Somehow became a "Data PM"


Just when I didn’t know how to describe my recent work life, I happened to chat with Joyce, a senior PM in Tokyo. After coming home and reading her article “What is a Data PM?”, I immediately knew how to start writing because I —————

Somehow became a “Data PM”!

It’s been 100 days since I joined the company, and I’ve experienced all the conflicts and adjustments that come with a new job. The cultural shock was significant for me, moving from a large organization to a smaller one, from a place with financial reports to one without, from having plans to having none…

However, the biggest shock was that I needed to gather data and build Dashboards myself.

Although I am quite familiar with data, and my previous job involved maintaining high-frequency monthly and quarterly reports by region and product, the data was always prepared for me. At most, I would communicate what I wanted to see next, and someone would help create the necessary Dashboards and standard reports. The most I had to do was pull together some quick calculations in Excel.

But here, aside from the warehouse and customer service platforms used for daily shipments, all data must be retrieved from the backend, or I have to build my own dashboards. In the last two weeks, I’ve even had to use Python to do some statistical analysis myself.

What is the role of a PM?

Let’s look at the three main responsibilities listed in my contract:

  • Customer Discovery: Discovering user needs
    a. Analyze user feedback from various sources. b. Create and conduct surveys for meaningful insights. c. Conduct customer discovery calls. d. Use this data to set product priorities.
  • Product Enhancement: Enhancing the product
    a. Monitor and analyze user engagement with our products.
    b. Collaborate with design and engineering teams to improve user experience.
    c. Implement strategies to enhance product appeal and functionality.
  • Product Evolution: Advancing product direction
    a. Manage the launch and iterative development of new products and features.
    b. Create solutions that address needs across various product areas.
    c. Continuously seek and implement opportunities for product innovation.

In the last two functions, Data Analytics is a crucial capability. I am grateful that my current company is an established startup that has been operating for over a decade. Although it’s not as large as mainstream e-commerce, the volume of data is sufficient for making various product decisions and even discovering many interesting insights.

This aspect of my job is exciting and exactly the new life experience I was looking for. However, it also echoes what Joyce said:

Data PMs are very sensitive to numbers and can easily judge whether new services or updates are genuinely beneficial to the company. However, they may find it challenging to spend time on the details of product experience; PMs focus more on each feature’s impact on user experience and emphasize the importance of qualitative analysis (user interviews).

Initially, I spent a lot of time studying how different tables were connected because I was not familiar with the company’s data structure. To answer a small question, I often had to spend a lot of time looking for data. Also, for collaboration, I had to create some charts in Looker Studio to present the background of the problems I was trying to explain, which was very different from the goal-oriented work environment I was used to. It also deprived me of the weight of qualitative analysis, both in terms of execution time and its significance in decision-making.

The burden of Data

When Data is overly emphasized, it’s easy to overlook obvious “common sense” and invest too many resources in solutions that are not cost-effective.

For example, someone might propose based on data:

The data shows that the OO area in Seoul is a hotspot for hiking activities, as seen from backend data showing many users activating the hiking feature there. But we seem to have few channels there. Should we strengthen our channel infrastructure there? (Or e-commerce delivery points?)

However, the common sense here is that while people go to certain areas to hike, Koreans are unlikely to purchase expensive sports products on a whim during hiking. We could conduct marketing campaigns at hiking hotspots, but establishing sales channels there would require a significant investment with dubious returns.

In my current job, discussions like these are common:

Boss: “You propose we should do OO, but the data shows there’s no demand from customers for that.” Me: “If we only use historical data for quantitative analysis, we might miss out on many opportunities because we don’t know if there are users who signed up and then left us immediately.”

I’ve also noticed that as I began to develop a passion for Data, I gradually lost the execution ability and intuition that I used to pride myself on.

Still, I want to be a Data-oriented PM

However, while working with data, I still discover many interesting things, such as some customers who have subscribed and paid for several months without ever using the service. Why is that?

Understanding these things is very intriguing to me, and being familiar with the data makes me more confident in my proposals. With the help of AI, writing queries has become much easier than before, making me realize that the language and platform for data mining are just the surface. What’s important is the subject of research behind it, which is closely related to the PM’s responsibilities. At least for now, accidentally becoming a Data PM seems pretty good to me.

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