Measure Customers Ordering from Bottom-Quartile Restaurants

Question

The question asks you to measure the number of customers who are ordering from restaurants that fall within the bottom quartile based on their performance metrics.

Codemia · Accepted Answer

To measure the number of customers ordering from bottom-quartile restaurants, we first need to define what constitutes a bottom quartile. In statistical terms, the bottom quartile represents the lowest 25% of data points in a dataset. For this analysis, we will consider performance metrics such as customer ratings, order volume, or delivery times to determine which restaurants fall into this category.

We will approach the problem in a few steps: First, we will gather the relevant data from our database, which may include restaurant IDs, performance metrics, and customer order data. Next, we will calculate the quartiles for the performance metrics to identify the cutoff for the bottom quartile. Finally, we will filter the customer orders to count how many orders were placed at these bottom-quartile restaurants.

In SQL, we can use the `NTILE` function to calculate quartiles. For example:

```sql
WITH RestaurantPerformance AS (
    SELECT restaurant_id, AVG(rating) AS avg_rating
    FROM Orders
    GROUP BY restaurant_id
),
Quartiles AS (
    SELECT restaurant_id, avg_rating, NTILE(4) OVER (ORDER BY avg_rating) AS quartile
    FROM RestaurantPerformance
)
SELECT COUNT(*) AS customer_orders
FROM Orders
WHERE restaurant_id IN (SELECT restaurant_id FROM Quartiles WHERE quartile = 1);
```
This SQL query calculates the average rating for each restaurant, determines the quartile, and counts the orders from restaurants in the bottom quartile.

When performing this analysis, we must consider the trade-offs of using different performance metrics. For instance, using average ratings may not fully capture restaurant performance if there are outliers. Additionally, if the dataset is large, we may need to optimize our queries to ensure they run efficiently, possibly by indexing key columns or using data sampling techniques.

Measure Customers Ordering from Bottom-Quartile Restaurants

DoorDash

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Sharpen Your Skills on Codemia

Sample Answer

Requirements

High-Level Design

Submit Your Answer

DoorDash Data Scientist Interview Guide

Related Questions

Optimize a slow query on products

Calculate conversion rate per user from orders

Window function: rank over sessions

Optimize a slow query on orders

Write a query to find time to first action from sessions