Without knowing too much, it sounds like you're supposed to make sure a hardware device gets set up so a user can use it. I could think a simple metric could be % of successful config completions. This would help you look across the funnel to see where they're falling out compared to the % of success.
If config is really the purpose, then it sounds like you don't really care about engagement or return metrics, just making sure they can use their purchase w/o friction. Return usage, in this case, could actually be a negative metric, like maybe the device is failing.
I started as a PM a few years ago and struggled with metrics getting started (still do sometimes). A book that I really like, which is a little older but still great, is Lean Analytics. It's marketed for startups, but it is a great primer for anyone in product or marketing looking to improve their use of analytics to achieve better outcomes. One thing they espouse in the book is that ratios are better for metrics since it helps ground it in comparables and take away some of the cruft from vanity metrics. Also, less is more. One great metric is always better than a few good metrics IMO.
Good luck and happy holidays!
These are all interesting metrics.
But it really depends on the type of your business and the goals you have with your website/app.
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I highly recommend the book Lean Analytics that is all about finding metrics that matter most for your business and then focussing on them.
Focussing on metrics is a key part of a growth PMs role and I would absolutely recommend reading Lean Analytics https://www.amazon.com/Lean-Analytics-Better-Startup-Faster/dp/1449335675
I'd highly recommend reading the book Lean Analytics. It answers questions like yours at fundamental level for different business types. Here's the link - https://www.amazon.com/Lean-Analytics-Better-Startup-Faster/dp/1449335675
I highly recommend reading the book Lean Analytics https://www.amazon.com/Lean-Analytics-Better-Startup-Faster/dp/1449335675
This is a great video for some inspiration and frameworks https://www.youtube.com/watch?v=MABmQhOlmJA
https://www.productmanagementexercises.com/ has a great collection of questions and answers, though you might have to pay to access the entire question repository.
https://igotanoffer.com/blogs/product-manager/estimation-interview-questions might be helpful.
https://www.youtube.com/channel/UCjm_qVkCPjOVDz9BWjNqO9A (Exponent) and https://www.youtube.com/user/rocketblocks (RocketBlocks) have some good content on their youtube channel.
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Hope this is useful :)
TLRD: I wanted to post this as a text post but I don't have enough karma. Posting here for now. If people find this useful, I'd love to move the disc to a self-post for other people to find this information more easily. I'm only posting part of it due to character limit
Summary: People in my personal life have asked for insight on breaking into the data science field/the interview loop. The following is a poorly formatted/continually updated list of my thoughts that I continually send out to people who've asked for them. I've decided to share it with the wider community. Apologizes for the poor formatting, I originally wrote this in email and I did not have the time to get the markup pretty.
Audience: People who are trying to break into data science and need help with the interview/job search. Early-mid career people might find some nuggets useful.
About me: Did my PhD doing experimental stuff with semiconductors. I'm comfortable with math and reading research papers, I'm a shit programmer. After grad school, I spent 2 years working for a no-name ML startup doing basic ML (mostly cleaning data, pipelines, feature engr experiments). I'm now a DS at FAANG-MULA for about a year. Opinions are my own, please feel free to disagree in the comments.
===================== CONTENT =====================
If you can code, consider looking into positions as a software engr. They make more money and there are about 10x more jobs than data scientists. The interviews at the lower levels are basically optimizing code that you can cram for via leetcode.com.
(a) Metric XX is going down. How would you investigate it? I always think about these problems from MECE + funnel analysis perspective as noted above.
(b) After expt AA, metric XX is going up but metric YY is going down. How would you think about it? This is a common problem where you're trying to understand tradeoffs/ambiguity and communication with managers/top line goals. If you EVER find yourself saying something definitive to this kind of problem, you're doing something wrong. Look up Pareto Frontier, but don't force it in.
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Since I see good suggestions for courses, some other things that data analysts use a lot
SQL - for data pulling and manipulation - you can learn it here interactively - https://mode.com/sql-tutorial/
Analytics concepts - Lean Analytics is a good summary of the methods needed to apply data to business problems - https://www.amazon.ca/Lean-Analytics-Better-Startup-Faster/dp/1449335675
For a overview of statistical concepts from a non-technical perspective, the recently released "Art of Statistics" is good to read even if you do more technical courses to help you with the bigger picture - https://www.amazon.ca/Art-Statistics-How-Learn-Data/dp/1541618513/ref=sr_1_1?dchild=1&keywords=art+of+statistics&qid=1606660024&s=books&sr=1-1