At BCG, particularly within BCG X which focuses on tech & AI, Data Analysts and Data Scientists uses SQL to analyze large datasets, such as customer sentiment and market trends, to gain strategic insights. They also manage databases for different consulting clients by integrating data from various sources and ensuring data quality, which is why SQL interview questions are part of BCG’s data job interviews.
So, to help you practice, here's 9 Boston Consulting Group SQL interview questions – how many can you solve?
Boston Consulting Group (BCG) is a global management consulting firm. For this question, we are going to find the VIP customers who have the highest total charges. A VIP customer is defined as the one who has total charges more than USD 10,000.
Assume that we have two tables, and , with the following schema:
customer_id | first_name | last_name | |
---|---|---|---|
1 | John | Doe | john.doe@gmail.com |
2 | Jane | Smith | jane.smith@gmail.com |
3 | Robert | Brown | robert.brown@gmail.com |
4 | Micheal | Johnson | micheal.johnson@gmail.com |
charge_id | customer_id | charge_amount | charge_date |
---|---|---|---|
1 | 1 | 5000 | 2022-12-01 |
2 | 1 | 6000 | 2022-12-02 |
3 | 2 | 3000 | 2022-12-01 |
4 | 2 | 2000 | 2022-12-03 |
5 | 3 | 4000 | 2022-12-04 |
6 | 4 | 12000 | 2022-12-05 |
The above SQL query will join the and tables on , group the result by , , and fields, and filter the grouped result where total charge amount per customer is more than USD 10,000. The resulting table would contain the list of VIP customers who have the highest total charges.
To practice a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
Suppose you had a table of BCG employee salary data. Write a SQL query to find all 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.
Try this question 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 code above is tough, you can find a step-by-step solution here: Employee Salaries Higher Than Their Manager.
The constraint is used to enforce rules on the data in a specific column. If a row is inserted or updated with data that does not follow the CHECK constraint's rule, the operation will fail. The CHECK constraint is often used with other constraints, such as NOT NULL or UNIQUE, to ensure that data meets certain conditions. You may want to use a CHECK constraint in your database if you want to maintain the quality and reliability of your data by ensuring that it meets specific criteria.
For example, you might use a CHECK constraint to ensure that a column contains only positive numbers, or that a date is within a certain range.
The Boston Consulting Group (BCG) maintains a database of customer product reviews. Each product review record contains a unique review id, user id, date of submission, product id, and star rating (1-5). To analyze customer satisfaction for its different products over time, BCG needs you to write a SQL query that calculates the average star ratings for each product on a monthly basis.
In other words, for each product, calculate its average star rating for each month in the database.
review_id | user_id | submit_date | product_id | stars |
---|---|---|---|---|
6171 | 123 | '2022-06-08' | 50001 | 4 |
7802 | 265 | '2022-06-10' | 69852 | 4 |
5293 | 362 | '2022-06-18' | 50001 | 3 |
6352 | 192 | '2022-07-26' | 69852 | 3 |
4517 | 981 | '2022-07-05' | 69852 | 2 |
month | product_id | avg_stars |
---|---|---|
6 | 50001 | 3.50 |
6 | 69852 | 4.00 |
7 | 69852 | 2.50 |
In this PostgreSQL query, I am extracting the month from the column and grouping by both the and to get the average star ratings () for each product per month. I am then ordering the results by and to display the ratings in a clear manner.
To practice another window function question on DataLemur's free online SQL code editor, try this Amazon BI Engineer interview question:
A primary key is a column (or set of columns) in a table that uniquely identifies each row in the table. It cannot contain null values and must be unique across all rows in the table.
A foreign key is a column (or set of columns) in a table that references the primary key of another table. It is used to establish a relationship between the two tables. A foreign key can contain null values, and multiple rows in the referencing table can reference the same row in the referenced table.
For example, consider a database with two tables: and . The BCG customers table might have a primary key column called , while the BCG orders table might have a foreign key column called that references the column in the table. This establishes a relationship between the two tables, such that each row in the orders table corresponds to a specific BCG customer.
Boston Consulting Group or BCG, a management consulting firm, keeps track of all its consultation sessions with clients. In an effort to assess efficiency, you're asked to calculate the average duration of consultation sessions in minutes for each consultant at BCG.
The database has a table , with the session_id, consultant_id, start_time, and end_time of all sessions.
session_id | consultant_id | start_time | end_time |
---|---|---|---|
1111 | 20 | 2023-06-08 10:30:00 | 2023-06-08 12:30:00 |
2222 | 20 | 2023-07-15 09:00:00 | 2023-07-15 11:15:00 |
3333 | 21 | 2023-08-18 13:45:00 | 2023-08-18 16:00:00 |
4444 | 22 | 2023-09-26 10:30:00 | 2023-09-26 12:00:00 |
5555 | 22 | 2023-10-05 14:00:00 | 2023-10-05 15:45:00 |
You will need to calculate the difference in start_time and end_time to get the duration of each session, then find the average duration for each consultant.
