Build dashboard; diagnose engagement–purchase gap
MetaData Scientist · OnsiteLast updated
This question evaluates a data scientist's competency in product analytics and experimentation, focusing on dashboard specification, precise event- and user-level metric definitions, core dimensions, data freshness SLAs, and instrumentation within the Analytics & Experimentation domain.
Build a comprehensive dashboard for the Shopping tab (organic only). Specify primary metrics (e.g., GMV, purchases, unique buyers, PDP CTR) and secondary metrics/guardrails (e.g., bounce rate, search exits, creator engagement). Define each metric precisely (event- and user-level), dimensions (country, device, acquisition channel), and freshness/SLAs. Then, suppose engagement with the tab is high but purchase rate is low: propose a diagnosis plan including segmentation (new vs. returning, search vs. feed, product category), funnel drop-off analysis, instrumentation checks, and experiments you’d run to isolate UX vs. supply issues.
Solution walkthrough is for members
Get the full walkthrough with reference code and complexity analysis, plus Kitty Coach at 300 messages a day. Hints, the code runner and community answers stay free.