At Progressive, SQL is used for analyzing customer behavior, including driving habits and accident history, to optimize insurance rates, as well as managing extensive vehicle data, including vehicle make and model, for accurate claim assessments. Because of this, Progressive often includes SQL questions during interviews for Data Analyst, Data Science, and BI jobs.
To help you prepare for the Progressive SQL interview, we've collected 8 Progressive SQL interview questions in this blog.
Progressive is an insurance company and major part of their business is understanding about their customer's policy interactions. Write a SQL query to calculate the monthly average premium for each insurance policy type for the year 2022 using the policies
table.
The policies
table has the following schema:
policy_id | customer_id | start_date | premium | policy_type |
---|---|---|---|---|
1281 | 23 | 01/02/2022 | 500.5 | Car |
5645 | 97 | 04/15/2022 | 1320.0 | Home |
3956 | 117 | 02/22/2022 | 250.4 | Motorcycle |
5138 | 89 | 01/09/2022 | 800.6 | Car |
6754 | 210 | 03/04/2022 | 740.8 | Home |
Where:
policy_id
is the id of the policy.customer_id
is the id of the customer.start_date
is the start date of the policy.premium
is the monthly premium for the policy.policy_type
is the type of policy (i.e., Car, Home, Motorcycle, etc.)month | policy_type | average_premium |
---|---|---|
1 | Car | 650.55 |
2 | Motorcycle | 250.40 |
3 | Home | 740.80 |
4 | Home | 1320.0 |
To solve this problem, a PostgreSQL SQL query using window functions would be appropriate. Here is the suitable query:
SELECT EXTRACT(MONTH FROM start_date) AS month, policy_type, AVG(premium) OVER (PARTITION BY EXTRACT(MONTH FROM start_date), policy_type) AS average_premium FROM policies WHERE EXTRACT(YEAR FROM start_date) = 2022 ORDER BY month, policy_type;
In this query, the window function AVG()
is used to calculate the average premium for each policy type for each month of the year 2022. The PARTITION BY
clause is used to specify the window, which includes all rows that have the same month and policy type values.
To solve a similar window function interview problem which uses RANK() on DataLemur's free online SQL code editor, solve this Amazon SQL question asked in a BI Engineer interview:
You're given a table of Progressive employee and department salary data. Write a SQL query to find departments where the departments average salary is higher than the company's average salary.
Solve this question directly within the browser on DataLemur:
The solution is LONG – 30+ lines of SQL. You can find a step-by-step solution with hints here: Department vs. Company Salary.
An inner join only includes rows from both tables that satisfy the join condition, whereas a full outer join includes all rows from both tables, regardless of whether they match the join condition or not.
For example, suppose you had a table of Progressive orders and Progressive customers.
INNER JOIN: When there is a match in the shared key or keys, rows from both tables are retrieved. In this example, an INNER JOIN
between the Orders and Customers tables would retrieve rows where the customer_id
in the Orders table matches the customer_id
in the Customers table.
FULL OUTER JOIN: A FULL OUTER JOIN
retrieves all rows from both tables, regardless of whether there is a match in the shared key or keys. If there is no match, NULL values will be returned for the columns of the non-matching table.
Progressive is an American insurance company that offers several types of personal and commercial auto insurances along with other insurances like Home, Renters, Condo, etc. They need to understand their customers better by analyzing their customer records database. They are interested in knowing how many customers with a driver's license age more than five years have bought auto insurance and do not have any accident records in the past three years.
Given a customers table with the following information:
customers
Example Input:customer_id | name | license_age | accident_records |
---|---|---|---|
4357 | John | 6 | 0 |
6298 | Oliver | 1 | 0 |
9753 | Mia | 10 | 1 |
2652 | Emma | 11 | 0 |
4571 | Noah | 3 | 2 |
Thelicense_age
column represents how many years the customer has held their driver's license, and the accident_records
column represents the number of accident records the customer had in the past three years.
Write a SQL query to retrieve all customers' names and IDs who have had a driver's license for more than five years and have not had any accidents in the past three years.
SELECT customer_id, name FROM customers WHERE license_age > 5 AND accident_records = 0;
This query first filters the customers
table to only include rows where the customer's license_age
is greater than 5 years and the number of accident_records
in the past three years is 0. It then returns the customer_id
and name
of these customers.
A self-join is a type of JOIN where a table is joined to itself. To execute a self-join, you must include the table name twice in the FROM clause and assign a different alias to each instance. You can then join the two copies of the table using a JOIN clause, and use a WHERE clause to specify the relationship between the rows.
One common use case for self-joins is to compare data within a single table. For example, you might use a self-join to compare the salaries of employees within the same department, or to identify pairs of products that are frequently purchased together (like in this real Walmart SQL interview question)[https://datalemur.com/questions/frequently-purchased-pairs].
For a more concrete example, imagine you had website visitor data for Progressive, exported from the company's Google Analytics account. To help the web-dev team, you needed to analyze pairs of pages for UX or navigational issues. As part of that analysis, you wanted to produce all pairs of URLs, but needed to exclude pairs where both URLs were the same since that is not a valid pair.
You could use the following self-join:
SELECT page1.url AS page_url, page2.url AS referred_from FROM google_analytics AS page1 JOIN google_analytics AS page2 ON page1.referrer_id = page2.id WHERE page1.id <> page2.id;
This query retrieves the url of each page (page1.url
) along with the url of the page that referred to it (page2.url
). The self-join is performed using the referrer_id field
, which identifies the id of the page that referred the visitor to the current page, and excludes any pages that referred to themselves (i.e., data anomalies since pages shouldn't refer to themself).
