Lenskart Product Analyst Interview Questions

Lenskart Product Analyst Interview Questions

What is Lenskart?

Lenskart is an Indian eyewear company that primarily operates as an online retailer of eyeglasses, sunglasses, contact lenses, and other eyewear accessories. It was founded in 2010 by Peyush Bansal and has since become one of the largest players in the Indian eyewear market.

Lenskart offers a wide range of eyewear products for men, women, and children, including prescription glasses, fashion eyeglasses, sunglasses, and various lens options. They collaborate with renowned brands as well as offer their own private label frames.

One of the notable aspects of Lenskart’s business model is their emphasis on providing affordable eyewear and eyecare solutions. They have introduced innovative technologies such as 3D virtual try-on tools and home eye check-up services to enhance the customer experience. Lenskart also operates numerous brick-and-mortar stores across India, known as Lenskart Offline, where customers can try on frames, get their eyes tested, and make purchases.

Lenskart has gained significant popularity in India and has expanded its presence beyond the online realm by establishing partnerships with optical stores, optometrists, and eye care hospitals. They aim to make eyewear accessible and affordable to a wide range of consumers in India through their extensive product selection, competitive pricing, and convenient shopping experience.

Lenskart Product Analyst Interview Questions

Why Analytics as a Career?

Target job roles – Business Analyst, Data Analyst, Data Scientist, Business Intelligence Engineer, Product Analyst, Machine Learning Engineer, Data Engineer

Target Companies – FAANG and only product-based companies

CTC offered –
12 to 20 LPA for Level 1 (0 to 4 YOE)
20 to 35 LPA for Level 2 (Senior level – 4 to 7 YOE )
35 to 50 LPA for Level 3 (Team Lead or Manager – 7 to 9 YOE)
50 to 80 LPA for Level 4 (Manager or Senior Manager – 9 to 12 YOE)

Tools and Technologies required
SQL – 9/10
Python – 7/10
Visualization tool (Power BI or Tableau) – Good to have
Machine Learning Algorithm – Expert in at least a couple of algorithms (if going for Data Science role)

Why The Data Monk?

We are a group of 30+ people with ~8 years of Analytics experience in product-based companies. We take interviews on a daily basis for our organization and we very well know what is asked in the interviews.
Other skill enhancer website charge 2lakh+ GST for courses ranging from 10 to 15 months.

We only focus on making you a clear interview with ease. We have released our Become a Full Stack Analytics Professional for anyone in 2nd year of graduation to 8-10 YOE. This book contains 23 topics and each topic is divided into 50/100/200/250 questions and answers. Pick the book and read it thrice, learn it, and appear in the interview.

We also have a complete Analytics interview package
– 2200 questions ebook (Rs.1999) + 23 ebook bundle for Data Science and Analyst role (Rs.1999)
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– Resume review and improvement (Top mate – Rs.500 per review)

Total cost – Rs.10500
Discounted price – Rs. 9000

How to avail of this offer?
Send a mail to nitinkamal132@gmail.com

Lenskart Product Analyst Interview Process

Company – Lenskart
Designation -Product Analyst
Year of Experience required – 3 to 5 years
Technical expertise – SQL, Python, Case Study
Salary offered – 14 to 20 LPA (10% variable)
Number of Rounds – 5

SQL Interview Questions

In almost all the rounds, SQL questions were regularly asked, the questions were simple but covered a lot of vast topics. Following are a few questions asked in the complete interview process throughout different rounds

What is the use of IFNULL and ISNULL in SQL?

IFNULL() and ISNULL() are two functions in sql to check the existence of null values in a particular column and replace with a value depending on the type of DB used.
ISNULL() in Sql server:- Checks for the null values and if it’s present it returns with an alternate value.
ISNULL(column_name, value_to_be_replaced)
ISNULL() in MYSQL :- Checks the column value if it is null or not null. It returns a boolean result.
IFNULL in MYSQL :- Checks for the null values and if it’s present it returns with an alternate value.
IFNULL(column_name, value_to_be_replaced)

Difference between SQL and NoSQL

The following are the five major distinctions between SQL and NoSQL:
1.Relational databases are SQL, while non-relational databases are NoSQL.
2.SQL databases have a specified schema and employ structured query language. For unstructured data, NoSQL databases use dynamic schemas.
3.SQL databases scale vertically, but NoSQL databases scale horizontally.
4.NoSQL databases are document, key-value, graph, or wide-column stores, whereas SQL databases are table-based.
5.SQL databases excel in multi-row transactions, while NoSQL excels at unstructured data such as documents and JSON.

