Hello Folks! Welcome back to our weekly AMA series. This week we got an opportunity to interact with Deepak Singh, Senior Director of Products at Unacademy.
Deepak has built 0 to 1 as well as products at scale (>50 million DAU). He has even built games, videos, gamification, edtech, classifieds, & data science products. Deepak shares interest in growth which is depicted by the fact that he runs a blog called Growth Catalyst. He led Voice Search and Assistant AI products for Flipkart and has also written a book for non-techies in PM roles.
In this AMA, Abhishek shared insights about key Product Management skills for entry level PMs. He also shared about some important PM tools and essentials for finding the right culture fit.
So let's jump straight into it!
The knowledge of tech is often debated for PMs. The reason is that we have seen great PMs who don't have a tech background.
But all of these PMs actually learned tech on the go. Not coding - that is different. But understanding the logic in tech - like why would something take more time. Or what APIs, BE, FE, SDKs, etc mean.
So to be a great PM, you need to understand the basics of tech but don't need to code. Over time, you are expected to understand technical system design and devops to be a good product leader.
To enhance knowledge, here is what Deepak recommends to you-
To improve in data science, don't look at learning a language. Do the course on Machine Learning by Andrew NG. It's possibly the most popular course on Coursera and the best one till date. While doing the course, focus on theory and why certain algorithms work and others don't. No need to do exercises except conceptual ones.
At an APM/PM level, you are expected to learn flawless execution, basically get things done. At an SPM level, you are also expected to hold conversations with different teams like marketing, business, etc., and find the best solution. This also means that you can start taking initiatives and thinking about strategy a little bit.
My biggest learning while writing the book was that if we build products the right way without giving in to shortcuts or short term rewards, we would be able to build something meaningful. “Having that long term view in mind makes a lot of difference between a good and a great outcome.”
Here are the things to remember in your first 90 days and more
Define what an ideal usage of the product looks like, and that would tell you the right metrics. Some of the ones that are definitely useful:
It's a long and hard path to figure out what will move these metrics. I have succeeded in some places and I have failed in others. I can suggest you start at activation (aha-moment) because that has a cascading effect on retention. If you need to learn how to improve activation, I wrote a series of posts on onboarding and activation at growth catalyst. You can also refer to Reforge articles around these.
A product analyst is supposed to figure out what is wrong (funnel, cohort, segment, etc) and highlight that to the team. Every once in a while, they will also do an analysis to understand why something is wrong, like why conversion is low, or revenue has dipped.
A PM is supposed to figure out what is wrong, why it's wrong. (through data and user interviews), and how to fix it. This is beyond their usual role to move metrics by building new things :)
Usually, all companies need data inclined PMs. The rigour is higher in industries that generate a lot of data and engagement. Examples are Facebook, gaming apps, video and music apps, etc. That is the reason you see FB or Google taking an analytical round in their PM interviews.
Another thing is how far are you from consumers and how diverse is your consumer base. For example, enterprise software doesn't need very data-driven PMs because they keep talking to their consumers, and also address a niche segment most of the time. So to sum it up, scale, diversity, and closeness to consumers determine data intensiveness.
Always take 15-30 mins to be prepared for pointers of discussion. This is most important. Updates, open questions, and seeking feedback are three things you can start with.
My favourite blogs/newsletters to read are-
ChatGPT is one of the hottest topics of discussion currently, and rightly so. The OpenAI-developed Generative AI program sits on top of the GPT 3.5 and GPT 4 LLM models to provide a human-like interaction experience with all the smartness of machines.
It is a busy Tuesday afternoon, and you have three back-to-back meetings (2 of them could've been emails, but what can you do, right?). You return to your desk 2 hours later to find each slack channel filled with 150+ messages. What's your first reaction?
Hello hello! Howdy? Where did January go? Well, tell us if you get to know :) As we step into February, we decided to focus on an aspect of life arguably as indispensable as food or water. No, not exercise. Apps. Do you think we're exaggerating? Maybe... but technology products have become so intertwined with life.