# 9 Klaviyo SQL Interview Questions (Updated 2024)

Updated on

April 2, 2024

At Klaviyo, SQL is used often for analyzing customer data for personalized marketing strategies and managing large datasets for effective email segmentation. Because of this, Klaviyo LOVES to ask SQL questions in interviews for Data Analyst, Data Science, and BI jobs.

To help you prepare for the Klaviyo SQL interview, we've collected 9 Klaviyo SQL interview questions – how many can you solve?

## 9 Klaviyo SQL Interview Questions

### SQL Question 1: Calculate Average Monthly Review Stars

Klaviyo is an email marketing platform created for online businesses featuring powerful email and SMS marketing automation. They make extensive use of analytics, and given your role involves data analytics, we're interested in how you can handle complex SQL queries.

Assuming you're given a table containing reviews written by users about products, can you write a SQL query to calculate the average star rating given for each product on a month by month basis? Your result should contain the month, the product ID, and the average stars for that product in the given month.

##### Example Input:
review_iduser_idsubmit_dateproduct_idstars
617112306/08/2022500014
780226506/10/2022698524
529336206/18/2022500013
635219207/26/2022698523
451798107/05/2022698522
##### Example Output:
mthproduct_idavg_stars
6500013.50
6698524.00
7698522.50

This query first extracts the month from the using the function. It groups the rows first by the month and then by the , and calculates the average star rating for each group using the function.

Pro Tip: Window functions are a popular SQL interview topic, so practice all the window function problems on DataLemur

### SQL Question 2: Customer behavior patterns and segmentation

As a data analyst at Klaviyo, you are required to understand and categorize customer behavior. Given the customer records database, your task is to filter out customers who have made purchases during weekends, and who have spent more than \$500 in the 'Health & Beauty' category. Alongside this, also determine the number of such customers per state.

customer_idstate
1CA
2WA
3TX
4NY
5FL
##### Example Input:
purchase_idcustomer_idcategoryamountpurchase_date
11Health & Beauty2002022-05-07
21Electronics3002022-05-06
32Health & Beauty6002022-05-15
43Groceries1502022-05-22
54Health & Beauty7002022-05-29

Below is the SQL query in PostgreSQL to solve this problem.

In the above query, a JOIN is used to combine customers and purchases tables based on customer_id. The WHERE clause filters out the customers who have spent more than \$500 in the 'Health & Beauty' category and have made purchases on weekends (Day 6 and 7 represents Saturday and Sunday according to ISO 8601). Finally, the COUNT function is used with GROUP BY clause to calculate the number of such customers per state.

### SQL Question 3: In what circumstances might you choose to denormalize a database?

Database normalization has several benefits:

• Reduces Redundancy: Normalization can minimize redundancy by breaking down a larger, general table into smaller, more granular tables. This often reduces the amount of data that needs to be accessed for particular queries, since some duplicated columns can be removed.

• Improves Data Integrity: Normalization can help to ensure the integrity of the data by minimizing the risk of data inconsistencies and errors. By establishing clear relationships between the tables via primary and foreign keys, and enforcing these constraints, you can have more reliable records and relationships stored in your DB.

• Improves Performance: Normalization can improve the performance of the database by reducing the amount of data that needs to be stored and accessed in a single table. This results in faster query times and better overall performance.

### SQL Question 4: Calculating Click-through-rates (CTR) and Conversion rates (CR)

As a data analyst for Klaviyo, a company specializing in email marketing for e-commerce, you're tasked with the following problems:

1. You're given two tables and . contains information regarding every time a user clicks an ad, and the date & time of the click.

2. contains information every time a user adds a product to their cart from a clicked ad, along with the timestamp of the event.

• Calculate the click-through-rate (CTR) for all ads.
• Calculate click-through conversion rate (CR) from viewing a product to adding a product to the cart for each unique ad.

The CTR is calculated as the total number of 'ad_clicks' / total number of ad views expressed as a percentage. The CR is calculated as the total number of 'add_to_cart' events / total number of 'ad_clicks' expressed as a percentage.

##### Example Input:
10112306/08/2022 00:00:00
10226506/10/2022 00:00:00
10136206/18/2022 00:00:00
10319207/26/2022 00:00:00
10198108/05/2022 00:00:00
##### Example Input:
1015000106/08/2022 00:10:00
1026985206/11/2022 01:00:00
1015000106/18/2022 02:00:00
1036985207/26/2022 03:10:00
1016985208/05/2022 04:30:00
##### Example Output:
101NN
102NN
103NN

To solve this, two separate calculations would be made. The first - a count of all clicks for each ad. The second - a count of all cart additions for each ad.

The output provides the ad_id, click-through-rate (CTR) as a percentage, and click-through conversion rate (CR) as a percentage.

To solve a similar problem about calculating rates, solve this TikTok SQL Interview Question on DataLemur's interactive coding environment:

### SQL Question 5: Why would you use the SQL constraint?

The CHECK constraint is used to set a rule for the data in a column. If a row is inserted or updated and the data in the column does not follow the rule specified by the CHECK constraint, the operation will be unsuccessful.The CHECK constraint is often used in conjunction with other constraints, such as NOT NULL or UNIQUE.

You might consider implementing the CHECK constraint in your database if you want to ensure that certain data meets specific conditions. This can be helpful for maintaining the quality and reliability of your data.

