Here's how we can analytically deal with a change in the cancellation policy at Booking.com using an A/B test. Let's break down the steps as you've outlined:
1. Clarifying Questions:
What specific change are we making to the cancellation policy? (e.g., stricter cancellation deadlines, penalties, etc.)
What is the primary goal of this policy change? (e.g., reducing cancellations, increasing revenue, etc.)
What user segments are we targeting? (e.g., leisure travelers, business travelers, etc.)
Are there any other ongoing changes or factors that could affect user behavior during the experiment period?
2. Prerequisites:
Success Metrics: Metrics that directly measure the goal of the policy change (e.g., decrease in cancellation rate, increase in revenue).
Counter Metrics: Metrics that ensure the policy change hasn't negatively impacted other important aspects (e.g., user satisfaction).
Ecosystem Metrics: Metrics that capture the broader impact on the business (e.g., customer lifetime value).
Control and Treatment Variants: Control group (current policy) and treatment group (new policy).
Randomization Units: Bookings/users that will be randomly assigned to either control or treatment groups.
Null Hypothesis: The new cancellation policy has no effect on the success metrics.
Alternate Hypothesis: The new cancellation policy has a significant effect on the success metrics.
3. Experiment Design:
Significance Level (α): Typically set to 0.05, represents the probability of rejecting the null hypothesis when it's actually true.
Practical Significance Level: A threshold that defines the minimum meaningful change in the success metric.
Power (1 - β): The probability of correctly rejecting the null hypothesis when it's false, usually set to 0.8 or higher.
Sample Size: Determined based on effect size, significance level, and power. For example, assuming 0.05 significance level, 0.8 power, and a medium effect size, you might need 10,000 bookings per group.
Duration: Estimate the experiment duration based on expected booking frequency and required sample size.
4. Running the Experiment:
Ramp-up Plan: Gradually introduce the new policy to avoid sudden disruptions. This could be implemented by applying the new policy to a percentage of bookings initially and increasing the percentage over time.
5. Result to Decision:
Basic Sanity Checks: Ensure randomization was successful and groups are comparable.
Statistical Test: Use appropriate statistical tests (e.g., t-test, chi-squared test) to compare success metrics between control and treatment groups.
Recommendation: If the treatment group shows a statistically significant and practically meaningful improvement in success metrics, recommend implementing the new policy.
Remember that this is a high-level overview, and each step involves more detailed planning and execution. Also, the example values provided for metrics, significance levels, and sample size should be adjusted based on your specific context and the magnitude of the changes you're making.