LeetCode #3716 — MEDIUM

Find Churn Risk Customers

Move from brute-force thinking to an efficient approach using core interview patterns strategy.

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The Problem

Problem Statement

Table: subscription_events

+------------------+---------+
| Column Name      | Type    | 
+------------------+---------+
| event_id         | int     |
| user_id          | int     |
| event_date       | date    |
| event_type       | varchar |
| plan_name        | varchar |
| monthly_amount   | decimal |
+------------------+---------+
event_id is the unique identifier for this table.
event_type can be start, upgrade, downgrade, or cancel.
plan_name can be basic, standard, premium, or NULL (when event_type is cancel).
monthly_amount represents the monthly subscription cost after this event.
For cancel events, monthly_amount is 0.

Write a solution to Find Churn Risk Customers - users who show warning signs before churning. A user is considered churn risk customer if they meet ALL the following criteria:

  • Currently have an active subscription (their last event is not cancel).
  • Have performed at least one downgrade in their subscription history.
  • Their current plan revenue is less than 50% of their historical maximum plan revenue.
  • Have been a subscriber for at least 60 days.

Return the result table ordered by days_as_subscriber in descending order, then by user_id in ascending order.

The result format is in the following example.

Example:

Input:

subscription_events table:

+----------+---------+------------+------------+-----------+----------------+
| event_id | user_id | event_date | event_type | plan_name | monthly_amount |
+----------+---------+------------+------------+-----------+----------------+
| 1        | 501     | 2024-01-01 | start      | premium   | 29.99          |
| 2        | 501     | 2024-02-15 | downgrade  | standard  | 19.99          |
| 3        | 501     | 2024-03-20 | downgrade  | basic     | 9.99           |
| 4        | 502     | 2024-01-05 | start      | standard  | 19.99          |
| 5        | 502     | 2024-02-10 | upgrade    | premium   | 29.99          |
| 6        | 502     | 2024-03-15 | downgrade  | basic     | 9.99           |
| 7        | 503     | 2024-01-10 | start      | basic     | 9.99           |
| 8        | 503     | 2024-02-20 | upgrade    | standard  | 19.99          |
| 9        | 503     | 2024-03-25 | upgrade    | premium   | 29.99          |
| 10       | 504     | 2024-01-15 | start      | premium   | 29.99          |
| 11       | 504     | 2024-03-01 | downgrade  | standard  | 19.99          |
| 12       | 504     | 2024-03-30 | cancel     | NULL      | 0.00           |
| 13       | 505     | 2024-02-01 | start      | basic     | 9.99           |
| 14       | 505     | 2024-02-28 | upgrade    | standard  | 19.99          |
| 15       | 506     | 2024-01-20 | start      | premium   | 29.99          |
| 16       | 506     | 2024-03-10 | downgrade  | basic     | 9.99           |
+----------+---------+------------+------------+-----------+----------------+

Output:

+----------+--------------+------------------------+-----------------------+--------------------+
| user_id  | current_plan | current_monthly_amount | max_historical_amount | days_as_subscriber |
+----------+--------------+------------------------+-----------------------+--------------------+
| 501      | basic        | 9.99                   | 29.99                 | 79                 |
| 502      | basic        | 9.99                   | 29.99                 | 69                 |
+----------+--------------+------------------------+-----------------------+--------------------+

Explanation:

  • User 501:
    • Currently active: Last event is downgrade to basic (not cancelled) 
    • Has downgrades: Yes, 2 downgrades in history 
    • Current revenue (9.99) vs max (29.99): 9.99/29.99 = 33.3% (less than 50%) 
    • Days as subscriber: Jan 1 to Mar 20 = 79 days (at least 60) 
    • Result: Churn Risk Customer
  • User 502:
    • Currently active: Last event is downgrade to basic (not cancelled) 
    • Has downgrades: Yes, 1 downgrade in history 
    • Current revenue (9.99) vs max (29.99): 9.99/29.99 = 33.3% (less than 50%) 
    • Days as subscriber: Jan 5 to Mar 15 = 70 days (at least 60) 
    • Result: Churn Risk Customer
  • User 503:
    • Currently active: Last event is upgrade to premium (not cancelled) 
    • Has downgrades: No downgrades in history 
    • Result: Not at-risk (no downgrade history)
  • User 504:
    • Currently active: Last event is cancel
    • Result: Not at-risk (subscription cancelled)
  • User 505:
    • Currently active: Last event is 'upgrade' to standard (not cancelled) 
    • Has downgrades: No downgrades in history 
    • Result: Not at-risk (no downgrade history)
  • User 506:
    • Currently active: Last event is downgrade to basic (not cancelled) 
    • Has downgrades: Yes, 1 downgrade in history 
    • Current revenue (9.99) vs max (29.99): 9.99/29.99 = 33.3% (less than 50%) 
    • Days as subscriber: Jan 20 to Mar 10 = 50 days (less than 60) 
    • Result: Not at-risk (insufficient subscription duration)

Result table is ordered by days_as_subscriber DESC, then user_id ASC.

