At Interpublic Group employees write SQL queries to help them analyze marketing data patterns to help them customize marketing initiatives to fit the customer profile. Unsurprisingly this is why Interpublic Group often tests SQL problems during interviews for Data Science and Data Engineering positions.
To help you study for the Interpublic Group SQL interview, here's 11 Interpublic Group SQL interview questions in this article.
For the company 'Interpublic Group', which offers advertising and marketing services, a 'whale' customer could be one who initiates numerous campaigns or whose ad spend is consistently high.
Write a SQL query to retrieve the customers who have either launched more than five marketing campaigns in the last six months or who have an average ad spend of over $10,000 per month for the same period.
You have two tables - and .
campaign_id | customer_id | launch_date |
---|---|---|
101 | 1001 | 02/01/2022 |
102 | 1001 | 02/15/2022 |
103 | 1002 | 03/07/2022 |
104 | 1002 | 03/15/2022 |
105 | 1002 | 04/15/2022 |
106 | 1003 | 04/20/2022 |
107 | 1003 | 05/15/2022 |
108 | 1001 | 05/30/2022 |
109 | 1002 | 06/10/2022 |
110 | 1001 | 06/15/2022 |
customer_id | month_year | amount_spent |
---|---|---|
1001 | February 2022 | $4000 |
1002 | March 2022 | $5000 |
1001 | March 2022 | $6000 |
1003 | April 2022 | $20000 |
1001 | April 2022 | $7000 |
1001 | May 2022 | $13000 |
1002 | May 2022 | $12000 |
1001 | June 2022 | $16000 |
1002 | June 2022 | $15000 |
1003 | June 2022 | $18000 |
This query first creates two common table expressions (CTEs), and to count the number of campaigns and calculate the average ad spend for each customer over the past six months, respectively. Using a FULL OUTER JOIN, it then combines the CTEs and filters the data for customers with more than five campaigns or an average ad spend of more than $10,000. The COALESCE function ensures that each customer ID is displayed even if it only appears in one of the CTEs.
To work on another SQL customer analytics question where you can solve it right in the browser and have your SQL query automatically checked, try this Walmart SQL Interview Question:
Assume there was a table of Interpublic Group employee salaries. Write a SQL query to find the employees who earn more than their direct manager.
employee_id | name | salary | department_id | manager_id |
---|---|---|---|---|
1 | Emma Thompson | 3800 | 1 | |
2 | Daniel Rodriguez | 2230 | 1 | 10 |
3 | Olivia Smith | 8000 | 1 | 8 |
4 | Noah Johnson | 6800 | 2 | 8 |
5 | Sophia Martinez | 1750 | 1 | 10 |
8 | William Davis | 7000 | 2 | NULL |
10 | James Anderson | 4000 | 1 | NULL |
employee_id | employee_name |
---|---|
3 | Olivia Smith |
This is the output because Olivia Smith earns $8,000, surpassing her manager, William Davis who earns 7,800.
Check your SQL query for this problem directly within the browser on DataLemur:
First, we perform a SELF-JOIN where we treat the first table () as the managers' table and the second table () as the employees' table. Then we use a clause to filter the results, ensuring we only get employees whose salaries are higher than their manager's salary.
If the solution above is hard to understand, you can find a step-by-step solution with hints here: Well Paid Employees.
A DBMS (database management system), in order to ensure transactions are relaible and don't ruin the integrity of the data, tries to mantain the following ACID properties: Atomicity, Consistency, Isolation, and Durability.
To make this concept more concrete, here is what each of the ACID properties would mean in the context of banking transactions:
As an ad agency, Interpublic Group continuously monitors the performance of its employees in various advertising projects. Suppose they have an table that contains details of each project completed by employees. For each employee and project, they record the completion date and a score reflecting the quality, creativity, and impact of the work on a scale from 1 to 10.
