GA4 Utilization Methods You Can Use Right Away Tomorrow [Monthly Datarian Seminar Replay | June 2023]

The Data Next Level Challenge (Data Level Challenge), a challenge for reading and recording recommended books related to data analysis, has returned for its 5th cohort.

This recommended book is the 12th Brunch Award 🏆 winner "UX Emerged After Data Struggles."

This book is a secret manual that generously shares the know-how of working in a data-driven way from a 17-year veteran product designer—insights that can only be provided by someone who has directly experienced hands-on work in the field.

Less than 50 days left in 2025! If you want to spend it meaningfully and productively, join us with Devel Challenge 🥰

📚 DevChall 5th Cohort Recommended Books

∙ 『UX Emerged After Data Struggles』 _ Written by Lee Mi-jin (Ranran)

✔ We recommend this challenge for these people
∙ Those curious about why designers, planners, and marketers need to understand data

∙ Those who want to know specific methods for working in a data-driven way
∙ Those who want to spend the remaining 2025 meaningfully

📌 How to Apply for the Challenge (Free, Application Deadline 11/23)
∙ https://dub.sh/5Qy11p6

If you have colleagues or friends around you who want to start studying data analysis, please share the news about this challenge.

Have you ever felt like you're studying data diligently on your own, but still relying on 'intuition' when it comes to actual work? You read books, take courses, and try things out in practice, but often it's hard to grasp what exactly it means to "work based on data."

Datarian has prepared a seminar to resolve that frustration.
In the November seminar, 17-year veteran product designer Lee Mi-jin (Ranran) will share 'How to work data-driven without data analysts' based on her direct experience at 7 small startups.

If you're curious about the vivid real-world cases and know-how that couldn't be fully covered in the Brunch Book award-winning work "UX Emerged at the End of Data Struggles", don't miss Datarian's November seminar "Data-Driven Design Without Data Analysts".

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"Data-Driven Design Without a Data Analyst" Seminar

• Date: November 4th (Tuesday) 7:00 PM - 9:00 PM

• Location: Zoom Online Seminar

• Speaker: Lee Mi-jin (Ranran) — Author of 『UX Emerged After Data Struggles』, 17-year Product Designer

► Register for Seminar

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We recommend this seminar for these people​

• Designers, planners, and marketers who want to work based on data

• Small startup practitioners without analysts

• All practitioners who want to persuade with data, not intuition

If you have team members, colleagues, or acquaintances who might need this seminar content, please be sure to share this link with them.

I'd like to share information about the free GA4 Hands-On Seminar that will be held next Tuesday evening.

I've prepared this for those who have resolved to learn GA4 this year but haven't started yet.

In just 2 hours at the seminar, you can install GA4 on your blog and check real-time data yourself.

✔ GA4 can be installed without development knowledge

✔ Hands-on, hands-on training with simple setup in just a few clicks

✔ Install GA4 on your Tistory blog and check real-time data

✔ You can ask practitioners directly about your GA4-related questions in the Q&A session.

📅 Seminar Information

• Event Date: 7/15 (Tue) 7:00 PM - 9:00 PM

• Location: Zoom Online

• Cost: Free

👉 Apply for participation

If you have a teammate or acquaintance who might need a seminar, please share the news with them :)

Data analysts can be divided into ‘in-house data analysts’ and ‘consulting data analysts’ depending on the type of company they work for.

Next Tuesday (7/9) at 7 PM, there will be a seminar where you can learn about the differences between the two types of data analysts , with senior data analysts who have built their careers as consulting data analysts and in-house data analysts .

If you have a colleague or friend who is an aspiring data analyst or junior analyst who is worried about their first company or career, please share the seminar information 🙂

👉 Check out the seminar: https://bit.ly/3GmaJzu

Datalian July Seminar “Data Analysts, Which Company Should You Go To?”

📅 7/9 (Tue), 7pm ZOOM Online

✔ [Part 1 Lecture] This is what data analysts at consulting firms do _ Kim Seon-young, Executive Director of Market Fit Lab Solutions Division

✔ [Part 2 Panel Talk] What is the difference between an in-house data analyst and a consulting data analyst?

👉 Apply: https://bit.ly/3GmaJzu

Data-driven marketing is the new trend, but you don't have a mentor to teach you how and don't know where to start?

Find out everything from how to get started with data-driven marketing to the latest trends in the marketing industry changing in the cookieless era.

11/14 (Tue) “Everything about Marketing Performance Analysis (feat. GA4)”

1⃣ [Lecture] Are you only looking at the last touch in terms of advertising performance?

2⃣ [Panel Talk] How should we measure marketing performance?

If you're struggling with measuring your marketing performance, see you at our November seminar.

If you have a colleague, friend, or acquaintance who you think needs this seminar, please share this news with them!

👉 Apply for the November seminar

[Questions received so far]

✔ How can you do data-driven marketing when you don’t have a data team and a data analysis environment in place?

✔ It’s hard to get a feel for what data to look at when judging marketing performance. There’s too much data to look at, and I’m curious about how to determine which data is the baseline and which data needs to be improved.

✔ I am concerned that the number of content views is high, but the conversion to APP downloads is low. What could be the problem?

✔ I am a small brand marketer. I planned influencer collaboration content and it got a good response and good reviews. However, there is too little data to know whether there was an increase in the number of followers or a direct impact on my store. How can I prove the results in this case?

✔ I work at a startup. I'm receiving surveys from users, but most of the data is qualitative, so I don't know how to use it. I'm thinking about getting rid of subjective questions altogether.