Let's walk through the process step by step for conducting an A/B test to measure the success of the new button in the Uber app suggesting riders to download and use Uber Eats.
1. Clarifying Questions:
What is the main goal of introducing this button? Is it to increase Uber Eats app downloads or increase actual orders on Uber Eats?
Are there any specific user segments or regions targeted for this test?
What is the current baseline conversion rate for riders downloading and using Uber Eats?
2. Prerequisites:
Success Metrics: Conversion rate of users who download and use Uber Eats after interacting with the button.
Counter Metrics: Impact on overall ride booking numbers (to ensure the new button is not cannibalizing rides).
Ecosystem Metrics: Increase in total user engagement, average order value on Uber Eats, etc.
Control and Treatment Variants: Control group (no button) and treatment group (with the button).
Randomization Units: Individual users who open the app during the test period.
Null Hypothesis: There is no significant difference in conversion rates between the control and treatment groups.
Alternate Hypothesis: The treatment group's conversion rate is significantly higher than that of the control group.
3. Experiment Design:
Significance Level (α): Typically set at 0.05.
Practical Significance Level: Decide the smallest effect size that's practically meaningful.
Power: Often set at 0.8, representing an 80% chance of detecting a true effect.
Sample Size: Calculate using an online sample size calculator or based on historical data. Let's assume 10,000 users per group.
Duration: Run the experiment for 2 weeks to capture different days and user behaviors.
Effect Size: Let's assume a 2% absolute increase in conversion rate from the baseline.
4. Running the Experiment:
Ramp-up Plan: Gradually roll out the button to a small portion of users and then increase the exposure over time. This helps identify any technical issues or unexpected behavior.
5. Result to Decision:
Basic Sanity Checks: Ensure the control and treatment groups are similar in terms of demographics and behavior before the test.
Statistical Test: Perform a two-sample hypothesis test, such as a chi-squared test or a t-test, comparing the conversion rates between the groups.
Recommendation: If the p-value is less than the significance level, reject the null hypothesis and recommend implementing the button if the effect size is practically significant.
6. Post Launch Monitoring:
Novelty/Primacy Effect: Monitor for short-term spikes in the treatment group's conversion rate due to users' curiosity about the new feature.
Network Effect: Keep an eye on whether increased Uber Eats usage leads to more referrals or word-of-mouth growth within the Uber app.
Remember, A/B testing is an iterative process. Regularly analyze the results and make data-driven decisions to optimize the feature further if needed.