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.

### SQL Question 6: Highest Valued Customer

As a digital marketing platform, Klaviyo maintains records of all transactions initiated by users. Write an SQL query that groups data on the basis of users and returns the user_id of the customer who has spent the most money on Klaviyo's services in a month.

Assume there is a table. This table captures all transactions raised by users.

##### Example Input:
transaction_iduser_idtransaction_dateamount
10112306/08/2022 00:00:00500
10226506/10/2022 00:00:00200
10312306/12/2022 00:00:00700
10419207/26/2022 00:00:00600
10512307/05/2022 00:00:00550
##### Example Output:
user_idmonthtotal_spent
12361200
2656200
1927600
1237550

This query groups transactions by user_id and month, sums up the transaction amount for each group, and retrieves the user_id and month of the user who spent the most in a given month. The EXTRACT function is used to get the month from transaction_date, and SUM is used to total the spending for each user per month. The results are ordered in descending order of total_spend to get the user who spent the most at the top, and LIMIT 1 is used to get only this user.

### SQL Question 7: What does adding 'DISTINCT' to a SQL query do?

The keyword removes duplicates from a query.

Suppose you had a table of Klaviyo customers, and wanted to figure out which cities the customers lived in, but didn't want duplicate results.

table:

namecity
AkashSF
BrittanyNYC
CarlosNYC
DiegoSeattle
EvaSF
FayeSeattle

You could write a query like this to filter out the repeated cities:

city
SF
NYC
Seattle

### SQL Question 8: Joining Customer and Orders data

At Klaviyo, it is critical to understand the purchasing behavior of our customers in close relation to their demographic details. Our business relies heavily on behavioral customer data. Given the following two tables, and , can you write a SQL query that can pull data to provide insights into the value generated by customers of different age groups?

##### Sample Data:
customer_idfirst_namelast_nameemailbirth_date
101JohnDoejohn.doe@gmail.com1988/03/16
102EmmaWatsonwatson.emma@gmail.com1990/08/24
103LiamSmithsmith.liam@gmail.com1970/05/17
104OliviaJohnsonjohnson.olivia@gmail.com1999/12/23
105NoahWilliamswilliams.noah@gmail.com2001/07/10
##### Sample Data:
order_idcustomer_idorder_datetotal_amount
11012022/01/11100.00
21022022/01/12340.50
31032022/01/13254.75
41012022/01/1489.34
51052022/01/15157.43

Here's the PostgreSQL query to get the result:

In the above query, we have joined the and tables on . We have restricted the relevant orders to those placed in 2022. We then grouped the resulting data by age of customers, and calculated the number of orders and total value generated by each age group in 2022. The result is sorted in descending order of total value generated. This data will give Klaviyo critical insights about the value generated by different customer age groups.

Because joins come up frequently during SQL interviews, try an interactive SQL join question from Spotify:

### SQL Question 9: Compute the Weighted Ratings for Each Product

Klaviyo has a product rating system where each product is rated by users on a scale of 1 to 5 stars, 5 being excellent. The weight is given based on how recent the review is. Reviews in the last 30 days are given a weight of 2, reviews between 30 and 60 days are given a weight of 1, and older than 60 days are given a weight of 0.5. Calculate the weighted average rating of all products.

##### Example Input:
review_iduser_idreview_dateproduct_idstars
61711232022-07-01500014
78022652022-06-01698524
52933622022-05-01500013
63521922022-08-01698523
45179812022-08-05698522
##### Example Output:
product_idweighted_rating
500013.67
698523.00

We'll use the CASE statement to assign weights based on the conditions and compute the sum of stars multiplied by the applicable weight divided by the sum of applicable weights.

This query first assigns a weight to each review based on the date of the review, and then calculates the weighted rating for each product by dividing the sum of the product of stars and weight by the sum of weights, rounding to two decimal places.

To practice a very similar question try this interactive Amazon Average Review Ratings Question which is similar for calculating average ratings or this Microsoft Teams Power Users Question which is similar for weighting based on time.

### Klaviyo SQL Interview Tips

The best way to prepare for a Klaviyo SQL interview is to practice, practice, practice. Besides solving the earlier Klaviyo SQL interview questions, you should also solve the 200+ SQL exercises on DataLemur which come from companies like Facebook, Google, and VC-backed startups.

Each interview question has hints to guide you, step-by-step solutions and crucially, there's an online SQL coding environment so you can instantly run your SQL query answer and have it graded.

To prep for the Klaviyo SQL interview it is also wise to solve interview questions from other tech companies like:

However, if your SQL query skills are weak, don't worry about going right into solving questions – strengthen your SQL foundations with this DataLemur SQL tutorial.

This tutorial covers topics including ordering data and RANK() window functions – both of which show up frequently in SQL job interviews at Klaviyo.

### Klaviyo Data Science Interview Tips

#### What Do Klaviyo Data Science Interviews Cover?

In addition to SQL query questions, the other types of questions tested in the Klaviyo Data Science Interview are:

#### How To Prepare for Klaviyo Data Science Interviews?

To prepare for Klaviyo Data Science interviews read the book Ace the Data Science Interview because it's got:

• 201 interview questions taken from Google, Microsoft & tech startups
• a refresher on Product Analytics, SQL & ML
• over 900+ reviews on Amazon & 4.5-star rating