Start by framing the problem as a causal inference question about incrementality, not just correlation. Propose a quasi-experimental design (e.g., geo-based or switchback) to isolate the causal effect of web on total revenue, and outline how you'd measure cannibalization from API and mobile. Then detail the model, metrics, power analysis, robustness checks, and decision thresholds to conclude whether the growth is incremental or cannibalistic.
Pro tip: Emphasize that you'd triangulate multiple methods (e.g., geo experiments, synthetic control, and causal impact) to validate findings, and that you'd pre-register your analysis plan to avoid p-hacking. Also, mention that you'd consider the business context: if API and mobile are strategic channels, cannibalization might be acceptable, but you'd still quantify the net effect.
Clarify the treatment (e.g., web channel exposure) and outcome (total revenue). Specify the estimand: the incremental revenue attributable to web, accounting for cannibalization from API and mobile.
Propose a quasi-experimental design such as a geo-based experiment (e.g., DMA-level) or a switchback experiment if web and mobile are mutually exclusive. Alternatively, use synthetic control or difference-in-differences with a suitable control group.
Use a model that estimates the effect of web on total revenue while controlling for API and mobile revenue. Key metrics: incremental revenue, cannibalization rate (change in API/mobile revenue per unit change in web revenue), and net lift.
Perform power analysis to determine required sample size or duration. Robustness checks: placebo tests, sensitivity to model specification, and validation with alternative methods (e.g., causal impact).
Define thresholds for incrementality (e.g., net lift > 0 with 95% confidence) and cannibalization (e.g., cannibalization rate < 20%). Use these to decide whether to scale web or adjust strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.