It was nice to hear about the actual analysis methods used.
5.0
Joung Inshik
60% enrolled
Thank you.
5.0
Jang Jaehoon
100% enrolled
Thank you for the great lecture!
What you will gain after the course
Upselling and Cross-selling Examples and Importance
Key Metrics and Examples for Sales Analysis
LTV Concept and Utilization
Concepts and Examples of Customer Segmentation Analysis
Concepts and Examples of Cohort Analysis
📍 Notice
This lecture will be converted to a free course starting Monday, June 2, 2025. Please keep this in mind before paying for the lecture.
If you have any questions, please click the 'Inquiry' button at the bottom right to leave a message.
Approximately 2,600 cumulative applicants! Meet the much-talked-about seminar through video.
📢 Please check before taking the course!
This lecture is a recorded video (VOD) of the "Data Analysis Skills for Immediate Practical Use" live seminar held in April 2022.
It includes answers to real-time chat questions that were posted during the live presentation.
In April, we will cover practical stories of data analysis! Monthly Datarian Seminar 🎤
The April Datarian Seminar is 🔍
Don't miss the Datarian April seminar, which is packed with everything from profitable revenue analysis to cohort analysis frameworks.
“From coming to buy an Avan* to signing for a full-option Gran*” Revenue, Paying User ratio, ARPU, ARPPU, LTV, Up-selling, Cross-selling, average transaction value... why are there so many technical terms related to sales? It's because generating revenue is the most important task in a company. From the basics of sales analysis to Up-selling/Cross-selling, we will share stories about 'money-making' data analysis.
“Is your analysis stalling? Then try adding a cohort.” On your first day of work, your team leader suddenly comes to your desk, leaves a cup of coffee, and asks, “We launched a service in January. Could you take a look and see if it's doing well?” The number of customers is increasing by 1,000 every month, and the average purchase amount per customer is 5,000 KRW in January, 4,300 KRW in February, and 4,500 KRW in March. Team leader, are we... doing well?
I recommend this to those who have these concerns ✅
I am working as a data analyst, but I don't know which analysis methods I should use for my analysis.
I want to get a job as a data analyst, but I can't figure out exactly what this role actually does.
I want to use data in my work, but how should I get started?
📺 In May, we will talk about how to better present data using visualization.
[May Topic] The Beginning of Persuasion: How to Properly Present Data
#Part 1 - Data Analysis Skills You Can Use Immediately in Practice
“From coming to buy an Avan* to signing for a Gran* with full options” _ Hyejeong
“Is your analysis stalling? Then try adding cohorts.” _ Sunmi
#Part 2 - Q&A with 4 Data Analysts (Hyejeong, Sunmi, Minju, Bomin)
2부에서 답하는 사전 질문 펼쳐보기
Q1. I'm also curious about CLV utilization! I understand that CLV is predicted and used when selecting premium customers or measuring marketing costs, but I'd like to know whether you identify individual CLV for everyone... or do you get a rough understanding using average CLV... I'd also like to know the reason for choosing your method! Or do you use a different analysis method!
Q2. It is said that domain knowledge is important for data analysts, but how does the application of the same analysis technique differ depending on the domain knowledge? Is there a way for data analysts to study domain knowledge?
Q3. It seems more difficult to look at and analyze various data for Retention than for Acquisition. I'm curious which data, including cohorts, you recommend analyzing.
Q4. From a job seeker's perspective, I'm curious about how to study analysis methodologies such as Cohort and AARRR.
Q5. How can I develop a business perspective? This is a question about methods, such as whether I should take separate business-related classes, etc.
Q6. I'm curious about the process of deriving hypotheses when a problem is given :)
Q7. What are the recent trends in analysis techniques?
Q8. I'm curious about insight derivation skills!
Q9. When performing data analysis, how can I distinguish which analyses are important? If I go into too much detail, it feels like TMI (and like I'm wasting time), but if I don't, I'm anxious that I might miss something important!
Q10. Have you ever regretted deciding to become an analyst?
Q11. I'm curious to know when you feel the greatest sense of accomplishment in your role as a data analyst. :)
April Seminar Participant Introduction 📖
Part 1 Speaker <Hyejung>
I worked as a data analyst at a content platform and am now the CPO of Datarian. I am truly passionate about creating and analyzing Datarian's original content.
Part 1 Speaker <Sunmi>
After working as a data analyst at Coupang, Hyperconnect, and Kakao, I am now the CEO of Datarian. Working with the members of Datarian has made me believe even more in the power of data.
Moderator, Panel <Minju>
After founding a co-living startup and working as an analyst at a B2B logistics startup, I am now the CEO of Datarian. I am a young entrepreneur with experience ranging from founding a company to its exit. Since my first startup, I have constantly contemplated business funnels, and I am currently designing and analyzing funnels at Datarian.
Panelist <Bomin>
A data worker at a recruitment platform. From creating data that never existed before to proposing business strategies and managing projects using data, I handle everything from A to Z that can be done with data.
Frequently Asked Questions Q&A 💬
Q. When is the Monthly Datarian Live Seminar held? Where can I apply?
You can find information about next month's seminar on the Datarian website. You can also sign up right away!
Q. Is there anything I need to prepare before listening?
None :D Anyone can listen!
Q. Is it possible to view the slides used in the seminar separately?
In this seminar, we are providing Notion lecture notes so you can take notes while listening to the lecture. Please duplicate them to your personal Notion or use a tablet to take notes while watching :) Notion Lecture Notes: https://bit.ly/3MpfyJc
If you are curious about the live participation reviews, check them out 👏👏
What was the most impressive part of the seminar?
This is an explanation of upselling and cross-selling. I was able to clearly understand exactly which products are being sold and how! The detailed explanation and examples on how to break down cohorts into specifics were also great.
It was great to learn that there are various analytical techniques and different ways to interpret analysis!
It was great that you explained using easy examples like Avante, iPhone, Baemin, and KakaoTalk.
The content definitely included things needed in practice, so as a rookie with zero years of experience, it felt like meeting a kind senior colleague.
I only knew the theory, but I'm walking away with tips on how to adapt and apply it in practice.
Sharing the Notion notes made it much easier to stay focused!
The practical tips were both relatable and helpful!
It was great that you shared your real-world experiences through the Q & A session.
A word for Datarian!
It would be great if you could continue to hold seminars on various topics! Today was very informative, and I should definitely listen to all the previous seminars as well :) Thank you!
Thank you for sharing such great information and for all the preparation! It was wonderful being able to take away so much useful information!
I was worried that I might be wasting my money after signing up for the bootcamp, but this seminar immediately made me regret those thoughts and motivated me to set up a study plan! See you soon!
The seminar content was substantial, and the way it was conducted was excellent. I am very satisfied because I feel like I've gained a lot of useful information.
Thank you for preparing and leading a lecture with such great content.
Go Datarian!
Curious about the past Monthly Datarian webinar? 📺
Recommended for these people
Who is this course right for?
Those who are working as data analysts but are unsure which analysis methods to use.
Those who are curious about what this role entails in order to get a job or change careers as a data analyst
Those who want to utilize data in their work but don't know how to get started.