Execution/Analytical Product Interviews: Common Mistakes that’ll get you rejected
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- Опубліковано 3 жов 2024
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Ace your execution/metrics/analytics product manager or data science interviews by avoiding the most common mistakes leading to weak answers.
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Great explanation on why you don't focus only on specific metrics like engagement or acquisition
Such great videos! Great advice, I love this channel.
Thanks for the support!! Glad it’s been helpful
Awesome videos! Very much appreciated.
awesome video. thanks!
🤩Comment below: what mistakes have YOU made in previous product interviews?
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great video, just a suggestion but recommend to remove the background guitar noise because its tricky to focus on the content with it.
Thank you so much!
I am an over thinker and a half. I stumbled across your video with a Facebook interview set for next week and I assure you ..I would have mentioned engagement in the most linear way. I appreciate content that stresses the wrong approach more than the ones emphasizing the "correct".
So glad we got this in front of you then before your upcoming interviews! 😂
So what would the two sides of the user ecosystem be for a product like Facebook? Would it be people who post and people who react/consume posts?
That would be the two sides if we’re strictly talking about newsfeed,
Awesome advice. Would love to see a “fail” video for “leadership and drive “
Great suggestion!
This is my new favorite PM channel! Two questions: (1) I recently got feedback about my interview, telling me I didn't define the product mission clearly. Can you please advise what is considered a mission, and what's a good way to define it? (I suppose we shouldn't just refer to the company's general mission).
(2) I find it pretty challenging to go through everything I want within 30 minutes.
For example, sometimes just thinking about enough metrics might take too much precious time. How do you know when you have "enough" metrics to move on to the next stage in the structure?
2) usually 5-8 metrics suffice, what’s important is less about quantity but explaining why each of your metrics matter
1) product mission could refer to a vision for the product or some statement of what we’re trying to achieve. For example, if I was building something for grocery shopping, a mission could be “we want to help people seamlessly shop for health groceries” it’s a bit general but I take a stand on “seamless” and “healthy” which could be my focus points when building a product
@ dianna yau Your channel is really helpful and I hope to see more useful videos like this. I want to see how you tackle execution question for a product that does not exist today - for example, As a PM, if you have to define the success metrics for product for LGBTQ community, how would you got about it? Naturallly, if i get this question, i would talk about this like a design question first on users, pain points and solutions then move to defining success. But from a time perspective, it is a lot of time and I do not know what is the best way to balance time and the right approach.
Great question! Two options here, if it’s strictly an execution question 1) you’ll have to ask the interviewer to clarify the product they have in mind OR assume some key features and ask the interviewer to validate. 2) if they expect you to do product sense AND metrics then you’ll want to spend the majority of the time on the product features and leave 5 mins to define metrics
@Dianna Yau, Could you elaborate more on the mistake about choosing engagement? I often struggle with this section.
yes I struggle with this section too. @Dianna could you please elaborate more on why not to chose engagement?
Thank you. You made me realize so many mistakes I had made. If I had viewed this before...
How can we know what type of metric frequency (by day/month/year) and what type (numbers/%/average) we should use? And the reasoning behind it?
How often is the product typically used by users? That’ll determine frequency.
Your metrics should be an assortment of total, avgs and %. I usually default to totals because we care that the whole number and ecosystem is going up vs. zeroing in on the average. % is lesser used except for analyses like drop off rates. Why % aren’t great for top line metrics is because a % can go up or down due to the denominator and numerator ratio, so showing it as a % can hide the fact that you might be gaming the metric by altering the denominator
This was fantastic
Dianna, how do you justify choosing Adoption v/s Engagement or vice versa for a specific product? What would your rationale be?
You don’t, adoption and engagement is both important for a product to be successful
Thanks, Dianna! Have a question. For FB dating or any dating product what are the two sides of the marketplace? Could it be people who are reaching out to a match and people who are responding (match itself). But then the match could also be reaching out to other people so it wont be a true MECE. Would love to get your thoughts.
for dating there’s 2 sides for a match to happen (either male/female, male,male, female/female etc) but essentially both have the same goals and user flow
Could you elaborate on how you would specify a "meaning conversation"?
Something like 3 back and forth messages