The Number of Users That Are Eligible for Discount

The Number of Users That Are Eligible for Discount

What is this problem about?

Marketing campaigns often target specific groups of users based on their engagement or purchase history. "The Number of Users That Are Eligible for Discount" is a database problem where you are asked to identify users who meet a specific set of criteria (such as minimum spending or a specific time range) and return the total count of such users. This is a fundamental task for generating mailing lists or applying promotional credits in a business environment.

Why is this asked in interviews?

This the Number of Users That Are Eligible for Discount interview question is commonly asked in data-focused roles, such as at Analytics quotient. It tests your ability to write clean SQL with specific filtering conditions (WHERE) and aggregate functions (COUNT). It also evaluates whether you can handle date and time constraints effectively, which are common in real-world business scenarios.

Algorithmic pattern used

The problem follows the Database interview pattern.

1. Use the COUNT() function to aggregate the results.
2. Apply the WHERE clause to filter the users based on the criteria provided (e.g., spending > threshold AND last_purchase_date BETWEEN date1 AND date2).
3. Ensure that you are counting unique users if the underlying table contains multiple records per user (using COUNT(DISTINCT user_id)).

Example explanation

Criteria: Users who spent more than $100 in January 2023. Table:

• User 1: $150 on 2023-01-10
• User 2: $50 on 2023-01-15
• User 3: $200 on 2023-02-01 (Not in Jan)
• User 1: $20 on 2023-01-20

1. Filtering:
   • User 1 ($150) meets criteria.
   • User 1 ($20) is irrelevant (it's the same user).
2. Result: Only User 1 and potentially others from the table who meet the criteria. The count of unique users is returned.

Common mistakes candidates make

In "The Number of Users That Are Eligible for Discount coding problem," a common mistake is forgetting to handle null values if the spending or date columns are nullable. Another error is not correctly implementing the date range, such as accidentally excluding the last day of the month or including records from the wrong year. Using COUNT(*) instead of COUNT(DISTINCT user_id) is also a frequent mistake when the data has duplicate user entries.

Interview preparation tip

Always clarify the boundary conditions of the "eligibility" criteria. Does "more than" mean strictly greater than or greater than or equal to? When dealing with dates, be careful about the time component (e.g., is '2023-01-31' the same as '2023-01-31 23:59:59'?). Practice basic SQL aggregations until they are second nature.