Write a PostgreSQL query to find the average performance score for each employee over their last three projects. If an employee worked on fewer than three projects, evaluate their average performance score based on those projects.
employee_id | project_id | completion_date | performance_score |
---|---|---|---|
1001 | 5001 | 01/10/2022 | 8 |
1001 | 5002 | 05/02/2022 | 9 |
1001 | 5003 | 15/09/2022 | 7 |
1002 | 6010 | 07/05/2022 | 9 |
1002 | 6011 | 23/07/2022 | 8 |
1003 | 7100 | 06/04/2022 | 7 |
employee_id | average_performance_score |
---|---|
1001 | 8.0 |
1002 | 8.5 |
1003 | 7.0 |
The PostgreSQL query above uses a window function to compute the average () performance score for each employee over their last three projects. The clause is used to divide the data into partitions based on . Within each partition, rows are ordered by , and the clause specifies that for each row, the average performance score is calculated over the current row and the two preceding rows, representing the last three projects. If there are less than three projects, it calculates the average over available projects, due to the flexible nature of window functions.
p.s. Window functions show up pretty often during SQL interviews, so practice the 27+ window function questions on DataLemur
A database index is a way to optimize the performance of a database by reducing the amount of data that needs to be searched to retrieve a record.
There are several types of indexes:
Assuming that Interpublic Group, being an advertising company, has run several advertising campaigns across different channels for its clients. Your task is to craft a SQL query that retrieves the total cost and total impressions for each client's campaign on each advertising channel.
Let's consider two tables, and , as outlined below:
campaign_id | client_id | channel | campaign_name | cost |
---|---|---|---|---|
1 | 1001 | TV | Spring Sale | 500000 |
2 | 1001 | Digital | Summer Launch | 200000 |
3 | 1002 | Radio | End of Year Clearance | 300000 |
4 | 1002 | TV | Black Friday | 400000 |
impression_id | campaign_id | impressions |
---|---|---|
10 | 1 | 5000000 |
11 | 2 | 1000000 |
12 | 3 | 2000000 |
13 | 4 | 8000000 |
Using these tables, write a PostgreSQL query to retrieve the total cost and total impressions for each client's campaign on each advertising channel.
This query joins the campaigns and impressions tables based on the campaign_id. It then groups the results by client_id, channel, campaign_name, and cost. The total impressions per campaign are calculated using the SUM aggregate function.
client_id | channel | campaign_name | cost | total_impressions |
---|---|---|---|---|
1001 | TV | Spring Sale | 500000 | 5000000 |
1001 | Digital | Summer Launch | 200000 | 1000000 |
1002 | Radio | End of Year Clearance | 300000 | 2000000 |
1002 | TV | Black Friday | 400000 | 8000000 |
To discover records in one table that are not present in another, you can utilize a and filter out any values in the right-side table.
For example, say you had a table of Interpublic Group customers and a 2nd table of all purchases made with Interpublic Group. To find all customers who did not make a purchase, you'd use the following
This query fetches all rows from the customers table, along with any rows that match in the purchases table. If there is no matching row in the purchases table, values will be returned for all of the right table's columns. The clause then filters out any rows where the purchases.id column is NULL, leaving only customers who have not made a purchase.
As a Data Analyst at Interpublic Group, your task is to analyze the click-through-rate (CTR) of digital ads. CTR is a highly important metric for assessing the effectiveness of a marketing campaign. You have been given two tables - 'Ad_impressions' and 'Ad_clicks'. The 'Ad_impressions' table includes each time an ad is displayed to a user, while 'Ad_clicks' tracks each time an ad is clicked by a user.
You are tasked with writing a SQL query that calculates the CTR for each ad_id as a ratio of the total number of clicks over the total number of impressions.
In the above query, we first create an between Ad_clicks and Ad_impressions on the and fields. Then, we use the function to calculate the total count of click events and impression events per each . By dividing these values, we calculate the Click-through-rate (CTR). The casting is to ensure a fractional output instead of integer division.
