My first instinct was to jump straight to A/B tests but that's kind of backwards since you're diagnosing a gap that already exists, not testing a change.
First, clarify the metric and validate the observation by checking definitions and data sources. Then, systematically explore potential causes across user, product, and content dimensions, using data to test hypotheses and isolate the root cause.
Pro tip: Frame the investigation as a structured root cause analysis, and emphasize the importance of understanding the 'why' behind the metric before jumping to solutions. Show awareness of confounding factors and the need for controlled comparisons.
Define what 'consume Stories' means (e.g., views, time spent, completion rate) and ensure the comparison is apples-to-apples. Check data sources and query logic to rule out measurement errors.
Break down the metric by user demographics, geography, device, and engagement levels to see if differences persist within segments. Compare the composition of Instagram vs. Facebook user bases.
Examine how Stories are presented and consumed on each platform: UI/UX, algorithm, content types, creator ecosystem, and social graph. Look for features that might drive higher consumption on Instagram.
Where possible, design A/B tests or use natural experiments to isolate causal factors. For example, test if changing the Facebook Stories UI to match Instagram's increases consumption.
Summarize the key drivers, quantify their impact, and propose actionable next steps. Prioritize based on potential impact and feasibility.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.