Funnel Count: Compute Drop-Off at Each Step of a Conversion Funnel
Interview Experience
Problem
You have a list of user events (user_id, event_name, timestamp). A conversion funnel is defined as an ordered list of event names. A user "completes" step i of the funnel if they fired event i after completing step i-1 (events must occur in order but not necessarily consecutively). Compute the number of unique users who reached each step.
python
def funnel_count(
events: list[tuple[int, str, int]], # (user_id, event, timestamp)
funnel: list[str]
) -> list[int]:
"""Return list of user counts at each funnel step."""
pass
**Input**:
events = [(1,"view",1),(1,"click",2),(1,"purchase",3),
(2,"view",1),(2,"click",4),
(3,"view",2)]
funnel = ["view", "click", "purchase"]
**Output**: [3, 2, 1]
# All 3 users reached step 1 (view)
# Users 1,2 reached step 2 (click)
# Only user 1 reached step 3 (purchase)
Follow-ups
- How do you write this as a SQL query using self-joins or window functions?
- If the funnel must be completed within a time window (e.g., 7 days), how does your logic change?
- How would you compute conversion rates and visualize the drop-off percentages?
- Extend to support optional funnel steps that are counted but do not block progression.
Full Details
Problem
You have a list of user events (user_id, event_name, timestamp). A conversion funnel is defined as an ordered list of event names. A user "completes" step i of the funnel if they fired event i after completing step i-1 (events must occur in order but not necessarily consecutively). Compute the number of unique users who reached each step.
python
def funnel_count(
events: list[tuple[int, str, int]], # (user_id, event, timestamp)
funnel: list[str]
) -> list[int]:
"""Return list of user counts at each funnel step."""
pass
**Input**:
events = [(1,"view",1),(1,"click",2),(1,"purchase",3),
(2,"view",1),(2,"click",4),
(3,"view",2)]
funnel = ["view", "click", "purchase"]
**Output**: [3, 2, 1]
# All 3 users reached step 1 (view)
# Users 1,2 reached step 2 (click)
# Only user 1 reached step 3 (purchase)
Follow-ups
- How do you write this as a SQL query using self-joins or window functions?
- If the funnel must be completed within a time window (e.g., 7 days), how does your logic change?
- How would you compute conversion rates and visualize the drop-off percentages?
- Extend to support optional funnel steps that are counted but do not block progression.
About This Question
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How To Practice This Type of Question
Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.
Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Faire reports consistently are the ones worth investing in; one-off niche problems are not.
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Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.
The single most predictive failure mode in Faire reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.