7 Comments

Congrats on the new domain!

I enjoyed this a lot. Useful for me, even when I'm not in the PM space. Previously only thought there were 2, maybe 3 types of PMs. This helps debunk the myth.

Will share with some of my new grad mentees who are looking at PM.

May not be within your scope, but you mention that Business PMs may have an ops background. Curious to learn how (and also why) ops folks would make a pivot to PM. Especially helpful as I'm started to think a few years ahead!

Thanks Will.

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Appreciate the considerate response (as usual). If someone in ops is looking to pivot to PM, aim to find roles that are close to the "Business driven-PM" archetype. This way you can leverage your skills while developing new ones. I have teams and companies that may have these roles in the article. Hope that helps!

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Loving the new colour & logo design, super hip! I appreciate the full breakdown of PM archetypes. It gives me a new understanding of how PMs play impactful role in different types of company. My favourite part of this entire article is the 10-30-50 PM, it’s very useful metric as well as reframe oneself from thinking that s/he might be spreading too thin by dabbling in different skill sets!

Wishing you a productive rest of your week, Will!

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Yes, it's a really awesome idea! Here's the full Twitter thread. Shreyas has some really great ideas, you should follow him if you aren't already: https://twitter.com/shreyas/status/1055718652102594560

And glad the purple is hip 😂

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The article was very insightful. The detailed differentiation in the various archetypes is thought-provoking and will help me shape my PM-Brand. Working on my radar chart and plan to track it overtime - thanks Will!

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Hello Will! Great article, thanks for the insight. One question however: if a PM is tech ou design-driven (while developers and designers already exist within the squad), then who will get the business or data role?

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William, as I was expressing myself on the LinkedIn post too.

Data-driven decision approach is not A correct approach.

Data Informed Decisions is a correct approach as majority of while working with Data Scientists and Analysts ; you as PM might get lost into the one or few key variables which they felt is important but as PM goal is to understand the user problem, understand the knowledge that you gained based on data analysis and prediction And then apply your wisdom to overall use case to determine whether to implement or not.

What are long term vs short term benefits or losses?

Am I getting the full information from the given data set?

Etc...

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