the user wants to preview the thumbnails as they scroll along 

Is this feedback for all videos or specifically for longer videos (>4 mins) ?

Understand if this aligns with company mission/growing trends/competition:

Tiktok's mission is to inspire creativity and bring joy, by empowering everyone to become creators and share their experiences with users all over the world. 

user feedback seeks to address a possible painpoint - for repeat users, and for longer videos. 

This is especially useful for Tiktok's ambitions to enable creators with both short and longer videos to become easily consumable. 

Clearly users are spending more and more time watching video either on Tiktok or on Youtube, and it positions Tiktok well if it makes it easier to consumer longer form videos from creators like exercise session, cooking session etc. 

Experiment (A/B test):

Outcomes that i'm interested in: 

More users watch longer videos

User watches more longer videos than before.

Users are able to jump easily to longer videos and hence watch it more than once

Metrics to track:

Previously longer videos may have been watched for more time trying to get to the exact point in video. But people may find it frustrating and stop watching longer videos. So time spent may not be the right metric to track. Instead we want the # of users who watch longer videos to increase as well as # of longer videos watched per user, # of longer videos watched more than once per user (to test for the hypthesis that repeat users find scrubbing useful), and time spent scrubbing. 

Run this experiment on subset of users (sampleset distributed among high freq, avg and low freq users of the platform) within a specific geo 

Will decide based on the metrics. 

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