You are working as a Data Analyst at Progressive, a company that offers various types of insurance policies. You want to analyze the average cost of insurance policies for different vehicle types (cars, motorcycles, trucks, etc.) on a monthly basis.
The insurance_policies
table in your database has the following structure:
insurance_policies
Example Input:policy_id | vehicle_type | issue_date | cost |
---|---|---|---|
1021 | Car | 06/08/2022 | 1200.00 |
2043 | Motorcycle | 06/10/2022 | 800.00 |
3001 | Car | 06/18/2022 | 1350.00 |
4101 | Truck | 07/26/2022 | 1600.00 |
5321 | Car | 07/05/2022 | 1299.00 |
You are interested in obtaining data that gives the average cost of policies for different vehicle types, summarized by month.
month | vehicle_type | average_cost |
---|---|---|
6 | Car | 1275.00 |
6 | Motorcycle | 800.00 |
7 | Car | 1299.00 |
7 | Truck | 1600.00 |
SELECT EXTRACT(MONTH FROM issue_date) AS month, vehicle_type, AVG(cost) AS average_cost FROM insurance_policies GROUP BY month, vehicle_type ORDER BY month, vehicle_type;
This query uses PostgreSQL's EXTRACT
function to obtain the month from the issue_date
timestamp. It groups the records by the month and the vehicle type, and for each combination, it calculates the average cost of the insurance policies. The ORDER BY
clause ensures that the output is sorted by the month and the vehicle type.
To explain the difference between a primary key and foreign key, let's start with some data from Progressive's marketing analytics database which stores data from Google Ads campaigns:
progressive_ads_data
:ad_id | campaign_id | keyword | click_count |
---|---|---|---|
1 | 100 | Progressive pricing | 10 |
2 | 100 | Progressive reviews | 15 |
3 | 101 | Progressive alternatives | 7 |
4 | 101 | buy Progressive | 12 |
In this table, ad_id
could be a primary key. It is unique for each row in the table and cannot contain null values.
campaign_id
could be a foreign key. It references the campaign_id
of the Google Ads campaign that each ad belongs to, establishing a relationship between the ads and their campaigns. This foreign key allows you to easily query the table to find out which ads belong to a specific campaign, or to find out which campaigns a specific ad belongs to.
It is also possible for a table to have multiple foreign keys that reference different primary keys in other tables. For example, the progressive_ads_data
table could have additional foreign keys for the ad_group_id
of the ad group that each ad belongs to, and the account_id
of the Google Ads account that the campaigns belong to.
You are given two tables, policies
and claims
. The policies
table includes details of each policy sold, such as policy_id
, customer_id
, product_code
(representing the type of policy), and start_date
(representing when the policy became effective). The claims
table includes details about each claim filed, such as claim_id
, policy_id
, claim_amount
, and claim_date
.
A very common analysis is to understand the total claim amount for each type of policy. Write a SQL query that combines the two tables and shows the total claim amount for each type of policy.
policies
Example Input:policy_id | customer_id | product_code | start_date |
---|---|---|---|
101 | 2001 | A | 2022-01-01 |
102 | 2002 | B | 2022-02-01 |
103 | 2003 | A | 2022-02-01 |
104 | 2004 | C | 2022-03-01 |
105 | 2005 | B | 2022-03-02 |
claims
Example Input:claim_id | policy_id | claim_amount | claim_date |
---|---|---|---|
1 | 101 | 500 | 2022-01-20 |
2 | 103 | 1500 | 2022-02-15 |
3 | 102 | 700 | 2022-02-20 |
4 | 104 | 300 | 2022-03-10 |
5 | 105 | 900 | 2022-03-25 |
SELECT p.product_code, SUM(c.claim_amount) AS total_claim_amount FROM policies p JOIN claims c ON p.policy_id = c.policy_id GROUP BY p.product_code;
This query first joins the policies
and claims
tables on the policy_id
column. Then it groups the result by product_code
, and for each product_code
, it adds up the claim_amount
to get the total claim amount.
Because join questions come up so often during SQL interviews, try an interactive Spotify JOIN SQL question:
Assuming that you've already got basic SQL skills, the next best tip we have to prepare for the Progressive SQL interview is to solve as many practice SQL interview questions as you can! Besides solving the above Progressive SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like Google, Uber, and Microsoft.
Each exercise has hints to guide you, full answers and best of all, there's an online SQL coding environment so you can right online code up your SQL query and have it checked.
To prep for the Progressive SQL interview it is also a great idea to practice interview questions from other insurance companies like:
Stay ahead of the curve with Progressive's latest announcements and updates!
In case your SQL foundations are weak, don't worry about jumping right into solving questions – strengthen your SQL foundations with this SQL tutorial for Data Scientists & Analysts.
This tutorial covers SQL concepts such as filtering with LIKE and manipulating string/text data – both of which come up often during Progressive SQL interviews.
In addition to SQL interview questions, the other types of questions tested in the Progressive Data Science Interview are:
To prepare for the Progressive Data Science interview make sure you have a deep understanding of the company's values and mission – this will be key to acing the behavioral interview. For the technical Data Science interviews, get ready by reading Ace the Data Science Interview. The book's got: