How do you solve Find Churn Risk Customers on LeetCode?

Find Churn Risk Customers (LeetCode #3716) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Active users are at risk if they downgrade and their revenue drops significantly.

What is the time complexity of Find Churn Risk Customers?

The optimal solution for Find Churn Risk Customers runs in O(n) time and uses O(n) space. Single pass through events allows efficient data collection.

What is the brute force solution for Find Churn Risk Customers?

The brute force approach for Find Churn Risk Customers is Brute Force, with O(n²) time complexity and O(1) space complexity. Check each user individually to see if they meet all churn risk criteria. This is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Find Churn Risk Customers?

Clarify the problem statement and constraints. Discuss edge cases and how to handle them. Explain your thought process clearly.

What are common mistakes when solving Find Churn Risk Customers?

Ignoring users with no downgrades. Not checking the last event type correctly.

#3716

Find Churn Risk Customers

Medium

Hash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

Use a single pass to collect user data and determine churn risk efficiently.

⚙️

Algorithm

3 steps

1Step 1: Create a dictionary to track user data (last event, downgrades, max revenue).
2Step 2: Iterate through events, updating user data accordingly.
3Step 3: After processing, filter users based on churn risk criteria.

solution.py11 lines

1def find_churn_risk_customers(events):
2    user_data = {}
3    for event in events:
4        user_id = event['user_id']
5        if user_id not in user_data:
6            user_data[user_id] = {'last_event': '', 'downgrades': 0, 'max_revenue': 0}
7        user_data[user_id]['last_event'] = event['event_type']
8        if event['event_type'] == 'downgrade':
9            user_data[user_id]['downgrades'] += 1
10        user_data[user_id]['max_revenue'] = max(user_data[user_id]['max_revenue'], event['monthly_amount'])
11    return [user for user, data in user_data.items() if data['last_event'] != 'cancel' and data['downgrades'] > 0 and data['max_revenue'] > 0 and (data['max_revenue'] / 2) > data['max_revenue']]

ℹ

Complexity note: Single pass through events allows efficient data collection.

1Active users are at risk if they downgrade and their revenue drops significantly.
2Monitoring user events can help predict churn effectively.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.