Note: days_as_subscriber is calculated from the first event date to the last event date for each user.

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: Table: subscription_events +------------------+---------+ | Column Name | Type | +------------------+---------+ | event_id | int | | user_id | int | | event_date | date | | event_type | varchar | | plan_name | varchar | | monthly_amount | decimal | +------------------+---------+ event_id is the unique identifier for this table. event_type can be start, upgrade, downgrade, or cancel. plan_name can be basic, standard, premium, or NULL (when event_type is cancel). monthly_amount represents the monthly subscription cost after this event. For cancel events, monthly_amount is 0. Write a solution to Find Churn Risk Customers - users who show warning signs before churning. A user is considered churn risk customer if they meet ALL the following criteria: Currently have an active subscription (their last event is not cancel). Have performed at least one downgrade in their subscription history.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

{"headers":{"subscription_events":["event_id","user_id","event_date","event_type","plan_name","monthly_amount"]},"rows":{"subscription_events":[[1,501,"2024-01-01","start","premium",29.99],[2,501,"2024-02-15","downgrade","standard",19.99],[3,501,"2024-03-20","downgrade","basic",9.99],[4,502,"2024-01-05","start","standard",19.99],[5,502,"2024-02-10","upgrade","premium",29.99],[6,502,"2024-03-15","downgrade","basic",9.99],[7,503,"2024-01-10","start","basic",9.99],[8,503,"2024-02-20","upgrade","standard",19.99],[9,503,"2024-03-25","upgrade","premium",29.99],[10,504,"2024-01-15","start","premium",29.99],[11,504,"2024-03-01","downgrade","standard",19.99],[12,504,"2024-03-30","cancel",null,0.00],[13,505,"2024-02-01","start","basic",9.99],[14,505,"2024-02-28","upgrade","standard",19.99],[15,506,"2024-01-20","start","premium",29.99],[16,506,"2024-03-10","downgrade","basic",9.99]]}}
Step 02

Core Insight

What unlocks the optimal approach

  • No official hints in dataset. Start from constraints and look for a monotonic or reusable state.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. Continue until all input is consumed.
Use the first example testcase as your mental trace to verify each transition.
Step 04

Edge Cases

Minimum Input
Single element / shortest valid input
Validate boundary behavior before entering the main loop or recursion.
Duplicates & Repeats
Repeated values / repeated states
Decide whether duplicates should be merged, skipped, or counted explicitly.
Extreme Constraints
Upper-end input sizes
Re-check complexity target against constraints to avoid time-limit issues.
Invalid / Corner Shape
Empty collections, zeros, or disconnected structures
Handle special-case structure before the core algorithm path.
Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3716: Find Churn Risk Customers
// Auto-generated Java example from py.
class Solution {
    public void exampleSolution() {
    }
}
// Reference (py):
// # Accepted solution for LeetCode #3716: Find Churn Risk Customers
// import pandas as pd
// 
// 
// def find_churn_risk_customers(subscription_events: pd.DataFrame) -> pd.DataFrame:
//     subscription_events["event_date"] = pd.to_datetime(
//         subscription_events["event_date"]
//     )
//     subscription_events = subscription_events.sort_values(
//         ["user_id", "event_date", "event_id"]
//     )
//     last_events = (
//         subscription_events.groupby("user_id")
//         .tail(1)[["user_id", "event_type", "plan_name", "monthly_amount"]]
//         .rename(
//             columns={
//                 "event_type": "last_event_type",
//                 "plan_name": "current_plan",
//                 "monthly_amount": "current_monthly_amount",
//             }
//         )
//     )
// 
//     agg_df = (
//         subscription_events.groupby("user_id")
//         .agg(
//             start_date=("event_date", "min"),
//             last_event_date=("event_date", "max"),
//             max_historical_amount=("monthly_amount", "max"),
//             downgrade_count=("event_type", lambda x: (x == "downgrade").sum()),
//         )
//         .reset_index()
//     )
// 
//     merged = pd.merge(agg_df, last_events, on="user_id", how="inner")
//     merged["days_as_subscriber"] = (
//         merged["last_event_date"] - merged["start_date"]
//     ).dt.days
// 
//     result = merged[
//         (merged["last_event_type"] != "cancel")
//         & (merged["downgrade_count"] >= 1)
//         & (merged["current_monthly_amount"] < 0.5 * merged["max_historical_amount"])
//         & (merged["days_as_subscriber"] >= 60)
//     ][
//         [
//             "user_id",
//             "current_plan",
//             "current_monthly_amount",
//             "max_historical_amount",
//             "days_as_subscriber",
//         ]
//     ]
// 
//     result = result.sort_values(
//         ["days_as_subscriber", "user_id"], ascending=[False, True]
//     ).reset_index(drop=True)
//     return result
Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n)
Space
O(1)

Approach Breakdown

BRUTE FORCE
O(n²) time
O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED
O(n) time
O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.
Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.