Measure Impact of Updated Rider ETA Algorithm

Q: Measure Impact of Updated Rider ETA Algorithm

This question evaluates a data scientist's competency in experimental design, causal inference, metric definition, and marketplace analytics for measuring how an ETA model update impacts demand and supply.

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Question

Scenario

A ride-hailing company has updated its rider ETA-prediction algorithm (the ETA shown to riders before they request a trip) and wants to quantify the marketplace impact on both demand (riders) and supply (drivers).

Task

Design an experiment to measure how the new rider-ETA model affects the business.

Questions

Which primary success metrics would you track on the demand and supply sides?
What intermediate or leading indicators would you monitor?
Would you pick a classic A/B test or a synthetic-control approach? Explain the design, randomization unit, duration, and how you would interpret results.

Hints

Map the customer → marketplace funnel; define guardrail KPIs.
Consider network interference, seasonality, and statistical power before choosing between parallel A/B and geo-level synthetic control.

Measure Impact of Updated Rider ETA Algorithm

Scenario

Task

Questions

Hints

Solution

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