Here's the SQL query to find out:
This query first calculates the duration of each session in minutes using EXTRACT(EPOCH FROM (end_time - start_time))/60. It then calculates the average duration for each consultant using the AVG function. The result is grouped by consultant_id to get separate averages for each consultant.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating duration or this Stripe Repeated Payments Question which is similar for < handling time-bound data.
When using , only rows that are identical in both sets will be returned.
For a concrete example, say you were a Data Analyst supporting the Sales Analytics team at BCG, and data on potential sales leads lived in both Salesforce and Hubspot CRMs. To write a query to analyze leads created before 2023 started, that show up in BOTH CRMs, you would use the command:
BCG (Boston Consulting Group) is conducting an internal study on the effectiveness of its in-house digital marketing campaigns. You are provided with two tables: and .
The table contains information about the different marketing campaigns such as the campaign_id, campaign name, and the date when the campaign started.
The table contains information about individuals who have clicked on the ads such as click_id, the user_id, the campaign_id that the ad was part of and the date and time when the click happened.
Your task is as follows: Determine the click-through rate for each campaign. The click-through rate is defined as the total number of clicks per campaign over the total number of campaigns.
campaign_id | campaign_name | start_date |
---|---|---|
1 | Campaign A | 2021-12-01 |
2 | Campaign B | 2021-12-03 |
3 | Campaign C | 2021-12-05 |
click_id | user_id | campaign_id | click_date |
---|---|---|---|
1 | 1001 | 1 | 2021-12-02 08:00:00 |
2 | 1002 | 1 | 2021-12-02 09:00:00 |
3 | 1003 | 2 | 2021-12-03 08:00:00 |
4 | 1004 | 2 | 2021-12-03 09:00:00 |
5 | 1005 | 3 | 2021-12-06 08:00:00 |
The above query first joins the and tables on . It then groups the result by and for each , it counts the number of click_ids i.e., the number of clicks that happened for that campaign. This gives us the total number of clicks for each campaign. To get the click through rate, we need to divide this count with the total number of campaigns. This will give us the click-through rate (CTR) for each campaign.
It's important to note that this query only provides the number of clicks for each campaign. If you also need to include the total number of campaigns to calculate the exact CTR, you would need an additional query or subquery to provide this denominator value. For simplicity in this question, we've not included those additional steps.
To practice another question about calculating rates, solve this TikTok SQL question within DataLemur's interactive SQL code editor:
BCG manages various consulting projects. They are keen to understand how their clients have been rating the services provided over the years so they can pinpoint their strengths and weaknesses. Write a SQL query to find out the average rating BCG received for each client across different years. Assume the ratings are on a scale of 1 to 5 (5 being the highest).
project_id | client_id | rating_date | rating |
---|---|---|---|
4598 | ABC | 02/15/2019 | 5 |
7583 | XYZ | 06/04/2019 | 4 |
4665 | XYZ | 08/10/2020 | 3 |
4875 | ABC | 07/22/2020 | 4 |
6674 | DEF | 05/15/2020 | 2 |
year | client | avg_rating |
---|---|---|
2019 | ABC | 5.00 |
2019 | XYZ | 4.00 |
2020 | ABC | 4.00 |
2020 | XYZ | 3.00 |
2020 | DEF | 2.00 |
In this query, we first extract the year from the rating_date column using the EXTRACT function. This gives us the year on which rating was made. We then group by the extracted year and client_id to get the average rating for each client across different years. The AVG function is used to calculate the average rating. The ORDER BY clause is used to order the result set by the year and client.
The key to acing a BCG SQL interview is to practice, practice, and then practice some more! Besides solving the above BCG SQL interview questions, you should also solve the 200+ DataLemur interview questions which come from companies like Google, Facebook, Microsoft and Amazon.
Each SQL question has hints to guide you, step-by-step solutions and best of all, there's an interactive coding environment so you can instantly run your SQL query and have it graded.
Discover how BCG is harnessing the power of Artificial Intelligence to drive business growth and innovation!
To prep for the BCG SQL interview it is also wise to practice SQL questions from other management consulting companies like:
In case your SQL foundations are weak, don't worry about diving straight into solving questions – refresh your SQL knowledge with this SQL tutorial for Data Analytics.
This tutorial covers things like how window functions work and manipulating date/time data – both of these pop up frequently in SQL interviews at BCG.
In addition to SQL interview questions, the other question categories to practice for the BCG Data Science Interview include:
To prepare for BCG Data Science interviews read the book Ace the Data Science Interview because it's got:
Read more about how data science and consulting are tied together through these real world scenarios!
Also focus on the behavioral interview – prepare for it with this guide on acing behavioral interviews.