Case Study on Ride-Hailing App
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Case Study: This is an open-ended case study asked around the reliability of ride-hailing service having decreased in a particular area. I had to suggest the data variables I will consider analyzing to solve the problem
Interviewer: The reliability of Ola cars has decreased in a particular area. What data parameters will you consider analyzing the problem?
Priyanka: There were some clarifying questions that I asked to get more insights about the problem. How do you measure reliability exactly?
Interviewer: Reliability would be a measure of Rides getting requested vs requests getting accepted i.e., Out of the requests accepted how many are actually completed.
Priyanka: Ok since the platform has both driver and rider sides do you want me to focus on any specific one.
Interviewer: You are free to choose.
Priyanka: Ok I would like to think about the data variables that driver considers while accepting a ride:
Distance/Length of trip — Drivers won’t prefer short rides as they might not earn much especially if the pickup location is from a far away location and the trip duration is also short. At the same time, they can pick someone from a longer duration trip.
Type of ride — Whether it is a shared ride or a non-shared one(Ola Mini/Micro). Share might be less profitable from a driver’s perspective as they will have to pick and drop passengers at different locations.
Geolocation — Generally we know the geolocation through the Lat/Long . Like if the Route is likely to have traffic then driver might not prefer to pick up from that location as it will be wastage of time being stuck in traffic.
Review and Rating of Rider — If a rider is rated poorly, he might be one who might cancel the ride or increase wait time.
Let us prioritize geolocation as an issue.
Interviewer: Assume that you have identified that Bannerghatta Road in Bangalore has this issue. Driver’s reliability is really low in the area despite the demand. How will you solve the issue?
Priyanka: As the area is always congested and traffic inflow is high, drivers prefer not to accept rides from that area.
If can identify some pickup points and ask drivers and customers to access the pickup points — as we have on airports, tech parks it can reduce the problem to some extent.
Interviewer: The solution will work only at some locations, but this will not serve all segments. For example — people living in residential areas might still face this issue.
Priyanka: Then another solution can be Surge Pricing — We can incentivize drivers if they accept rides in that area.
Interviewer: Ok this sounds correct, This is how we also compensate our drivers.
Interviewer: If there is feature X that you need to implement, and your team lead asks you to implement another feature Y. How would you decide which one to implement given that u can implement only one of these?
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