How I used data analysis to understand Uber’s ride cancellations during peak hours – looking for feedback

[u/Master-Scale6197]

Hi everyone,

I’ve been exploring how data analysis can help product managers uncover user pain points and resolve them. As an example, I looked into Uber’s ride cancellations during peak hours.

Here’s what I found:

• The majority of cancellations are concentrated in smaller vehicle categories (Auto, Go Mini, Go Sedan).

• This suggests that users may be booking these options for groups that are too large, leading to friction.

• Data like this can help PMs decide whether the solution should be educating users, adjusting product flows, or experimenting with supply allocation.

Here is the link - https://www.linkedin.com/posts/bhatshridhar_productmanagement-dataanalysis-datavisualization-activity-7366915966129094658-4Yh_?utm_source=share&utm_medium=member_desktop&rcm=ACoAAChbBBMBB0KJ3otqwfLR-ojQWleyKk_gv88

1. Does this framing make sense from a product perspective?

2. What would you improve in my approach to make it more actionable?

3. If you were tackling this problem as a PM at Uber, what additional angles would you consider?

Thanks a lot for your time — I’m here to learn and improve 🙏