I started by listing confounders which felt right, seasonality, platform changes, competitor launches, but I got a bit tangled trying to bridge from 'here's what could cause this' to 'here's how I'd actually test it.' The A/B framing is tricky because you're often post-hoc on a drop like this, so I ended up talking about a holdout design and synthetic control for the Mexico market since the sample dynamics differ.
Start by systematically identifying potential confounders through data exploration and domain knowledge, then design a controlled experiment (e.g., A/B test or quasi-experiment) to isolate the cause, specifying key variables to control for. Finally, communicate findings clearly to stakeholders, emphasizing actionable insights and limitations.
Pro tip: Always consider both internal and external factors (e.g., seasonality, competitor actions, product changes) and use a mix of quantitative and qualitative methods to triangulate the root cause. When communicating, tailor the message to the audience—focus on business impact for executives and technical details for engineers.
Brainstorm and list possible internal and external factors that could explain the drop, such as seasonality, marketing campaigns, product updates, competitor actions, or economic trends. Use data to check correlations and rule out obvious causes.
Choose an appropriate experimental design (e.g., A/B test, switchback, or quasi-experimental method like difference-in-differences) that can isolate the effect of the suspected cause while accounting for confounders. Ensure randomization and control groups where possible.
Identify key covariates to include in the analysis, such as user demographics, device type, time of day, and pre-experiment behavior. Use statistical techniques (e.g., regression, matching) to control for these variables and reduce bias.
Run the experiment, analyze the data using appropriate statistical methods, and check for validity (e.g., power, novelty effects). Validate findings with robustness checks and sensitivity analyses.
Present results in a clear, non-technical manner for business stakeholders, highlighting the root cause, confidence level, and recommended actions. Use visualizations and tailor the message to the audience's priorities.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.