Identify Booking Drivers
Analyzing Booking Drivers for a Peer-to-Peer Car-Sharing Marketplace
As a Data Scientist at a peer-to-peer car-sharing marketplace, your team aims to identify which product, listing, host, renter, and market features most significantly influence whether an exposed listing ultimately results in a booking.
Data Description
Assume you have access to a dataset where each row represents an eligible listing impression or a listing-detail-page view. Each row contains the following attributes:
user_idsession_idlisting_idhost_idimpression_timestampmarkettrip dateslisting pricefeesdiscountdistance from rentervehicle classvehicle agephoto counthost ratingreview countcancellation historyresponse timeinstant-book eligibilityavailabilityrenter historydevicetraffic channel- A binary indicator specifying whether the impression converted into a booking within 24 hours.
Tasks
Design an analysis and presentation that addresses the following questions:
- How would you define the target, denominator, and primary metric for this analysis?
- Which features appear most important for predicting booking conversion?
- Which features are likely causal levers versus merely correlated signals?
- How would you control for confounding, selection bias, seasonality, and marketplace effects?
- What recommendations would you make to product, pricing, host-quality, or search-ranking teams based on your findings?
- How would you validate these recommendations using both offline analysis and controlled experiments?
AI interview prep
Core dilemma
Extracting actionable, causal insights from complex observational data while mitigating inherent biases and confounding factors to drive product and business strategy.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Identify Booking Drivers
Analyzing Booking Drivers for a Peer-to-Peer Car-Sharing Marketplace
As a Data Scientist at a peer-to-peer car-sharing marketplace, your team aims to identify which product, listing, host, renter, and market features most significantly influence whether an exposed listing ultimately results in a booking.
Data Description
Assume you have access to a dataset where each row represents an eligible listing impression or a listing-detail-page view. Each row contains the following attributes:
user_idsession_idlisting_idhost_idimpression_timestampmarkettrip dateslisting pricefeesdiscountdistance from rentervehicle classvehicle agephoto counthost ratingreview countcancellation historyresponse timeinstant-book eligibilityavailabilityrenter historydevicetraffic channel- A binary indicator specifying whether the impression converted into a booking within 24 hours.
Tasks
Design an analysis and presentation that addresses the following questions:
- How would you define the target, denominator, and primary metric for this analysis?
- Which features appear most important for predicting booking conversion?
- Which features are likely causal levers versus merely correlated signals?
- How would you control for confounding, selection bias, seasonality, and marketplace effects?
- What recommendations would you make to product, pricing, host-quality, or search-ranking teams based on your findings?
- How would you validate these recommendations using both offline analysis and controlled experiments?