Design an A/B test for a recommendation algorithm
Last updated: September 14, 2025
Quick Overview
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
Booking.com
September 14, 2025106
4
2,722 solved
Design an experiment to test the impact of new pricing tiers. Include sample size calculation, metrics, and analysis plan.
This analytics question from Booking.com's Take-home Project tests your ability to think critically about data. The interviewer expects you to consider confounding variables, selection bias, and the difference between correlation and causation.
What the Interviewer Expects
- Define clear success metrics aligned with business goals
- Propose a basic experimental design with control and treatment groups
- Interpret results correctly and draw reasonable conclusions
- Identify obvious confounding variables
Key Topics to Cover
How to Approach This
- Define success metrics carefully. A good metric is measurable, actionable, and aligned with business goals.
- Run experiments long enough to account for novelty effects and weekly seasonality.
- Use funnel analysis to identify where users drop off for maximum optimization impact.
- Segment results by key dimensions (platform, country, user cohort) to catch hidden patterns.
- Consider network effects and interference between treatment and control groups.
Possible Follow-up Questions
- How would you handle interference between treatment and control?
- What if the experiment shows a positive short-term effect but you suspect a negative long-term impact?
- What would you do if a stakeholder wants to end the experiment early because initial results look good?
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Browse Analytics QuestionsSample Answer
Problem Setup
We want to assess the impact of new pricing tiers for our recommendation algorithm on Booking.com. The analytical question is: "Do the new pricing tiers lead to an increase in booking conversions comp...
Methodology
To design our A/B test, we will use a randomized control trial (RCT) approach. We will randomly assign users to either the control group (existing pricing) or the treatment group (new pricing tiers). ...
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