To practice a related problem on DataLemur's free interactive SQL code editor, try this Meta SQL interview question:
As part of Interpublic Group, an advertising company, your task is to analyse ad campaigns of our clients. Given a table listing ad impressions (times an ad was displayed) and clicks (times an ad was clicked), can you write a SQL query to retrieve the campaign name, the total impressions and clicks, and calculate the click-through-rate (CTR) for each campaign?
Provide the results sorted by CTR in descending order.
campaign_id | campaign_name | impressions | clicks |
---|---|---|---|
101 | Campaign A | 5000 | 400 |
202 | Campaign B | 7500 | 330 |
303 | Campaign C | 2100 | 200 |
404 | Campaign D | 9000 | 300 |
505 | Campaign E | 3500 | 220 |
Here is your answer written in PostgresSQL:
This query groups the data by campaign name, then it sums up the impressions and clicks. The click-through-rate (CTR) is calculated as (total clicks / total impressions) * 100. The resulting rows are then ordered by the CTR in descending order so we can instantly see which campaign has the highest effectiveness in terms of the percentage of impressions that lead to clicks.
In a database, constraints are rules that the DBMS (database management system) follows when performing operations such as inserting, updating, or deleting data.
For example, consider a table of employee records at Interpublic Group. Some constraints that you might want to implement include:
Interpublic Group is involved in marketing and advertising, they would likely maintain databases for their clients and various advertising campaigns. Imagine you're given these two tables in your SQL server:
customer_id | first_name | last_name | email_address | city | state |
---|---|---|---|---|---|
4312 | John | Doe | johndoe@email.com | New York | NY |
7852 | Jane | Smith | janesmith@email.com | Los Angeles | CA |
6531 | Mark | Taylor | marktaylor@email.com | Chicago | IL |
2461 | Emily | Johnson | emilyjohnson@email.com | Las Vegas | NV |
9376 | Michael | Brown | michaelbrown@email.com | Atlanta | GA |
campaign_id | customer_id | start_date | end_date | campaign_name |
---|---|---|---|---|
001 | 4312 | 05/01/2022 | 05/31/2022 | Spring Sale |
002 | 7852 | 06/01/2022 | 06/30/2022 | Summer Blowout |
003 | 6531 | 09/01/2022 | 09/30/2022 | Back to School |
004 | 2461 | 11/25/2022 | 12/01/2022 | Black Friday Week |
005 | 9376 | 12/01/2022 | 12/31/2022 | Holiday Sale |
Your task is to write a SQL query that displays all customers' names, email addresses and the names of the campaigns they participated in. If a customer did not participate in any campaign, display 'No Campaign'.
The answer involves postgreSQL's "LEFT JOIN" clause, which gathers records from the "customers" table and any matching records from the "campaigns" table. If there's no match, the result is NULL, which is then replaced with 'No Campaign' using "COALESCE". This way, you get a complete list of every customer, regardless of their participation in a campaign.
Also read about how Huge (a advertising firm part of IPG) uses AI and Data Science to support their creatives!
Since join questions come up frequently during SQL interviews, take a stab at this Snapchat SQL Interview question using JOINS:
The best way to prepare for a Interpublic Group SQL interview is to practice, practice, practice. In addition to solving the earlier Interpublic Group SQL interview questions, you should also solve the 200+ FAANG SQL Questions on DataLemur which come from companies like Google, Facebook, Microsoft and Amazon.
Each DataLemur SQL question has multiple hints, full answers and most importantly, there is an interactive SQL code editor so you can right online code up your SQL query answer and have it checked.
To prep for the Interpublic Group SQL interview you can also be useful to practice SQL questions from other advertising companies like:
However, if your SQL skills are weak, forget about going right into solving questions – improve your SQL foundations with this interactive SQL tutorial.
This tutorial covers SQL topics like turning a subquery into a CTE and filtering on multiple conditions using AND/OR/NOT – both of these come up often during Interpublic Group SQL interviews.
In addition to SQL interview questions, the other topics to prepare for the Interpublic Group Data Science Interview are:
The best way to prepare for Interpublic Group Data Science interviews is by reading Ace the Data Science Interview. The book's got: