My first instinct was to jump straight to 'maybe a UI bug' and I had to pull myself back.
Start by clarifying the metric definition and time frame, then segment the drop by dimensions like platform, user cohort, and product category to isolate the cause. Use a structured root cause analysis, considering internal changes, external factors, and data quality issues, and validate hypotheses with data before proposing solutions.
Pro tip: Always quantify the impact of each potential cause and prioritize based on the size of the drop they explain; this shows you can focus on what matters most rather than listing every possibility.
Define what 'cart additions' means (e.g., unique users adding to cart, total add-to-cart events) and the time period of the drop. Confirm if the drop is compared to previous week, month, or year, and whether it's absolute or percentage.
Break down the metric by dimensions such as platform (iOS, Android, web), user type (new vs. returning), geography, product category, and traffic source to identify where the drop is concentrated.
Brainstorm potential causes: internal changes (app updates, pricing changes, promotions ending), external factors (competitor actions, seasonality, economic shifts), and data/measurement issues (tracking bugs, logging errors).
For each hypothesis, identify data sources and run analyses (e.g., funnel analysis, cohort analysis, A/B test results) to confirm or reject. Quantify the impact of each confirmed cause.
Summarize the root cause(s) with evidence, estimate the impact, and propose next steps such as fixing a bug, reverting a change, or launching a new initiative to recover the metric.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.