I jumped straight into regression and kind of glossed over the causation problem, which I think was a mistake.
Start by clarifying the business goal and defining the causal question: does higher average rating or review volume cause higher sales? Then propose a mixed-methods approach combining observational analysis (e.g., panel data with fixed effects) and randomized experiments (e.g., A/B tests that manipulate review display or solicitation) to establish causality, while addressing confounders and endogeneity.
Pro tip: Emphasize that correlation is not causation and that you would use methods like instrumental variables or difference-in-differences to isolate the effect, while also considering potential feedback loops (e.g., sales driving reviews).
Confirm whether the goal is to quantify the causal impact of reviews on sales or to build a predictive model. Define key metrics: average rating, review volume, sales (e.g., units sold, revenue), and potential moderators (e.g., product category, price).
Use historical data to explore correlations, controlling for confounders (e.g., product quality, marketing spend, seasonality) via regression with fixed effects, panel data methods, or propensity score matching. Check for reverse causality.
Propose A/B tests where you manipulate review-related factors (e.g., display order, solicitation emails, highlighting positive reviews) and measure the impact on sales. Ensure randomization at the user or product level and sufficient power.
Use instrumental variables (e.g., review platform changes) or natural experiments to strengthen causal inference. Validate findings across segments and time periods, and consider sensitivity analyses.
Translate findings into actionable insights for the product team, such as the expected sales lift from improving average rating by X stars or increasing review volume by Y%. Discuss limitations and next steps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.