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Robinhood Investment Recommendations: Guardrail Metrics for Trade Volume Optimization

Metrics Definition · intermediate

As a PM for Robinhood's personalized investment recommendations feature, your team's primary goal is to increase the volume of trades made through these recommendations. What critical counter-metrics and guardrails would you implement to ensure this optimization doesn't lead to adverse outcomes for users or the platform?

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Community approaches

Ishant Juyal’s approach

The feature is - we make recommendations to the users about the investments they can or should make, and the goal is to increase the volume of trades which are made specifically through this recommendation. What we are doing is we are nudging the user that we are giving you a thought-through recommendation. Now we just click on it and it will be done.

Now, when we talk about the counter metrics and guardrails, we need to think about what are the outcomes that people don't want or that the platform doesn't want.

Let's talk about the outcomes that the users don't want:

  • They don't want to lose money.
  • They don't want to make bad investments.
  • They don't want their money to get stuck.
  • They don't want a bad experience.

Now let's talk about the outcomes that the company wants to avoid:

  • They don't want the user to lose money because of a bad recommendation.
  • They don't want the user to have a bad experience.
  • They don't want people to stop investing or churn
  • They don't want the users to raise a lot of complaints about a bad experience which leads to a complaint.
  • We don't want the customer support queries to increase because of this feature.
  • We don't want the ratings of the app or platform to go down
  • We don't want legal complications because of the feature or in general too.

Whatever outcome we don't want, we can turn that into a guardrail metric.

  • Losses made by users on the platform (with a comparison and segmentation between people who use this feature and people who do not use this feature)
  • The number of complaints by the users on the customer service platform.
  • The retention rate of users, specifically who use this feature.
  • The churn rate of the users.
  • The rating for the platform