Difference between CTE and Temp table on the basis of performance and persistence

CTE (Common Table Expression) and temporary tables are both used in SQL to store intermediate result sets, but they have some differences in terms of usage, scope, and performance. Here’s a comparison between CTE and temporary tables:

Structure and Usage:
CTE: A CTE is defined using the “WITH” keyword and can be used to simplify complex queries, recursive queries, or create self-contained subqueries. It can be referenced in subsequent parts of the query, including SELECT, INSERT, UPDATE, or DELETE statements.
Temporary Table: A temporary table is created using the “CREATE TABLE” statement and behaves like a regular table. It can be used to store and manipulate data within a session or transaction. It is typically used when you need to perform complex operations or store intermediate results that need to be accessed multiple times.

Performance:

CTE: CTEs are usually optimized by the query optimizer and may provide better performance for smaller result sets or simpler queries. They are suitable when the result set is relatively small and does not need to be indexed.
Temporary Table: Temporary tables can be indexed, which can improve performance for larger result sets or complex queries that involve multiple operations. They are better suited when you need to perform multiple operations on the intermediate data or join it with other tables.

Persistence:

CTE: CTEs are not persisted in the database and are discarded once the query execution is completed.
Temporary Table: Temporary tables are persisted in the database until they are explicitly dropped or until the session or transaction ends.
In summary, CTEs are useful for creating temporary result sets within a query and are beneficial for simplifying complex queries. Temporary tables are more suitable when you need to store and manipulate intermediate data that requires indexing or multiple operations. The choice between CTEs and temporary tables depends on the specific requirements and characteristics of the query or task at hand.

Important conditions for joining two tables on a key?
Primary conditions for joining two tables on keys:
1. The key column should contain non-NULL values
2. The datatype of both tables’s column should be similar

What is a Cross-Join?
Cross join can be defined as a cartesian product of the two tables included in the join. The table after join contains the same number of rows as in the cross-product of the number of rows in the two tables.

SELECT stu.name, sub.subject
FROM students AS stu
CROSS JOIN subjects AS sub;

What is an Index?
A database index is a data structure that provides a quick lookup of data in a column or columns of a table. It enhances the speed of operations accessing data from a database table at the cost of additional writes and memory to maintain the index data structure.

How do the SQL commands flow at the back end?

Order of execution for an SQL query
1) FROM, including JOINs
2) WHERE
3) GROUP BY
4) HAVING
5) WINDOW Functions
6) SELECT
7) DISTINCT
8) UNION
9) ORDER BY
10) LIMIT AND OFFSET

Write a SQL query to get the second highest query using sub query.

SELECT
Name, MAX(salary) AS salary
FROM Table_name
WHERE salary < (SELECT MAX(salary) FROM Table_name)

250 Most Asked SQL interview questions with solution at 60% discount

Analytics Case Study Interview Questions

There were a few questions asked in case study, that includes questions on top KPIs to show to the

Case Study 1 – Develop a dashboard for Lenskart with as many metrics as possible

Revenue: Total sales revenue generated by Lenskart.
Gross Profit Margin: The percentage of revenue remaining after deducting the cost of goods sold.
Average Order Value: The average value of each customer’s purchase.
Conversion Rate: The percentage of website visitors who make a purchase.
Customer Acquisition Cost (CAC): The cost incurred to acquire a new customer.
Customer Lifetime Value (CLTV): The predicted net profit attributed to the entire future relationship with a customer.
Repeat Purchase Rate: The percentage of customers who make more than one purchase.
Return Rate: The percentage of products returned by customers.
Net Promoter Score (NPS): A measure of customer satisfaction and loyalty.
Website Traffic: The number of visitors to the Lenskart website.
Mobile App Downloads: The number of downloads of the Lenskart mobile app.
App Engagement: The level of user activity and interactions within the mobile app.
Average Time on Site: The average duration of time visitors spend on the website.
Inventory Turnover: The number of times Lenskart sells and replaces its inventory in a given period.
Customer Service Response Time: The average time taken to respond to customer inquiries or issues.
Employee Satisfaction: Measures of employee engagement and satisfaction.
Social Media Engagement: The level of engagement, such as likes, shares, and comments, on Lenskart’s social media platforms.
Brand Awareness: The level of recognition and familiarity consumers have with the Lenskart brand.
Market Share: Lenskart’s share of the overall eyewear market in India.
Net Profit Margin: The percentage of revenue remaining after deducting all expenses, including operating costs and taxes.

Case Study 2
Problem Statement – Can you choose a strategy for increasing the number of songs listened by the user on an online application? How will you decide the types of playlists to suggest him?

