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There's no way to answer this in a comment...you can take a full course on tech product management and still not have all the answers. But I'll at least offer s
by capkutay 6y ago
There's no way to answer this in a comment...you can take a full course on tech product management and still not have all the answers. But I'll at least offer some advice:
Just be very good at measuring and presenting data from all the channels in your company. Your customers, your prospective customers, your customer engineers, your sales people, your support team, your engineering managers, directly from developers too.
Understand the real cost of each feature. That's not easy. Try to predict the real value of each feature. That can be even harder. How many customers are asking for that feature? What's the avg LTV of that cohort? Will the feature reduce churn? By what percentage? Own those metrics and tell the whole company that's why you're prioritizing those specific features.
Be prepared to go back and check your assumptions and see if your predictions were correct...If you were wrong, then your next job is figuring out why and then working towards improving your own models to predict the value/cost of each feature. The fact is once you start intensely measuring that type of data, you'll find lots of gaps in your own process and fixing it as you go.
- coder1001 6y agoAny good courses you can recommend on tech product management? Seems like very relevant here.
- jayp 6y agoThis is a great comment. Love it. On my end, I’ve recently started working on documenting (still very, very early) on the “measuring” part at https://datadriventeam.org https://datadriventeam.org. It is backed by an open source GitHub repo and I hope people would contribute once I’ve gotten the initial version out.
- raywu 6y agoI just checked out Data Driven Team and Herald. What do you think of this comment[0]: > Good PMs know the importance of talking to customers frequently. Good PMs listen intently to customers to develop their product hypothesis. Great PMs also listen to what isn’t said and anticipate where the industry overall is headed when developing their product hypothesis. How do you think about balancing data (customer) driven approach and product vision? This is an age-old question. I sided with the former and am learning that there's also wisdom in the latter. This might be a better way to put it: there's time and place for each approach, in a product's life cycle. [0] https://twitter.com/shreyas/status/1249039646094311424 https://twitter.com/shreyas/status/1249039646094311424
- jayp 6y agoYup, totally agree. I will update the guide with this quote/source and expand on this line of thinking on my next edits. As you can see it's very early days for the guide. Thanks Ray!