The November part was easy, Black Friday and Cyber Monday basically write themselves.
Start by separating the recurring November spike from the one-off June spike, then hypothesize business drivers for each. For November, consider seasonal shopping events like Black Friday/Cyber Monday and holiday preparation; for June, think of company-specific events like a major product launch, marketing campaign, or external shock. Validate hypotheses by checking if the spikes align with known events and whether they are consistent across regions or merchant segments.
Pro tip: Always tie your hypotheses back to Shopify's business model: more sessions can come from either more merchants or more shoppers per merchant. Distinguishing between these two sources will show you understand the platform dynamics.
Confirm the November spike is recurring annually and the June spike is a one-time event. Note the magnitude, duration, and whether they affect all merchants or specific segments.
Link the November spike to seasonal shopping events like Black Friday, Cyber Monday, and holiday shopping. Consider increased merchant activity (e.g., new store launches, promotions) and consumer behavior.
Brainstorm possible causes: a major product launch (e.g., new Shopify feature), a global event (e.g., pandemic shift to e-commerce), a marketing campaign, or a competitor's outage driving traffic to Shopify stores.
Suggest ways to test hypotheses: compare with industry trends, check if the spike is global or regional, analyze merchant segments (new vs. existing), and look for correlations with internal events or external factors.
Summarize the most likely explanations, acknowledge uncertainty, and propose further analysis to confirm. Highlight how this understanding can inform capacity planning or marketing strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This part was actually fun but I probably spent too long on the EDA and not enough time clearly articulating the hypotheses.
Start by clarifying the dataset's structure and the metric showing spikes, then systematically explore the spikes to identify patterns and potential drivers. Formulate at least two testable hypotheses that could explain the spikes, and outline how you would validate them with additional data or experiments.
Pro tip: Focus on actionable hypotheses that can be tested with available or easily obtainable data, and consider both internal factors (e.g., product changes) and external factors (e.g., seasonality) to show comprehensive thinking.
Ask clarifying questions about the dataset, the metric, and the time period. Identify when and where the spikes occur and their magnitude.
Analyze the spikes by segmenting data (e.g., by user cohort, geography, device) to see if they are broad or concentrated. Look for correlations with other variables.
Based on patterns, propose at least two plausible explanations for the spikes. Ensure hypotheses are specific and testable.
For each hypothesis, describe what additional data you would collect and what analyses or experiments (e.g., A/B tests) you would run to validate or refute it.
Discuss which hypothesis to test first based on potential impact and feasibility, and how you would communicate findings to stakeholders.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.