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  • A/B Test Cases to Improve Adoption

Brief Description: In this challenge you need to design & develop test cases for A/B testing, to increase adoption of a new feature
Difficulty: Hard
Skills Evaluated: Prioritization, Analytical Ability, Research, Dealing with Ambiguity

At launch Instagram Reels was a product that came with a lot of expectations. Especially in India, it was meant to be the replacement for a void left by TikTok. 

Safe to say, the opportunity still exists even today, despite a lot of local alternatives that have sprung up. But whether Reels is the right product or not, to fill that void, is still debatable.

And so Instagram is regularly carrying out experiments with Reels.

You are part of the Reels product team (either a PM or designer) and you have been tasked with creating 3 test cases for experiments you would like to run, in order to get more people view & engage with content on Reels. Each test case needs to be backed by reasons as to why that test is important.

These cases can be related to: the algorithm, feature set within Reels, the look and feel of Reels, discovery etc.

You also need to define the specific segment of users the test cases will be shown to, as well as any other specific characteristic of the test. Bonus: The more detailing on this aspect the better.

So, to summarise you need to define:

  • 3 test cases (with reasons)
  • Segments to test on (with detailing for bonus pts)

Finally, no matter what you do, your brief is clear:

Don’t let Reels become another IGTV!

Please note:

  • Instagram is NOT hosting this challenge. This is only a learning exercise. However, we will pass on the top solutions to Instagram
  • Designers: You may choose to elaborate your test cases as mocks/prototypes for impact. This is not essential though
  • PMs: You may use wireframes to create more impact/communicate your test cases better. This is not essential though
  • Part of your tests may also involve changes to Instagram itself, which is completely fine
  • Some of the elements of A/B testing have been simplified above for better understanding

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