The sessionization part is where I spent most of my mental energy.
Start by loading and merging the events and guests tables, then filter to the 7-day window and remove bot traffic. Sort events by guest and timestamp, compute inter-event gaps, and flag new sessions when the gap exceeds 30 minutes. Use groupby and transform to assign session IDs, then aggregate to session and guest levels to compute the required metrics, finally grouping by traffic_source and device.
Pro tip: Always validate sessionization logic with a small sample and use vectorized operations like cumsum on boolean flags to avoid slow loops; also, be explicit about how you handle ties or missing timestamps to show rigor.
Load the raw events and guests tables, merge them to enrich events with guest attributes, and filter to the 7-day window ending 2025-09-01. Remove bot traffic using a predefined flag or user-agent pattern.
Sort events by guest_id and event_timestamp, compute the time difference between consecutive events per guest, and create a new session flag when the gap exceeds 30 minutes. Use cumulative sum of this flag to assign a unique session ID per guest.
For each session, determine if it contains an order_completed event and compute the time from first page_view to first order_completed. Aggregate to guest level to count sessions and identify converting guests.
Group by traffic_source and device to compute unique guests, total sessions, session-level conversion rate (sessions with order / total sessions), guest-level conversion rate (guests with order / total guests), average sessions-to-first-order, and median time from first page_view to first order_completed.
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First, clarify the event data schema and define the 7-day window and guest identifier. Then, for each guest, determine the highest funnel step they reached, ensuring de-duplication by counting each guest once at their furthest step. Finally, compute step-through rates and absolute drop-offs between consecutive steps.
Pro tip: Always confirm whether the funnel should be strictly sequential (e.g., a guest must complete page_view before add_to_cart) or if any occurrence of a later step counts; this affects de-duplication logic and results.
Confirm the event data structure, guest identifier, timestamp, and event types. Define the 7-day window and ensure events are filtered accordingly.
For each guest, find the maximum funnel step reached (e.g., page_view=1, add_to_cart=2, checkout_start=3, order_completed=4) within the window.
Count each guest once at their highest attained step, ensuring no double-counting across steps.
Calculate step-through rate as (guests at step N+1 / guests at step N) * 100, and absolute drop-off as guests at step N - guests at step N+1.
Sanity-check numbers (e.g., monotonic decrease), and present the funnel with clear labels and insights.
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I blanked for a second on the Wilson CI formula off the top of my head.
First, compute the sitewide guest-level conversion rate as the baseline. Then, filter segments with at least 100 sessions, calculate each segment's conversion rate and lift relative to the baseline, and compute the 95% Wilson confidence interval for each segment's conversion rate. Finally, rank segments by lift and report the top 5 with their lift and confidence intervals.
Pro tip: When reporting lift, clarify whether it's absolute or relative, and consider the confidence interval overlap with the baseline to assess significance. Also, be prepared to discuss the trade-off between statistical significance and practical significance, especially with multiple comparisons.
Calculate the sitewide guest-level conversion rate by dividing total conversions by total sessions (or guests) across all segments. This serves as the reference for lift.
Remove any segment with fewer than 100 sessions to ensure sufficient sample size for reliable estimates.
For each remaining segment, compute the conversion rate, lift (absolute difference or relative percentage) versus the baseline, and the 95% Wilson confidence interval for the segment's conversion rate.
Sort segments by lift in descending order and select the top 5. Report their lift and Wilson confidence intervals.
Discuss the results, noting any segments where the confidence interval does not include the baseline rate, and consider practical implications and potential multiple comparison issues.
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Honestly the most interesting edge case in the whole problem.
Start by clarifying the business context and data model, then outline a step-by-step algorithm that handles the edge case. Emphasize the trade-offs between attribution accuracy and data completeness, and propose validation metrics to ensure the rule works as intended.
Pro tip: When implementing the fallback to prior 24 hours, ensure you don't double-count events that might already be attributed to a previous conversion. Consider adding a flag to indicate when the fallback was used for transparency in analysis.
Confirm the definition of 'window', 'order_completed event', and 'earlier events'. Understand the event schema and how sessions are typically attributed.
For each order_completed in the window, check for earlier events in the same window. If none, look back 24 hours from the order_completed timestamp to find the first event. If found, attribute the conversion to that event; otherwise, treat as direct.
Address potential issues like multiple order_completed events, events exactly at the 24-hour boundary, and missing or malformed timestamps. Ensure the logic is deterministic and reproducible.
Write efficient SQL or code to apply the rule, then validate with test cases and compare against a baseline. Monitor the impact on conversion metrics and attribution distribution.
Discuss the trade-offs: increased attribution accuracy vs. potential complexity and bias. Recommend documenting the rule and considering A/B testing if feasible.
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Start by clarifying the scale and constraints (data size, latency, cost) and the current Pandas pipeline's logic. Then systematically map each Pandas operation to its PySpark equivalent, highlighting differences in execution model (lazy vs eager), memory usage, and partitioning. Finally, discuss trade-offs and optimizations like broadcast joins, caching, and avoiding UDFs.
Pro tip: Emphasize that the biggest shift is from in-memory, single-node operations to distributed, lazy transformations—so you must think about data partitioning and shuffles upfront. Mention that you'd validate the PySpark pipeline against a Pandas sample to ensure correctness before scaling.
Ask about data volume, growth rate, latency SLAs, and cost constraints to determine if PySpark is necessary and what optimizations matter.
Identify each Pandas step (e.g., read_csv, groupby, apply, merge) and translate to Spark equivalents (spark.read, groupBy, transform, join), noting semantic differences.
Explain how Spark's lazy evaluation, partitioning, and shuffling affect performance, and how to avoid collecting to driver or using Python UDFs unnecessarily.
Discuss techniques like broadcast joins for small tables, repartitioning, caching intermediate results, and using built-in functions over UDFs.
Propose testing the Spark pipeline on a sample against the Pandas output, then gradually scaling up while monitoring performance and cost.
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