I jumped straight to seasonality and marketing campaigns, which felt right but also kind of obvious.
Start by clarifying the data and the spike: is it a one-time event or recurring each May? Then structure your hypotheses into categories: internal changes, external events, data/measurement issues, and seasonality. Prioritize hypotheses that are testable with available data and suggest how you would validate each.
Pro tip: Always consider data quality issues first—a spike might be a tracking bug or bot traffic. Also, check if the spike is consistent across all segments or driven by a specific region/product, which can quickly narrow down causes.
Ask questions to understand the spike: Is it a one-time occurrence or recurring each May? What is the magnitude? Is it across all metrics or just sessions? Ensure the data is reliable and not due to tracking changes.
Group potential causes into: internal factors (e.g., marketing campaigns, product launches, pricing changes), external factors (e.g., holidays, competitor actions, economic events), seasonality (e.g., back-to-school, summer sales), and data/measurement issues (e.g., bot traffic, logging errors).
Rank hypotheses by likelihood and ease of validation. Use data to test each: compare segments (geography, device, user type), check for corresponding changes in other metrics (conversion, revenue), and look for external events.
If possible, corroborate with qualitative sources (e.g., marketing calendars, news) or quantitative methods (e.g., time-series decomposition, anomaly detection). Confirm whether the spike is meaningful and actionable.
Conclude with the most likely cause(s) and recommend actions, such as replicating successful campaigns or fixing data issues. Highlight any remaining uncertainties.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty natural follow-on once you've seen the spike question.
Start by acknowledging that a simultaneous spike in sessions and drop in conversion rate is a classic pattern that often points to a shift in traffic quality or a technical issue. Then systematically outline how you would diagnose the root cause by segmenting the data, checking for instrumentation problems, and validating with external factors. Finally, emphasize the importance of quantifying the impact and recommending actionable next steps.
Pro tip: Always rule out data collection issues first—a sudden spike in sessions could be bot traffic or a tracking bug, which would artificially deflate conversion rate. Mentioning this shows you think about data integrity before jumping to business conclusions.
Check for tracking or instrumentation errors that could cause a spike in sessions (e.g., bot traffic, duplicate events) or a drop in conversions (e.g., broken conversion tracking).
Break down sessions and conversions by source, device, geography, and user type to identify which segment is driving the spike and the drop.
Correlate the spike with marketing campaigns, site changes, or external events (e.g., a viral post, a sale) that could bring in lower-intent traffic.
Investigate whether the spike caused performance degradation (e.g., slow load times, errors) that hindered conversions.
Estimate the impact on revenue and propose actions such as filtering bot traffic, optimizing landing pages, or adjusting targeting.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The data quality piece is where I stumbled a bit.
Start by explaining how you would parse the string year_week into a proper date format, then validate data quality and consistency at the week level. Finally, describe how you would use the parsed dates for time series analysis or metric calculations, ensuring alignment with business logic.
Pro tip: Mention that you would create a reusable date parsing function and validate it against known edge cases (e.g., week 53, year boundaries) to prevent silent errors in downstream analysis.
Convert the string into a standardized date format (e.g., ISO week date) using a function that handles formats like 'YYYY-WW' or 'YYYYWW'. Ensure correct handling of week numbering and year boundaries.
Check for missing, malformed, or duplicate year_week values. Verify that weeks are sequential and within expected ranges (e.g., 1-53). Flag anomalies for further investigation.
Group data by parsed week and calculate relevant metrics (e.g., weekly active users, revenue). Compare trends and identify outliers or shifts that may indicate issues.
If anomalies are found, drill down by dimensions (e.g., product, region) to identify drivers. Use statistical tests or visualizations to confirm significance.
Summarize validation results and insights, highlighting any data quality issues and their impact. Recommend actions or further analysis.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, interpret the pattern: near-zero-duration sessions concentrated in specific shop types likely indicate bot traffic, tracking artifacts, or a technical issue rather than genuine user engagement. Then, outline a systematic investigation plan: validate data quality, segment further, and collaborate with engineering to confirm and resolve the root cause.
Pro tip: Always consider data instrumentation issues before assuming a behavioral shift; many 'insights' are actually tracking bugs. Proactively suggest a quick sanity check with raw event logs to rule out false positives.
List plausible explanations: bot/crawler traffic, tracking pixel misfires, page load failures, or redirect loops. Consider that zero-duration sessions often signal non-human activity or technical glitches.
Check for instrumentation errors: verify session duration calculation logic, ensure timestamps are correctly captured, and look for anomalies in event sequencing. Compare with historical data to see if this is new.
Break down by shop type, geography, device, user agent, and referral source. Identify if the spike correlates with specific bots, marketing campaigns, or app versions.
Engage engineering or product teams to inspect server logs, bot detection systems, and recent deployments. Confirm whether the pattern is due to a known issue or requires a fix.
Propose actions: filter out bot traffic, fix tracking bugs, or adjust metrics. Set up alerts to monitor future spikes and validate the fix.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.