How do you solve Analyze Subscription Conversion on LeetCode?

Analyze Subscription Conversion (LeetCode #3497) 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: Users who convert show different engagement patterns.

What is the time complexity of Analyze Subscription Conversion ?

The optimal solution for Analyze Subscription Conversion runs in O(n) time and uses O(n) space. Single pass through data allows for linear complexity; space used for storing user data.

What is the brute force solution for Analyze Subscription Conversion ?

The brute force approach for Analyze Subscription Conversion is Brute Force, with O(n²) time complexity and O(1) space complexity. Check each user's activity for free trial and paid subscription periods separately. Calculate averages by iterating through all records for each user. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Analyze Subscription Conversion ?

Analyze Subscription Conversion uses the following concepts: Database. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Analyze Subscription Conversion ?

Explain your thought process clearly. Use examples to illustrate your approach. Be prepared to discuss trade-offs between brute force and optimal solutions.

What are common mistakes when solving Analyze Subscription Conversion ?

Not considering users who only canceled without converting. Forgetting to round averages to two decimal places.

#3497

Analyze Subscription Conversion

Medium

DatabaseHash 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 aggregate activity data for each user, storing results in a hash map for efficient lookups.

⚙️

Algorithm

3 steps

1Step 1: Iterate through UserActivity and store total duration and counts for free trial and paid subscription in a hash map.
2Step 2: After processing, calculate averages using stored totals and counts.
3Step 3: Return results for users who converted from free trial to paid.

solution.py7 lines

1# Full working Python code
2SELECT user_id,
3       ROUND(SUM(CASE WHEN activity_type = 'free_trial' THEN activity_duration END) / COUNT(CASE WHEN activity_type = 'free_trial' THEN 1 END), 2) AS avg_free_trial,
4       ROUND(SUM(CASE WHEN activity_type = 'paid' THEN activity_duration END) / COUNT(CASE WHEN activity_type = 'paid' THEN 1 END), 2) AS avg_paid
5FROM UserActivity
6WHERE user_id IN (SELECT user_id FROM UserActivity WHERE activity_type = 'paid')
7GROUP BY user_id;

ℹ

Complexity note: Single pass through data allows for linear complexity; space used for storing user data.

1Users who convert show different engagement patterns.
2Tracking activity duration can inform retention strategies.

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