Mainly I will tackle this problem like any recommendation engine.
Understand User Preferences: Gather data on the user’s listening history, including genres, artists, albums, and songs they have enjoyed in the past. Analyze their preferences to identify patterns and determine their musical tastes.
Collaborative Filtering: Implement a collaborative filtering algorithm to recommend songs based on the user’s listening history and the preferences of similar users. This approach helps suggest songs that align with the user’s taste and introduces them to new music they might enjoy.
Personalized Recommendations: Utilize machine learning techniques, such as content-based filtering and recommendation algorithms, to generate personalized playlists tailored to the user’s specific interests. Consider factors like genre, mood, tempo, and artist similarity to curate playlists that resonate with the user.
Dynamic Recommendations: Implement dynamic recommendations that adapt based on real-time user behavior and context. Consider factors like time of day, day of the week, and current events to suggest playlists that match the user’s mood or the current situation.
User Feedback and Ratings: Encourage users to provide feedback and ratings on songs they listen to. Leverage this feedback to refine the recommendation algorithms and improve the accuracy of future playlist suggestions.
Variety and Diversity: Ensure that the suggested playlists offer a diverse range of music to cater to different moods, occasions, and genres. Incorporate popular playlists, trending tracks, and curated playlists from experts to provide a broad selection for users.
Discoverability and Exploration: Include features that promote music discovery and exploration. For example, suggest “Discover Weekly” or “New Releases” playlists that introduce users to fresh content, emerging artists, or genres they haven’t explored yet.
Social Integration: Integrate social features into the application to enable users to follow friends, influencers, or artists. Recommend playlists created by people with similar tastes or those they follow to encourage engagement and shared music discovery.
Contextual Recommendations: Incorporate contextual information, such as location, weather, or activity, to suggest playlists that align with the user’s current situation. For instance, suggesting upbeat playlists for workouts or relaxing playlists for a rainy day.
Regularly Refresh Playlists: Keep the suggested playlists fresh and updated to maintain user interest. Regularly update playlists with new songs, replace outdated tracks, and incorporate user feedback to ensure the recommendations remain relevant.

Guesstimate – How many street lights are there in India?
Let’s start with the formula we can use to estimate this:
(Area covered by streets in India) * (Percentage of street area containing street lights) * (Avg. number of street lights in the area/Km^2)
Let’s float some numbers now:
The total area of India is: 3.28~3.3 million Km^2
1. Area covered by streets: .66 million Km^2 (Real Estate+Farm Lands+Streets)
Assuming real estate to be most of the area followed by farm land (including barren land), I’ll divide the area into 50%, 30% & 20%. So, we have total street area = 3.3*20% = .66 million Km^2.

2. Percentage of street area covered by street lights: 30%
For this, I would assume a 70%-30% division of people in villages and cities.
Let’s take into account 60% of tier 1 (10% of overall cities) cities, 40% in tier 2 (70% of overall cities), 10% of tier 3 (10% of overall cities) and 10% in other small towns (another 10%).
So, we have 6%, 28%, 1%, 1%. Adding them up and rounding them off to lower number owing to poor infrastructure and an increase in assumption of total area in India. We assume total as 30%.

3. Avg. number of street lights per unit area: 2100
Assuming avg Width of the street containing 1 fleet of street lights is: 10 m = .010 Km
Number of Street lights per Km = 2 (Factor of two coz the number of street lights at one spot)* [1000 m/ Distance between street lights (50 meters)] = 40.
So, Avg Street lights per unit area: 20/.010 = 2000/ Km^2
I will spike this number up by 5% considering flyovers, Multiple lane expressways making it to be:
2100/ Km^2.
So, Putting it in formula: (.66 million Km^2)(30%)(2100/Km^2)= 415.8 million

100+ Case Studies and Guesstimates solved completely at 50% discount

Daily Quiz to crack Analytics Interviews

We have been curating interview questions for the Top Product based companies. Following are the interview questions shared so far:-
Day 1 – Amazon Business Intelligence Engineer Interview Questions – https://thedatamonk.com/amazon-business-intelligence-engineer-interview-questions/
Day 2 – Myntra Senior Business Analyst Questions – https://thedatamonk.com/myntra-senior-business-analyst-questions/
Day 3 – OYO Data Analyst Interview Questions – https://thedatamonk.com/oyo-data-analyst-interview-questions/
Day 4 – Swiggy Business Analyst Interview Questions – https://thedatamonk.com/swiggy-business-analyst-interview-questions/
Day 5 – Flipkart Business Analyst Interview Questions – https://thedatamonk.com/flipkart-business-analyst-interview-questions-2/
Day 6 – Dream11 Product Analyst Interview Questions – https://thedatamonk.com/dream11-product-analyst-interview-questions/

2200 Questions to Crack ANY Analytics Interview

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