Design a randomized controlled experiment (A/B test) where users are randomly assigned to either the new chat notification design or the existing design, and measure the difference in daily active users (DAU) over a sufficient period. Use causal inference techniques like difference-in-differences or switchback experiments if randomization is not feasible, and validate assumptions through pre-experiment checks and robustness tests.
Pro tip: Emphasize the importance of defining the metric precisely (e.g., DAU as any engagement with Chat) and checking for novelty effects by analyzing the treatment effect over time. Also, consider network effects and spillover, which are common in communication products like Google Workspace Chat.
Clearly specify the treatment (new notification design), control (old design), and outcome (DAU). Define DAU precisely, e.g., number of unique users who send at least one message or react to a message in a day.
Prefer a randomized controlled trial (A/B test) at the user level. If randomization is not possible (e.g., due to network effects), consider cluster randomization (by team or organization) or a switchback experiment. Discuss potential biases and how the design addresses them.
Collect user-level data on assignment, engagement events (messages sent, reactions, etc.), timestamps, and covariates (e.g., user tenure, past activity). Ensure data quality and sufficient sample size/power.
Key assumptions: random assignment, no interference (SUTVA), and parallel trends (if using DiD). Validate via pre-experiment covariate balance, placebo tests, and checking for spillover effects.
Estimate the average treatment effect (ATE) on DAU using appropriate statistical methods (e.g., t-test, regression adjustment). Check for heterogeneity, novelty effects, and long-term impact. Conduct sensitivity analyses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.